Search is not available for this dataset
text
stringlengths 1
134k
| id
stringlengths 11
16
| article_id
stringlengths 8
10
| section_title
stringlengths 1
1.35k
| educational_score
float64 0.52
5.16
| domain
stringclasses 3
values | document_type
stringclasses 4
values | domain_scores
listlengths 3
3
| document_type_scores
listlengths 4
4
| language
stringclasses 38
values | language_score
float64 0
1
|
|---|---|---|---|---|---|---|---|---|---|---|
EP, in particular, is propelled by an immune response dominated by T-cells, particularly Th1 and Th17 subsets, along with dendritic cells that produce potent pro-inflammatory cytokines such as IL-17, IL-22, TNF-α, and IFN-γ . Chronic stress further amplifies T-cell activation, significantly intensifying the immune response. This excessive cytokine production may result in a “cytokine storm,” a widespread and severe inflammatory reaction that manifests as extensive skin redness, scaling, and edema .
|
PMC11698379_p14
|
PMC11698379
|
Discussion
| 4.18991 |
biomedical
|
Study
|
[
0.9994953870773315,
0.00034534139558672905,
0.00015933610848151147
] |
[
0.9395788908004761,
0.039633363485336304,
0.020032424479722977,
0.0007553598843514919
] |
en
| 0.999993 |
Additionally, EP compromises the skin’s barrier function, heightening the risk of infection and fluid loss. In cases triggered by stress, dysregulated cortisol levels may further undermine skin barrier integrity. Together with the high systemic inflammation, these factors render the skin highly susceptible to infections, while stress-related immune suppression increases the body’s vulnerability to opportunistic infections .
|
PMC11698379_p15
|
PMC11698379
|
Discussion
| 3.956589 |
biomedical
|
Study
|
[
0.999259889125824,
0.0004180085670668632,
0.0003220778307877481
] |
[
0.6122983694076538,
0.21678712964057922,
0.16948996484279633,
0.0014245196944102645
] |
en
| 0.999998 |
Nonpharmacological treatments, such as effective stress management techniques, mindfulness meditation, cognitive-behavioral therapy, relaxation techniques, and physical exercise, can help patients reduce their physiological stress response. In five out of six randomized control trials, participants demonstrated improvements in self-administered psoriasis area and severity index scores following eight or 12 weeks of meditation and/or mindfulness interventions. Additionally, two studies indicated psychological benefits for psoriasis patients after engaging in these practices. Collectively, these findings imply that meditation may serve as an effective method for enhancing both psoriasis severity and the quality of life for patients .
|
PMC11698379_p16
|
PMC11698379
|
Discussion
| 3.904744 |
biomedical
|
Review
|
[
0.998491644859314,
0.0007491851574741304,
0.0007592173060402274
] |
[
0.35699164867401123,
0.0017157798865810037,
0.640872061252594,
0.000420457887230441
] |
en
| 0.999995 |
Pharmacological treatment usually includes oral hydration, topical steroids, vitamin D analogs, and a trial of biological therapy. In this instance, due to the severity of the condition and the risk of further systemic complications, the decision was made to initiate treatment with adalimumab, a TNF-alpha inhibitor. Adalimumab was chosen because of its potent anti-inflammatory properties, which specifically target the overactive immune response seen in psoriasis. It effectively neutralizes TNF activity by impeding its interaction with TNF receptors on the cell surface. This inhibition curtails the migration of leukocytes, subsequently reducing the proliferation and differentiation of keratinocytes .
|
PMC11698379_p17
|
PMC11698379
|
Discussion
| 4.088549 |
biomedical
|
Other
|
[
0.9219450354576111,
0.07706757634878159,
0.0009873387170955539
] |
[
0.11823933571577072,
0.838143527507782,
0.007627737708389759,
0.035989388823509216
] |
en
| 0.999998 |
In combination with adalimumab, the patient was also prescribed methotrexate, a well-known immunosuppressive medication that inhibits the rapid turnover of skin cells and reduces immune system activity. Methotrexate is often used alongside biologics to enhance treatment efficacy and to provide more comprehensive immune suppression.
|
PMC11698379_p18
|
PMC11698379
|
Discussion
| 2.459931 |
biomedical
|
Other
|
[
0.7731882333755493,
0.2226279079914093,
0.004183813463896513
] |
[
0.01845940575003624,
0.748936116695404,
0.0031537895556539297,
0.22945065796375275
] |
en
| 0.999998 |
This case report represents a rare presentation of EP associated with psychological stress. When patients present critically ill with an erythrodermic rash, it is important to consider EP in the differential diagnosis, especially with recent psychological stress. The mechanisms through which psychological stress interferes with psoriasis onset or exacerbations are not completely understood. However, psychoneuroimmunology studies have shown that acute and chronic stress can affect immune function, leading to a worsening of psoriasis by inducing keratinocyte proliferation and incomplete maturation.
|
PMC11698379_p19
|
PMC11698379
|
Conclusions
| 4.045541 |
biomedical
|
Clinical case
|
[
0.6716824173927307,
0.32607313990592957,
0.0022444322239607573
] |
[
0.029014937579631805,
0.039732132107019424,
0.006153050810098648,
0.9250999093055725
] |
en
| 0.999998 |
Visual methods represent a novel approach in qualitative evidence synthesis (QES) by introducing another dimension to the synthesis process and contributing to the understanding and generation of knowledge . The terminology “qualitative data or evidence” broadly refers to findings from primary qualitative studies (e.g., analysis of data from interviews, focus groups, and the production of new theories or theoretical insights), or qualitative data (such as narrative responses to open ended questions). Numerous methods can be used for data synthesis in a QES including meta‐ethnography , thematic synthesis , and framework synthesis . Irrespective of the method used for analysis and synthesis, additional visual methods can play a crucial role in aiding review authors and readers to comprehend, organize and display qualitative data collected from included studies .
|
PMC11698405_p0
|
PMC11698405
|
BACKGROUND
| 3.87933 |
biomedical
|
Other
|
[
0.8124322891235352,
0.0013480803463608027,
0.18621966242790222
] |
[
0.2392115741968155,
0.385601282119751,
0.3745119273662567,
0.0006751841283403337
] |
en
| 0.999997 |
There is a tendency for review authors to underutilize the diverse array of visual display methods, tools, and techniques available to enhance a chosen synthesis method . Some review authors may assume that their selected synthesis method includes all necessary supplementary methods, tools, and processes, while others may lack the skills or confidence to generate alternative formats for their synthesis. This paper provides an overview of accessible visual methods supporting the synthesis stages of a QES, with examples and their application in the development of a Cochrane QES using meta‐ethnography. The paper also addresses the role of stakeholders, considerations of equity, diversity, and inclusion, and reflexivity in selecting and employing additional visual methods. Although the focus is on QES, the visual methods described here can also be used to facilitate communication of complex and sensitive topics in primary qualitative research during data collection and analysis .
|
PMC11698405_p1
|
PMC11698405
|
BACKGROUND
| 4.019563 |
biomedical
|
Study
|
[
0.9805529713630676,
0.0010498804040253162,
0.01839711144566536
] |
[
0.7465792894363403,
0.004200077150017023,
0.24884556233882904,
0.00037506886292248964
] |
en
| 0.999995 |
Table 1 presents an overview of visual methods according to their role in the synthesis process (i.e., support and develop synthesis, facilitate stakeholder engagement, and record the synthesis process). Visual methods can be used in different ways at various timepoints to support the synthesis of qualitative evidence, such as data display and management, initial exploration of data, and synthesis. Visual methods can help with the development of new insights from data allowing a deeper understanding and construction of new knowledge .
|
PMC11698405_p2
|
PMC11698405
|
Overview of visual methods and their uses
| 2.415939 |
other
|
Other
|
[
0.4755048155784607,
0.0020047277212142944,
0.5224904417991638
] |
[
0.04160965234041214,
0.9358651041984558,
0.022038757801055908,
0.00048651715042069554
] |
en
| 0.999997 |
Visual methods should be selected based on whether they can add value to facilitating the synthesis or be used as an integral part of the analytical process (e.g., use of diagrams to develop and visualize the synthesis). All of the visual methods listed in Table 1 are flexible and adaptable to different tasks and the creativity of the review team.
|
PMC11698405_p3
|
PMC11698405
|
Considerations when selecting a visual method to support the synthesis
| 1.685076 |
biomedical
|
Other
|
[
0.5879424810409546,
0.004870034754276276,
0.4071875512599945
] |
[
0.018001846969127655,
0.9346525073051453,
0.0462627075612545,
0.00108303502202034
] |
en
| 0.999997 |
When selecting visual methods, it is important to first consider the content and objectives of the synthesis. The choice of method should align with the analytical goals, such as enhancing understanding or encouraging collaboration within the research team. For example, some visual methods may be more effective for developing early‐stage ideas, while others might help to present findings more clearly.
|
PMC11698405_p4
|
PMC11698405
|
Considerations when selecting a visual method to support the synthesis
| 1.964033 |
other
|
Other
|
[
0.4801631569862366,
0.0031102935317903757,
0.516726553440094
] |
[
0.031091583892703056,
0.9482309818267822,
0.019987015053629875,
0.0006904088077135384
] |
en
| 0.999997 |
Delivery considerations, particularly when working remotely, also play a key role. Resources such as internet connectivity, relevant hardware (e.g., computers), and software (e.g., Microsoft Teams) are essential if implementing visual methods online. Remote working also demands technical knowledge from the research team. In addition, visual methods involving a group of researchers (whether in person or remotely) can involve high costs, preparation, and may require a facilitator. Practical considerations, such as preparing materials (e.g., paper labels) and developing a plan for how the method will be used, are crucial for group meetings. If using arts or performance‐based methods, the presence of artists or actors may be required.
|
PMC11698405_p5
|
PMC11698405
|
Considerations when selecting a visual method to support the synthesis
| 1.704618 |
other
|
Other
|
[
0.10838785022497177,
0.0013718780828639865,
0.8902403116226196
] |
[
0.013456898741424084,
0.9853101968765259,
0.0009476456907577813,
0.00028521064086817205
] |
en
| 0.999997 |
To illustrate the use of visual methods to support a synthesis, a worked example of a recent QES using meta‐ethnography is used . Meta‐ethnography is one of the most complex QES methods designed to synthesize mainly rich data from primary qualitative studies in a series of steps to develop new theoretical insights and theory . The review authors investigated how children and young people with chronic noncancer pain and their families experience and understand their condition, pain services and treatments . The whole team was involved in conducting the analytic synthesis with two members leading on and carrying out the majority ofthe analytic synthesis. They produced three lines of argument, a model and a theory of chronic pain management. The combination of their lines of argument was named “The journey of living with chronic pain” which expressed the experiences of children and young people with chronic pain and their families from the onset of chronic pain; their struggle to navigate health services seeking a cure, and to have their needs and expectations met; and the outcome, moving on either to prioritize living well with pain or give up hope .
|
PMC11698405_p6
|
PMC11698405
|
How to apply visual methods in qualitative evidence synthesis
| 4.094589 |
biomedical
|
Review
|
[
0.9922646284103394,
0.002098764991387725,
0.005636563058942556
] |
[
0.03925556689500809,
0.001978646032512188,
0.9583898186683655,
0.00037587620317935944
] |
en
| 0.999998 |
The review involved a diverse stakeholder group of health professionals, third sector organizations, policy makers, academics, and the public as well as a patient and public involvement (PPI) group of children and young people with chronic pain aged 8–20 years old and parents from across the United Kingdom. The PPI and stakeholder groups were involved throughout the entire review including making decisions about which studies to include in the synthesis and how to group studies in order to analyze and synthesize them, and the analysis and interpretation of findings from primary studies and of preliminary synthesis findings.
|
PMC11698405_p7
|
PMC11698405
|
Stakeholder involvement and engagement
| 2.406574 |
biomedical
|
Review
|
[
0.8839608430862427,
0.014139981009066105,
0.10189918428659439
] |
[
0.007895148359239101,
0.05668026953935623,
0.9341613054275513,
0.001263233833014965
] |
en
| 0.999996 |
France used a variety of visual data display methods at different stages of their meta‐ethnography to help convey the complex evidence and synthesis, especially to PPIs. The review authors worked mainly remotely as a dispersed team with few opportunities to meet face‐to‐face due to the COVID‐19 pandemic, hence they developed and delivered most of their visual methods virtually. In the absence of guidance on the selection of visual methods, the review authors drew on high‐quality relevant QES reports that used visual display methods successfully . Once the review team gained confidence and found that these visual methods were highly valuable, they selected visual methods to further enhance the synthesis process .
|
PMC11698405_p8
|
PMC11698405
|
Visual methods to display data
| 2.956052 |
biomedical
|
Review
|
[
0.9706446528434753,
0.002890746109187603,
0.02646457776427269
] |
[
0.05564607307314873,
0.04952099919319153,
0.893731415271759,
0.0011015074560418725
] |
en
| 0.999998 |
In the following sections, we discuss and evaluate the contribution of each method used in the meta‐ethnography.
|
PMC11698405_p9
|
PMC11698405
|
Visual methods to display data
| 1.723641 |
biomedical
|
Study
|
[
0.7078250050544739,
0.0022932817228138447,
0.28988170623779297
] |
[
0.6602516174316406,
0.23588737845420837,
0.10194671154022217,
0.001914268359541893
] |
en
| 0.999997 |
This method was used among the review team only between phases 5 and 6 as part of the analytic synthesis process (to translate the studies into one another and in synthesizing translations). The review authors used paper labels to initiate the synthesis process and start developing novel insights . In a previous step, at least two review authors interpreted the meaning of every relevant finding, concept, or theme from the studies using NVivo version 12. The authors then compared the meanings within and across studies to identify common or unique concepts. Where possible, the common concepts were then matched, merged, and further interpreted by two review authors and discussed with the wider team of review authors to develop new interpretations. The common concepts and new interpretations were summarized on paper labels. Unlike NVivo 12 , paper labels provided the necessary visual and textual components needed to allow a larger number of researchers to work together during in‐person analysis. For instance, labels included a title and a short summary explaining the specific findings and contributing studies. All labels were color‐coded according to health condition and whose interpretation was presented (i.e., that of the primary study author or the review team). Paper labels were also numbered to match the structure of an accompanying detailed Word document which gave the full details of the primary study data underpinning the short summary of the findings. This strategy of visually displaying all findings helped the review authors to iteratively test different ways of thematically grouping the findings. It also helped to conduct a thematic analysis with the creation of new themes signposted using Post‐it notes. Photos were taken to record different versions (e.g., version 1, version 2, etc.) so that the review authors could follow the development of their analysis and subsequent synthesis.
|
PMC11698405_p10
|
PMC11698405
|
Paper labels
| 4.122055 |
biomedical
|
Study
|
[
0.9960175156593323,
0.0009017145493999124,
0.0030808313749730587
] |
[
0.9904941916465759,
0.004607481881976128,
0.004780380055308342,
0.00011792127043008804
] |
en
| 0.999996 |
The use of paper labels supported the development of new themes and an initial draft of thematic groupings. Labels helped the review authors to efficiently analyze a large volume of rich data and findings as a team. Paper labels enabled teamwork in the identification of overarching concepts and creation of new understandings or concepts during a synthesis meeting.
|
PMC11698405_p11
|
PMC11698405
|
Paper labels
| 1.325011 |
other
|
Other
|
[
0.3412507474422455,
0.006250699050724506,
0.6524985432624817
] |
[
0.010627508163452148,
0.9480355381965637,
0.03978244960308075,
0.0015545214992016554
] |
en
| 0.999997 |
When working remotely, the review authors adapted the method by recreating all paper labels virtually using Padlet , a real‐time collaborative web platform. Padlet virtual labels were color‐coded according to whose interpretation was presented (i.e., whether it was the interpretation of the primary study author or of the review team) and included a title and description of the construct. Both physical and virtual labels were used together during the team meeting. The idea was that members joining remotely via Microsoft Teams could participate in the thematic synthesis in real time using the virtual labels. However, the review authors learned that constantly updating Padlet to match the thematic groupings in real time was challenging and time‐consuming. This process could have been more efficient with the involvement of a dedicated facilitator, who could have taken responsibility for regularly updating the Padlet . As a result, the review authors that were joining the meeting online were updated verbally regarding the changes in the configurations of labels and Padlet was used only as a visual aid. At the end of the meeting, photos showing the labels that were used to create “new constructs” or understandings were uploaded on Padlet to facilitate discussion with the whole team and to provide a record of the analysis (Appendix S1 ).
|
PMC11698405_p12
|
PMC11698405
|
Paper labels
| 3.406972 |
biomedical
|
Study
|
[
0.8127303123474121,
0.0020208924543112516,
0.18524882197380066
] |
[
0.8795161843299866,
0.09532567113637924,
0.0246915053576231,
0.00046669537550769746
] |
en
| 0.999997 |
A digital interactive whiteboard, Google Jamboard, was used during phase 6 of the meta‐ethnography to display data and develop analytic categories remotely . Google Jamboard is composed of different “frames,” similar to pages or slides. The authors used each frame to analyze a specific cluster of related themes, which were grouped together into a broader “analytic category,” for instance, as shown in the frame in Figure 4 . All findings were recreated as notes that were color‐coded according to the “analytic category” to which they belonged. All notes included a title, the contributing studies, the health condition, and whose interpretation was presented (i.e., that of the primary study authors or the review team). The “analytic categories,” themes, and their constituent findings and all notes were numbered to match the same structure as the textual synthesis (i.e., a Word document containing the full details of the primary study data underpinning the findings, themes, and analytic categories). This strategy allowed the authors to easily transfer any changes or new interpretations into the textual synthesis document. Google Jamboard also facilitated the tracking of how the themes were organized according to the different interpretations from the team and facilitated team discussions of the different interpretations.
|
PMC11698405_p13
|
PMC11698405
|
Interactive whiteboard—Google Jamboard
| 3.854807 |
biomedical
|
Study
|
[
0.9919845461845398,
0.0004335715784691274,
0.007582010701298714
] |
[
0.99062180519104,
0.008537772111594677,
0.0007477680337615311,
0.00009266407141694799
] |
en
| 0.999998 |
Using Google Jamboard to display the analytic categories, themes, and their constituent findings resulted in the creation of five analytic categories that organized the whole textual synthesis. This visual method was crucial to allow interactive online analytic synthesis meetings using all the different perspectives and expertise from the whole research team .
|
PMC11698405_p14
|
PMC11698405
|
Interactive whiteboard—Google Jamboard
| 1.753615 |
biomedical
|
Other
|
[
0.5767369866371155,
0.0015500785084441304,
0.4217129051685333
] |
[
0.3107199966907501,
0.6860423684120178,
0.0023849247954785824,
0.0008527002646587789
] |
en
| 0.999997 |
This method was used during phase 6 of the meta‐ethnography to visualize and further develop the synthesis. Microsoft Whiteboard is a multiplatform application which simulates a virtual whiteboard and enables real‐time collaboration. The review authors used Microsoft Whiteboard to express and understand how findings were connected to one another to create a coherent “storyline” [line of argument] (see Section 1.3 ). Initially, the authors included all themes under their respective category as text boxes on Whiteboard . All text boxes were color‐coded according to context (i.e., different colors were used to indicate starting points, potential links with other categories, and findings representing a positive impact). The authors used arrows to indicate which findings/themes were related, and the result was a large diagram linking all five categories (Appendix S2 ). Short descriptions for each analytic category were created based on the diagrams and these were discussed during an analysis meeting with the research team. At this point, the review authors focused on creating a better understanding of each analytic category. Subsequently, the diagram was further developed incorporating different interpretations and perceptions from the multidisciplinary team, resulting in major modifications to allow a more in‐depth exploration of these data (Appendix S3 ).
|
PMC11698405_p15
|
PMC11698405
|
Interactive whiteboard – Microsoft Whiteboard
| 4.010222 |
biomedical
|
Study
|
[
0.983647882938385,
0.0005603579338639975,
0.01579170674085617
] |
[
0.9946398138999939,
0.0017928554443642497,
0.003488844260573387,
0.0000785782394814305
] |
en
| 0.999996 |
At this stage, the visual representation of all analytic categories in the form of diagrams allowed the team to develop their understanding of and start developing the initial “overarching storylines” or lines of argument. The initial interpretations and hypotheses were inserted in the diagram as virtual notes. The final step was the creation of a further simplified version of the diagram that displayed how all four final analytic categories and findings were connected. The researchers used this last version of the diagram to further develop the description of the diagram to include how all categories and themes/findings were related which was used to create the textual synthesis.
|
PMC11698405_p16
|
PMC11698405
|
Interactive whiteboard – Microsoft Whiteboard
| 3.059001 |
biomedical
|
Study
|
[
0.7668159604072571,
0.0014751156559213996,
0.23170892894268036
] |
[
0.7984345555305481,
0.1997729390859604,
0.0014557777903974056,
0.0003367596073076129
] |
en
| 0.999999 |
Microsoft Whiteboard was used to develop the overarching storylines which culminated in the development of three lines of argument. This process also resulted in the development of four analytic categories and the initial textual synthesis. While the Whiteboard allowed real‐time collaboration and facilitated teamwork, it only worked well with the core research team of two people as it was hard for the wider team to keep track of or readily interpret the large and complex diagrams.
|
PMC11698405_p17
|
PMC11698405
|
Interactive whiteboard – Microsoft Whiteboard
| 1.275679 |
other
|
Other
|
[
0.05537699535489082,
0.0011948766186833382,
0.9434282183647156
] |
[
0.01738380640745163,
0.9809190630912781,
0.0011384933022782207,
0.0005586158367805183
] |
en
| 0.999997 |
Cartoons and an infographic were used during phase 6 (synthesizing translations) of the meta‐ethnography to engage stakeholders and further develop and clarify the synthesis findings. PPI was fundamental to help clarify ambiguous or unclear findings. The review authors delivered virtual workshops with parents and young people to discuss, clarify, and interpret preliminary findings of the synthesis. Storyboard was used to create cartoons to convey ambiguous and unclear findings to prompt discussion among the PPI members and a scenario was created for each cartoon to facilitate this process.
|
PMC11698405_p18
|
PMC11698405
|
Cartoons and infographic
| 2.266819 |
biomedical
|
Study
|
[
0.8065202236175537,
0.002036454388871789,
0.1914432942867279
] |
[
0.6251744627952576,
0.3456924855709076,
0.02781500108540058,
0.0013180735986679792
] |
en
| 0.999997 |
The cartoons were representative, including people of different ethnicities and genders and the language used was accessible and engaging for children around 8–9 years of age. Patient and public members received the cartoons along with an infographic explaining the preliminary findings a week prior to the workshop. The use of cartoons and an infographic to engage PPI members in facilitated discussions on what some concepts and findings would mean to parents and young people, providing context and adding nuances based on lived experience to some of the findings. Subsequently, new data and insights were incorporated into the analytic synthesis and were used to further refine and develop the interpretation of findings.
|
PMC11698405_p19
|
PMC11698405
|
Cartoons and infographic
| 1.966827 |
biomedical
|
Other
|
[
0.8567543029785156,
0.009224526584148407,
0.1340211182832718
] |
[
0.4455259442329407,
0.5485399961471558,
0.0043645380064845085,
0.0015695967013016343
] |
en
| 0.999997 |
Diagrams were used by the review authors during phases 6 and 7 to further understand and express how the three lines of argument they developed were related. They used the final diagram they had produced using Microsoft Whiteboard , data from the PPI workshop, and the textual synthesis, to create an initial version of a visual model to refine and represent the findings of the synthesis connecting all lines of argument in Microsoft Word . The initial synthesis model was developed following feedback from the whole research team and depicted the nonlinear nature of the phenomenon of interest (i.e., families' journeys living with chronic pain and how they are affected by services). Subsequently, the researchers used Drawio to draw and refine the model with the inclusion of more context and nuance. This process of further refining the model consisted of rich interpretative discussions among the core members of the research team until an intuitive final version was constructed . The synthesis model expressed the concept of a journey families are navigating while they deal with chronic pain and access services. To express the concept of the journey and time, the researchers used rounded arrows to create an illusion of a cycle and described (text in red) where families might stay “stuck.” Two text boxes between both pathways indicated how families might navigate between these distinct pathways.
|
PMC11698405_p20
|
PMC11698405
|
Diagrams to express the synthesis findings
| 4.033415 |
biomedical
|
Study
|
[
0.9958100318908691,
0.0013692236971110106,
0.002820751164108515
] |
[
0.9918528199195862,
0.0013629993190988898,
0.006666319910436869,
0.00011796416220022365
] |
en
| 0.999998 |
The output was the final development and expression of the synthesis with a model produced initially in Microsoft Word and finalized in Drawio The model was fundamental to finalizing the synthesis, as it allowed remote teamwork and the incorporation of nuances and context provided during the PPI workshop. The model also enabled the expression of the overarching storyline connecting all lines of argument and the visualization of a complex nonlinear phenomenon.
|
PMC11698405_p21
|
PMC11698405
|
Diagrams to express the synthesis findings
| 1.451958 |
other
|
Other
|
[
0.2201196253299713,
0.002914569340646267,
0.7769657373428345
] |
[
0.013067117892205715,
0.9829965233802795,
0.003301353892311454,
0.0006349931354634464
] |
en
| 0.999994 |
Diagrams were used during phases 6 and 7—synthesizing the translations and expressing the synthesis. The review authors produced a theory explaining their phenomenon of interest (i.e., theory of good chronic pain management). This process included multiple analysis meetings with the core review authors and it also integrated insights from PPI lived experience and the key findings from the synthesis of the studies included in the review data. The authors used two of their main analytic categories (related to family life and their social relationships and their experiences navigating health services) to construct the initial structure for the theory in the center of the diagram on Microsoft Whiteboard . The review authors then placed all factors that had a positive impact on family life on the right side of the diagram, and factors with a negative impact on the left side. They used arrows to indicate when an aspect could be modified by the factors placed on each side of the diagram. The final version of the diagram mapped all factors that had the potential to “modulate” families' experiences with chronic pain (Appendix S4 ). Figure 9 shows the simplified version of the diagram.
|
PMC11698405_p22
|
PMC11698405
|
Diagrams to develop and express theory
| 4.055096 |
biomedical
|
Study
|
[
0.9984884262084961,
0.0007324158796109259,
0.0007791976677253842
] |
[
0.9536859393119812,
0.0017512644408270717,
0.04431943595409393,
0.00024337130889762193
] |
en
| 0.999997 |
The factors that positively impacted family life and their experiences with services were then developed into actions in a whole systems biopsychosocial theory. This was achieved with further interpretation of the key findings (i.e., key outcomes families consider as important) while drawing from expertise from the research team and PPI lived experiences. The Drawio software was used to continue developing the theory, as it enabled clear visualization of the processes and facilitated discussions with the core research team. The final product expressed the whole system approach underpinning the theory through different background colors indicating different environments within the system .
|
PMC11698405_p23
|
PMC11698405
|
Diagrams to develop and express theory
| 2.631497 |
biomedical
|
Study
|
[
0.8308537006378174,
0.00410240376368165,
0.165043905377388
] |
[
0.8432747721672058,
0.15453025698661804,
0.0016757696866989136,
0.0005192518001422286
] |
en
| 0.999998 |
The output was the conceptualization and expression of a theory produced initially with MS Whiteboard and finalized in Drawio . The diagrams were fundamental to developing the theory as they allowed remote teamwork and the mapping of all factors modulating the phenomenon of interest. The expression of the theory through the diagram also allowed a clear visualization of gaps in the data, and where the evidence was based on lived experiences or on the research team's hypothesis. The use of the diagrams enabled complex analysis and supported the convergence of evidence from different sources into a detailed theory. More examples illustrating the use of diagrams in QES are available in Table 1 .
|
PMC11698405_p24
|
PMC11698405
|
Diagrams to develop and express theory
| 2.389613 |
biomedical
|
Other
|
[
0.7208989262580872,
0.0009915503906086087,
0.27810946106910706
] |
[
0.2550137937068939,
0.7407012581825256,
0.003679161425679922,
0.0006058017606846988
] |
en
| 0.999999 |
We have shown that the use of additional visual methods in a QES facilitated better data visualization, remote analysis group meetings, interpretation of large amounts of data, and meaningful PPI during synthesis. Visual methods varied in complexity, costs, and required expertise, allowing flexibility to adapt to different contexts, whether virtual or face‐to‐face. For instance, certain methods such as paper labels worked better in face‐to‐face settings and facilitated group work involving multiple people. This method was essential to allow group work when dealing with large amounts of data. In contrast, the use of labels in virtual platforms such as Padlet was time‐consuming and demanded the presence of a facilitator and could only cope with moderate amounts of data.
|
PMC11698405_p25
|
PMC11698405
|
DISCUSSION
| 3.813989 |
biomedical
|
Study
|
[
0.936566174030304,
0.0006336721708066761,
0.06280006468296051
] |
[
0.9933711290359497,
0.005464017856866121,
0.0010839616879820824,
0.00008080248517217115
] |
en
| 0.999997 |
Virtual platforms for implementing virtual methods remotely also worked differently depending on the task. For example, whilst Google Jamboard facilitated collaboration with the wider team as it was more accessible and interactive compared to the textual synthesis, it did not allow the analysis of a large amount of data. Each frame could only cope with one main analytic category and required a facilitator to enable discussion. In contrast, Microsoft Whiteboard allowed the processing of a large amount of data but only the collaboration of a small team of two people. Irrespectively, both methods allowed interactive online analytic synthesis meetings in different phases of the QES and were crucial for the development of findings.
|
PMC11698405_p26
|
PMC11698405
|
DISCUSSION
| 1.465548 |
other
|
Other
|
[
0.07017384469509125,
0.0009504123590886593,
0.928875744342804
] |
[
0.0706898644566536,
0.9266932606697083,
0.0018111123936250806,
0.0008057264494709671
] |
en
| 0.999996 |
It is imperative to carefully consider equity, inclusion, and diversity in the development and application of visual methods to ensure their accessibility and relevance across diverse populations. In addition, when developing and tailoring all virtual methods or outputs, the review authors need to carefully consider their personal biases and professional perspectives and positioning concerning what they would choose to present visually and how they interpret it. As such, it is essential that visualizations are either informed by or created with those they represent so they are inclusive and relatable to their audience. For example, in France's meta‐ethnography the authors co‐developed cartoons with members of the public and were careful to ensure these were representative of different ethnicities, and genders and did not promote an idealized context. The review authors also carefully considered the scenario and context of each cartoon ensuring these were appropriate and inclusive (e.g., a plain doctor's surgery and non‐descript hospital settings). Visual methods should also use accessible and engaging language and include accessibility features such as subtitles, image, and audio descriptions.
|
PMC11698405_p27
|
PMC11698405
|
DISCUSSION
| 3.103554 |
other
|
Review
|
[
0.19105501472949982,
0.0033655078150331974,
0.8055794835090637
] |
[
0.02485385537147522,
0.09337685257196426,
0.8810028433799744,
0.0007664309814572334
] |
en
| 0.999998 |
To the best of our knowledge, this is the first study to date showing the application of additional visual methods in a published QES. Visual methods are currently underused and underreported in QESs. QES authors should consider making use of available visual methods, particularly when involving members of the public during synthesis. Selecting the appropriate visual method for synthesis should be guided by its ability to enhance analysis and align with the study's objectives. Methods must suit the content and stage of synthesis, whether for idea development or presenting findings. Practical factors, including available resources and the team's technical skills, are crucial, especially for remote work. Additionally, group‐based methods may require significant preparation, facilitation, and specialized skills. Careful consideration of these aspects will ensure the effective and efficient use of visual methods.
|
PMC11698405_p28
|
PMC11698405
|
CONCLUSIONS
| 4.00247 |
biomedical
|
Study
|
[
0.9867985844612122,
0.0004117825301364064,
0.012789689935743809
] |
[
0.9960636496543884,
0.002313837641850114,
0.0015563219785690308,
0.00006624427624046803
] |
en
| 0.999996 |
Dr. Mayara Silveira Bianchim, Professor Emma France and Professor Jane Noyes all participated in conceptualization, data curation, formal analysis, investigation, methodology, project administration, resources, software, visualization, writing of original draft and review and editing.
|
PMC11698405_p29
|
PMC11698405
|
AUTHOR CONTRIBUTIONS
| 0.98114 |
other
|
Other
|
[
0.06715048104524612,
0.002336296485736966,
0.9305132031440735
] |
[
0.0015328138833865523,
0.9978596568107605,
0.0002851466997526586,
0.00032238682615570724
] |
en
| 0.999996 |
The authors declare no conflict of interest.
|
PMC11698405_p30
|
PMC11698405
|
CONFLICT OF INTEREST STATEMENT
| 0.871033 |
other
|
Other
|
[
0.07701165974140167,
0.002940838923677802,
0.9200475215911865
] |
[
0.01240311935544014,
0.984458327293396,
0.002042097970843315,
0.0010964975226670504
] |
en
| 0.999995 |
The peer review history for this article is available at https://www.webofscience.com/api/gateway/wos/peer-review/10.1002/cesm.70009 .
|
PMC11698405_p31
|
PMC11698405
|
PEER REVIEW
| 1.069338 |
other
|
Other
|
[
0.01895168609917164,
0.0016148011200129986,
0.9794334769248962
] |
[
0.0008906032890081406,
0.9981902241706848,
0.0005116704269312322,
0.00040751195047050714
] |
en
| 0.999997 |
It is generally accepted that cancers develop and evolve by adaptive genetic and molecular changes over time [ 1 – 3 ]. Sequential selection from this process of evolution leads to clones and subclones with altered phenotype leading to more aggressive behaviour. Ultimately, these phenotypic changes lead to metastatic spread and drug resistance, which is responsible for the majority of cancer-related deaths .
|
39752458_p0
|
39752458
|
Introduction
| 4.00814 |
biomedical
|
Review
|
[
0.9989913105964661,
0.00048616534331813455,
0.000522553629707545
] |
[
0.2268858104944229,
0.018326791003346443,
0.7540282011032104,
0.0007592227193526924
] |
en
| 0.999996 |
It is necessary to distinguish accurately tumour heterogeneity and determine clonal evolution by identifying the clonal source of metastatic disease. This not only has an impact on the understanding of tumour progression but the relationship between clonal composition and the index lesion is also important and clinically relevant for both molecular diagnostics and focal therapy [ 5 – 8 ]. Indeed, it would help and support treatment decision-making by using new markers to determine whether cells are indicative of aggressive disease or to predict sensitivity to treatment.
|
39752458_p1
|
39752458
|
Introduction
| 3.962033 |
biomedical
|
Study
|
[
0.9989458918571472,
0.0006020726286806166,
0.00045209593372419477
] |
[
0.4412354528903961,
0.11931825429201126,
0.43828070163726807,
0.0011655956041067839
] |
en
| 0.999996 |
One of the challenges to understand the tumour heterogeneity is that the origin of mutations occurring in cancer can be hereditary or somatic. Although identification of inherited mutations is relatively straightforward, these are only responsible for 5 to 10% of all cancer [ 9 – 11 ]. By contrast, post-developmental somatic genetic alterations are usually only present in a small fraction of clonally-expanding cells but constitute the most common cause of cancer . To identify these somatic mutations in situ , techniques such as laser capture microdissection have been employed, but this requires pre-knowledge to isolate a specific cell type or region of interest from a tissue section and so limits the ability to undertake a de novo spatial clonal analysis. Recently, these limitations have been overcome by spatial transcriptomics, which allows the analysis of gene expression profiles in a tissue sample while preserving spatial tissue architecture. This approach captures transcripts in situ , with sequencing of barcoded reads carried out ex situ and then mapped back to the cells of origin . This cutting-edge technology permits visualisation and in-depth analysis of intra-tumoural heterogeneity and could permit spatial analysis of clonal evolution.
|
39752458_p2
|
39752458
|
Introduction
| 4.408013 |
biomedical
|
Study
|
[
0.9990454316139221,
0.0005387849523685873,
0.00041578998207114637
] |
[
0.7353439927101135,
0.0025605529081076384,
0.2614963948726654,
0.0005990771460346878
] |
en
| 0.999997 |
Clonal evolution and, more precisely, the relationship between clones and subclones is often represented and visualised by phylogenetic trees . These phylogenetic trees have been used mainly in recent years to study data derived from DNA sequencing . However, to use spatial transcriptomics to study clonal evolution, it is necessary to know whether RNA can also be used to determine clonal phylogenetic hierarchies. In this meta-analysis, we investigate the correlation between DNA sequencing data and RNA sequencing data using phylogenies derived from inferred single-nucleotide variants (SNV) and copy-number variants (CNV) in order to determine whether transcriptome-derived phylogenies can accurately reflect genome-based phylogenies.
|
39752458_p3
|
39752458
|
Introduction
| 4.138758 |
biomedical
|
Study
|
[
0.9995627999305725,
0.0002517241518944502,
0.00018536654533818364
] |
[
0.9988974332809448,
0.00020398737979121506,
0.0008302861242555082,
0.00006821678834967315
] |
en
| 0.999995 |
In order to benchmark and validate methods to generate phylogenies derived from inferred single-nucleotide variants and copy-number variants, we reviewed the literature and found a recent publication which simultaneously extracted both DNA and RNA, from the same exact single tumour cells, and performed whole genome and whole transcriptome sequencing . These public datasets contained data from 38 single cells that had been subject to simultaneous WGS and RNAseq using the SIDR methodology. Han et al describe a quality control process to determine which cells were satisfactorily sequenced for downstream analysis, leaving a total of 30 paired samples that passed all qc metrics .
|
39752458_p4
|
39752458
|
Data acquisition
| 4.072346 |
biomedical
|
Study
|
[
0.9995923638343811,
0.00023905152920633554,
0.00016866503574419767
] |
[
0.9986816048622131,
0.0002406892308499664,
0.0010016087908297777,
0.00007612617628183216
] |
en
| 0.999996 |
Next, we reviewed the literature for publications and available data from patients with prostate cancer, who had both conventional bulk DNA and RNA sequencing applied to the same specimen, and from patients that had three or more total specimens. We identified patient A21 , patient 498 . For further validation and comparison, WGS and RNA-microarray data were obtained from cases 6, 7 and 8 from Cooper et al. .
|
39752458_p5
|
39752458
|
Data acquisition
| 3.935807 |
biomedical
|
Study
|
[
0.9994938373565674,
0.0003137435996904969,
0.0001923675590660423
] |
[
0.9986227750778198,
0.00039287793333642185,
0.000867091934196651,
0.0001172616466647014
] |
en
| 0.999997 |
Lastly, we obtained paired WGS sequencing data and paired Spatial Transcriptomics data from the n = 12 regions from a single patient in a recent publication .
|
39752458_p6
|
39752458
|
Data acquisition
| 3.246075 |
biomedical
|
Study
|
[
0.9986431002616882,
0.00034188959398306906,
0.001014971756376326
] |
[
0.9963955283164978,
0.002938143676146865,
0.00047888135304674506,
0.00018741482926998287
] |
en
| 0.999999 |
Only 38 paired cells were available with both scWGS and scRNAseq . After removing the individual cells that failed either scWGS or scRNAseq QC left only 30 in common.
|
39752458_p7
|
39752458
|
Quality control of single-cell whole genome sequencing data
| 2.581897 |
biomedical
|
Study
|
[
0.9958353042602539,
0.0006241862429305911,
0.0035405242815613747
] |
[
0.9469343423843384,
0.05157986655831337,
0.0008439623052254319,
0.0006418429547920823
] |
en
| 0.999995 |
Paired end sequencing data was aligned against the GRCh38 reference genome with the Burrow-Wheeler Aligner (0.7.17).
|
39752458_p8
|
39752458
|
DNA sequencing preprocessing of single-cell whole genome sequencing data
| 3.970173 |
biomedical
|
Study
|
[
0.9991995692253113,
0.00028297651442699134,
0.0005174172110855579
] |
[
0.9673278331756592,
0.03162108734250069,
0.0006700109806843102,
0.000381135061616078
] |
en
| 0.999997 |
WGS variants were called using a pipeline broadly based on the GATK best practice Germline short variant discovery (SNPs + Indels) workflow using Picard (2.23.0) and GATK (4.1.7.0). This consisted of pre-processing the raw alignment to mark duplicate reads and perform base recalibration. Raw variants were called using GATK HaplotypeCaller in GVCF mode followed by GATK GenotypeGVCFs. Finally the raw variants were filtered to generate a downstream analysis ready cell by variant dataset.
|
39752458_p9
|
39752458
|
iSNV calling from single-cell whole genome sequencing data
| 4.159884 |
biomedical
|
Study
|
[
0.9994080066680908,
0.0003783751744776964,
0.00021361141989473253
] |
[
0.9870834350585938,
0.011782264336943626,
0.0008237509173341095,
0.000310494244331494
] |
en
| 0.999996 |
The processed variants were converted to an Identity by State matrix, clustered and converted to dendrogram format in R using the SNPrelate package .
|
39752458_p10
|
39752458
|
iSNV calling from single-cell whole genome sequencing data
| 3.807562 |
biomedical
|
Study
|
[
0.9992057681083679,
0.00020864275575149804,
0.0005855823401361704
] |
[
0.9669584035873413,
0.03208472207188606,
0.0006950613460503519,
0.00026181378052569926
] |
en
| 0.999996 |
After preprocessing and QCing, n = 30 cells remained, and were then analyzed by Gingko . BAM files were converted to.BED files using bamToBed in BedTools. We utilized a variable bin size of 50 kb, with 101 bp reads . The clustering of CNV’s was performed using ward linkage and Euclidean distance as the distance metric. Copy-Number tree results were downloaded in Newick format for further downstream analysis.
|
39752458_p11
|
39752458
|
gCNV calling from single-cell whole genome sequencing data
| 4.063564 |
biomedical
|
Study
|
[
0.9995612502098083,
0.0002037007361650467,
0.00023493956541642547
] |
[
0.9991387128829956,
0.0006034919060766697,
0.00018913959502242506,
0.00006868243508506566
] |
en
| 0.999996 |
Paired end sequencing data was aligned against the GRCh38 reference genome with STAR (2.7.3a) with per-sample 2-pass mapping and annotation with comprehensive gene annotation data from GENCODE GRCh38. Gene counts per cell were tabulated from aligned data using the featureCounts function from the Subread (1.6.4) package.
|
39752458_p12
|
39752458
|
RNA sequencing preprocessing of single-cell whole transcriptome sequencing data
| 4.145833 |
biomedical
|
Study
|
[
0.9995428323745728,
0.00021103488688822836,
0.0002461428812239319
] |
[
0.9951931238174438,
0.0041910442523658276,
0.0004525334807112813,
0.00016341995797120035
] |
en
| 0.999996 |
iSNV calling from RNAseq data was performed according to the pipeline outlined by Zhou et al and based on GATK best practices . The STAR aligned data underwent sorting, annotation with read group information, deduplication, SplitNCigarReads, realignment, and base recalibration, before variant calling with GATK (3.8.0) HaplotypeCaller. Raw iSNVs were processed by DENDRO to calculate a genetic divergence matrix between cells and to generate a phylogeny using hierarchical clustering (ward.D method).
|
39752458_p13
|
39752458
|
iSNV calling from single-cell whole transcriptome sequencing data
| 4.168663 |
biomedical
|
Study
|
[
0.9995799660682678,
0.00023375364253297448,
0.00018625413940753788
] |
[
0.998420000076294,
0.0011004225816577673,
0.0003830410714726895,
0.00009652215521782637
] |
en
| 0.999998 |
Data were analyzed using R version 4.0.1, and inferCNV (version 1.4.0) . A merged file from the previously described pre-processing steps, containing feature counts for each cell, as well as a gene position file, and an annotation file were generated for input to inferCNV. An inferCNV object was created with no defined reference group. After creation of the InferCNV object, inferCNV was ran with the following parameters: cutoff = 0.1, cluster_by_groups = FALSE, denoise = TRUE, HMM = TRUE.
|
39752458_p14
|
39752458
|
iCNV calling from single-cell whole transcriptome sequencing data
| 3.947151 |
biomedical
|
Study
|
[
0.9993484616279602,
0.00015217720647342503,
0.00049938028678298
] |
[
0.9930601119995117,
0.0064289625734090805,
0.0003913942782673985,
0.00011952115164604038
] |
en
| 0.999996 |
For comparison of dendrograms created by WGS-CNVs (Gingko) and inferred CNV’s from RNA (InferCNV), the clust2.newick and infercnv.21_denoised.observations_dendrogram.txt files were imported into R and analyzed with packages dendextend and phylogram.
|
39752458_p15
|
39752458
|
Comparison of dendrograms from single-cells
| 3.887988 |
biomedical
|
Study
|
[
0.9994896650314331,
0.00014617906708735973,
0.0003640755021478981
] |
[
0.9937804341316223,
0.005606642924249172,
0.0004809906240552664,
0.00013198588567320257
] |
en
| 0.999997 |
RNA counts were analzyed, by comparing individual gene count values to the median (MED) and standard deviation (SD) values of global RNA count values per sample: if the count value was less than MED-SD, then it was assigned a value of -1, else if the count value was greater than MED+SD, then it was assigned a value of +1, else it was assigned 0. The resultant values from each sample or cell were converted into a phydat object using phangorn ’s function phyDat(), with the parameters type = "USER", levels = c(’-1’, ’0’, ’1’). Pairwise distances between cells or tissue samples were calculated using the phangorn dist.ml() function with previously described phyDat() object as input. UPGMA clustering was applied using the phangorn upgma() function and converted to a dendrogram using the dendextend function as.dendrogram().
|
39752458_p16
|
39752458
|
Analysis of transcript derived phylogenies
| 4.149493 |
biomedical
|
Study
|
[
0.9995779395103455,
0.00021137263684067875,
0.0002106895699398592
] |
[
0.9984856247901917,
0.000921640545129776,
0.0005170938093215227,
0.00007565871783299372
] |
en
| 0.999998 |
Data were analyzed as previously described with the following exceptions. Original 1k array Spatial Transcriptomics data were obtained. As gCNV comparison data were from whole sections, all ST count data were ‘pseudo-bulked’ within sections, resulting in 12 pseudobulked count matrices for analyses. InferCNV was ran using standard parameters with no reference set. The resultant infercnv . observations_dendrogram . txt dendrogram was used for downstream tanglegram analysis.
|
39752458_p17
|
39752458
|
CNV calling from spatial transcriptomics data
| 3.931816 |
biomedical
|
Study
|
[
0.9992778897285461,
0.00018008094048127532,
0.0005420470261014998
] |
[
0.9988895058631897,
0.0008595949620939791,
0.00018758850637823343,
0.00006336775550153106
] |
en
| 0.999996 |
The original outputs for CNV calling from Berglund et al., were not available, and the ReadDepth package used to generate the calls has since been deprecated by the author . Thus, we ran a new pipeline using the WGS data from Berglund et al . FASTQ files were obtained and aligned to HG38. Battenberg CNV analyses were performed using the matched reference blood FASTQ data as the reference.
|
39752458_p18
|
39752458
|
Comparison of dendrograms from WGS and ST
| 3.951582 |
biomedical
|
Study
|
[
0.9995425939559937,
0.0001659153786022216,
0.00029152954812161624
] |
[
0.9980506896972656,
0.0015710084699094296,
0.00027521917945705354,
0.0001030982966767624
] |
en
| 0.999998 |
The Battenberg package (v2.2.10) was used to determine copy number, and estimate tumour purity and ploidy from WGS data. Impute2 (v2.3.0) was used with GRCh38 loci for phasing germline heterozygous SNPs. The Battenberg pipeline was run with the following parameters: segmentation_gamma = 10, phasing_gamma = 10, platform_gamma = 1, min_ploidy = 1.6, max_ploidy = 4.8, min_rho = 0.13, max_rho = 1.02.
|
39752458_p19
|
39752458
|
Copy number calling with Battenberg
| 4.124702 |
biomedical
|
Study
|
[
0.9992275238037109,
0.00043868046486750245,
0.00033374608028680086
] |
[
0.9704659581184387,
0.0283685140311718,
0.0007271806243807077,
0.0004383118648547679
] |
en
| 0.999997 |
The recal_subclones . txt text files were downloaded for each of the 12 prostate tissues, and processed through a custom pipeline as follows. Battenberg CNV segments were binned into 1200 bp segments and aligned, generating n = 2439447 bins across the genome. CN amplifications and deletions were called at thresholded values of -1.5 and 2.5 respectively. Next, the processed bins from all samples were merged to create a CN bin matrix. CN calls for segments that were shared for all samples were dropped, resulting in a final matrix containing n = 28 discordant CN calls.
|
39752458_p20
|
39752458
|
Copy number calling with Battenberg
| 4.127077 |
biomedical
|
Study
|
[
0.9995790123939514,
0.00023302137560676783,
0.00018788562738336623
] |
[
0.9990963935852051,
0.0005506736342795193,
0.00026968191377818584,
0.00008326056558871642
] |
en
| 0.999996 |
This CN matrix was then used similarly as described by Berglund et al., with the R package pvclust , and n = 1000 bootstraps. The structure of the cluster was converted to a dendrogram using the R package dendrogram for comparison to the inferCNV dendrogram via a tanglegram using the dendextend package (step2side).
|
39752458_p21
|
39752458
|
Copy number calling with Battenberg
| 3.991659 |
biomedical
|
Study
|
[
0.999110758304596,
0.00015830440679565072,
0.0007309949724003673
] |
[
0.998267650604248,
0.0014265151694417,
0.0002395338233327493,
0.00006629518611589447
] |
en
| 0.999997 |
In order to benchmark performance of transcriptome-derived phylogenies, we first identified an individual cancer cell dataset with simultaneously isolated DNA and RNA (SIDR) from single cells . The SIDR approach resulted in paired DNA and RNA nucleic acid extractions from isolated single cells of three different cancer cell lines: HCC827, MCF7 and SKBR3 . They then performed whole-genome sequencing (WGS) and RNA-sequencing on the extracted nucleic acids . Given the cell purity, we hypothesized that WGS and RNA sequencing data from these individual cancer cells could be analyzed in an “in-silico” experiment to benchmark performance of transcriptome and genome-derived phylogenies.
|
39752458_p22
|
39752458
|
Transcriptome and genome derived clonal phylogenies from single cancer cells
| 4.069427 |
biomedical
|
Study
|
[
0.9995428323745728,
0.00023741186305414885,
0.0002198963484261185
] |
[
0.9994206428527832,
0.00022659481328446418,
0.0002980399294756353,
0.000054642281611450016
] |
en
| 0.999997 |
We performed secondary analyses of the published, publicly available DNA and RNA sequencing data from Han et al . After quality control , we identified a total of 30 cells that had both sufficient quality DNA and RNA sequencing data, resulting in a dataset of a total of 10 MCF7 cells, 7 HCC827 cells, and 13 SKBR3 cells for analysis. We performed genomic SNV (gSNV) and inferred RNA-based SNV (iSNV) analyses from all cells, derived dendrograms, and performed tanglegram analysis to compare gSNV and iSNV dendrograms. In analysis of gSNVs and iSNVs, we observed a high concordance of transcriptome and genomic phylogenies . Next, we performed genomic CNV (gCNV) and inferred RNA-based CNV (iCNV) analyses from all cells, derived dendrograms, and performed tanglegram analysis to compare gCNV and iCNV dendrograms. In analysis of gCNVs and iCNVs, we also observed a high concordance of transcriptome and genomic phylogenies . We therefore concluded that RNA-derived inference of genomic SNVs and CNVs in three purified single cell populations generated strong phylogenetic concordance.
|
39752458_p23
|
39752458
|
Transcriptome and genome derived clonal phylogenies from single cancer cells
| 4.16688 |
biomedical
|
Study
|
[
0.9994655251502991,
0.0002946005843114108,
0.00023984046129044145
] |
[
0.999352753162384,
0.00017036240024026483,
0.0004087506385985762,
0.00006808367470512167
] |
en
| 0.999993 |
Having established high in-silico concordance of transcriptome and genome-derived phylogenies, we then sought to study prostate cancer sequencing data from patients with paired DNA and RNA extracted from the same tumours. Gundem and colleagues reported WGS data from 55 disseminated tumour samples, from 10 patients that underwent rapid-autopsy after death due to prostate cancer . A subset of n = 7 tumour specimens from patient A21 also underwent RNA-sequencing .
|
39752458_p24
|
39752458
|
Transcriptome and genome derived clonal phylogenies from bulk prostate cancer sequencing
| 4.047873 |
biomedical
|
Study
|
[
0.9994935989379883,
0.00029877465567551553,
0.00020754651632159948
] |
[
0.9995238780975342,
0.000203566494747065,
0.00020188355119898915,
0.00007069431012496352
] |
en
| 0.999997 |
We performed secondary analyses of RNA sequencing data from Bova et al. and obtained iSNV and iCNV calls. From the iSNV and iCNV calls, we separately performed phylogenetic analyses through hierarchical clustering, resulting in iSNV and iCNV derived dendrograms . In both iSNV and iCNV analyses, liver metastases (C, G, H, E) clustered together. In both iSNV and iCNV analyses, Clones F, A and J also clustered together. Clone I, clustered together with the liver metastases in iCNV analyses, but not in the iSNV analyses. Taken together, the iSNV and iCNV dendrograms reflect the manually assembled clonal phylogeny published by Gundem et al, .
|
39752458_p25
|
39752458
|
Transcriptome and genome derived clonal phylogenies from bulk prostate cancer sequencing
| 4.08881 |
biomedical
|
Study
|
[
0.99944669008255,
0.0002447826263960451,
0.0003084830823354423
] |
[
0.9995330572128296,
0.00015353622438851744,
0.0002638677542563528,
0.000049509075324749574
] |
en
| 0.999996 |
Next, we analyzed data from patient 498, analyzed by Hong et al.. This patient’s primary prostate cancer progressed to distant skeletal metastases, which then further re-seeded the prostatic bed. Of the n = 7 reported specimens, a total of n = 4 also underwent RNA sequencing. We performed secondary analyses of the RNA sequencing data and obtained iSNV and iCNVcalls. From the iSNV and iCNV calls, we separately performed phylogenetic analyses through hierarchical clustering, resulting in iSNV and iCNV derived dendrograms . In contrast to the results from Gundem et al., both iSNV and iCNV presenting differing tree patterns as compared to one another.
|
39752458_p26
|
39752458
|
Transcriptome and genome derived clonal phylogenies from bulk prostate cancer sequencing
| 4.081288 |
biomedical
|
Study
|
[
0.9994338154792786,
0.00033412137418054044,
0.00023212346422951669
] |
[
0.9995133876800537,
0.0001837082381825894,
0.0002270684199174866,
0.00007582895341329277
] |
en
| 0.999997 |
We then analyzed data from primary prostate cancer cases 6, 7 and 8, analyzed by Cooper et al., who each underwent radical prostatectomy, from which multiple tissue punches of both normal and tumour regions were sampled . The samples then underwent WGS, which were subsequently analyzed and tumour phylogenies were manually produced. From a subset of the same specimens, adjacent tissue sections were taken and subjected to RNA microarray analysis. Additionally, each patient had a blood sample taken, that also underwent RNA microarray analysis. Being microarray data, we were unable to derive iSNV and iCNVs. Therefore, we built a custom pipeline to analyze and cluster the RNA microarray data directly, to generate hierarchical clustering represented as a dendrogram. To benchmark this pipeline, we first compared gCNV and gSNV to SIDR data and observed entanglement values of 0.21 and 0.16 respectively. Having established this pipeline, we then applied it to the microarray data from Cooper et al to generate dendrograms. These dendrograms were then analyzed in comparison to the published WGS-based gDNA phylogenies . In all three patients, the blood specimen clustered separately from the prostate tumour and normal tissue specimens. In cases 7 and 8, the (multiple) normal tissue specimens clustered together and distinctly clustered separately from the tumours, whereas in case 6 the two normals clustered with T 2 , T 3 and T 4 , separate from T 1 . Taken together, RNA-microarray derived dendrograms were able to recapitulate manually assembled WGS-derived gDNA phylogenies.
|
39752458_p27
|
39752458
|
Transcriptome and genome derived clonal phylogenies from bulk prostate cancer sequencing
| 4.164872 |
biomedical
|
Study
|
[
0.9994456171989441,
0.0003359808470122516,
0.00021844641014467925
] |
[
0.9993453621864319,
0.00016848993254825473,
0.00040534534491598606,
0.0000807364922366105
] |
en
| 0.999998 |
Next, we then sought to determine the ability of spatial transcriptome derived tumour phylogenies to recapitulate gDNA based phylogenies. Spatial transcriptomics generates transcriptome signal from poly-A captured short 3’ RNA sequences of up to 200 bp length, sufficient for hg38 alignment and, we deduced, sufficient to enable iCNV analysis. Berglund and colleagues performed spatial transcriptomics (ST) on a total of n = 12 prostate tissue regions from a patient that underwent radical prostatectomy . Of these sections, a total of n = 4 were detected to have prostate cancer. The authors also performed WGS on adjacent serial sections from each of these 12 tissue sections, as well as a matched blood specimen from the same patient. Given that WGS is not spatially resolved, we performed ‘pseudo-bulked’ iCNV analyses on ST data from all 12 sections, and generated a clonal phylogeny in the form of a dendrogram. We also performed gDNA CNV calling from each of the 12 sections to generate a clonal phylogeny which was represented as a dendrogram. We then compared the iCNV and gCNV derived dendrograms using a tanglegram and observed a degree of concordance consistent with the resolution of the data . Interestingly, three of the tumour regions (P2_4, P1_3, P1_2) clustered together in the iCNV analysis, whereas they were represented on different subclusters in the gCNV phylogeny, suggesting that the iCNV approach may have generated a more accurate clustering in this case.
|
39752458_p28
|
39752458
|
Transcriptome and genome derived clonal phylogenies from bulk WGS and spatial transcriptomics from multi-region prostate cancer sequencing data
| 4.185534 |
biomedical
|
Study
|
[
0.9994664788246155,
0.00033062996226362884,
0.00020288096857257187
] |
[
0.9992812275886536,
0.0001945256080944091,
0.00043845930485986173,
0.00008571342914365232
] |
en
| 0.999997 |
Results from single-cancer cells demonstrate that transcriptome-derived iCNV and iSNV phylogenies are highly concordant with ground truth gDNA based phylogenies. In our in-silico analyses, the analysed data represent a highly selected and well controlled set of cells, with a 1:1 pairing of data resulting in extremely low entanglement values of the resultant tanglegrams. These results are in line with findings by Han et al., where they reported positive correlations for all three cell lines between gCNV and mRNA expression levels that were binned across the genome . Our quantitative results in single-cells were supported by qualitative comparisons in prostate cancer cells where we did not have access to all ground truth data to enable a true like-to-like comparison.
|
39752458_p29
|
39752458
|
Discussion
| 4.11709 |
biomedical
|
Study
|
[
0.9994605183601379,
0.00025669674505479634,
0.00028289982583373785
] |
[
0.9994643330574036,
0.00013645755825564265,
0.00034705622238107026,
0.000052191404392942786
] |
en
| 0.999996 |
There are limitations to consider in the construction of transcriptome-derived inferred phylogenies. First, the design and resolution of the genetic sequencing technologies can greatly affect the ‘resolved signal’. For example, only 2% of the entire genome is translated into proteins , and thus the genomic coverage of the transcriptome represents a sub-fraction of potential data for mapping tumour phylogenies. This is further compounded by variable coverage within transcripts themselves: many modern scRNAseq and spatial transcriptomics techniques, such as Chromium and Visium offered by 10x Genomics, perform polyA capture, resulting in sequencing of 75–300 bp near the end of transcripts. Further, for iSNV approaches , the coverage of transcribed SNV loci can be extremely low being confined to the exome. Potential issues with iSNVs seem to be mitigated in iCNV approaches [ 34 – 36 ], which incorporate machine learning algorithms to bin genomically adjacent transcripts. Additionally transcriptional regulation programs [ 37 – 39 ] can affect transcription without any changes to copy-number status: these may result in false positives or negatives in iCNV analyses. Indeed, Han et al observed a discrepancy in Chromosome 3 gCNV calls and expression profiles . Finally, one key factor affecting the ability of iCNV/iSNV (as well as gCNV and gSNV) approaches is use of well annotated references. All of the patient-derived WGS analyses in the data used in this publication had access to reference blood controls for calling gCNVs and gSNVs. Such data are not often taken or obtained for RNA sequencing, and thus are unavailable for iCNV and iSNV calling. This can also be further compounded by tissue or cell-of-origin transcriptional programs unrelated to copy-number alterations. Spatial transcriptomic data offers the opportunity to compensate for this through selection of histologically normal regions as control references.
|
39752458_p30
|
39752458
|
Discussion
| 4.36075 |
biomedical
|
Study
|
[
0.9992990493774414,
0.0003832429356407374,
0.0003177195321768522
] |
[
0.998259961605072,
0.0002782844239845872,
0.0013684699079021811,
0.00009327851876150817
] |
en
| 0.999995 |
As the tumour evolution community moves increasingly to single cell and spatial resolution, our ability to resolve clonal and subclonal tumour evolution patterns will greatly increase. Our results underscore the need for proper reference sets when calling iCNV and iSNV derived clonal phylogenies. These issues may be partly mitigated by next-generation iCNV and iSNV algorithms that incorporate both into combined iSNV+iCNV phylogenies . Other approaches incorporating evolutionary game theory through mathematical models could aid in resolving clonal phylogenies . Further work will also need to be done to identify and control for non copy-number alteration derived transcriptional regulation leading to further refinements in the ability of transcript-based clonal phylogenies to resolve ground truth.
|
39752458_p31
|
39752458
|
Discussion
| 4.149629 |
biomedical
|
Study
|
[
0.9994663596153259,
0.0002159763389499858,
0.00031761001446284354
] |
[
0.9986750483512878,
0.00030043552396818995,
0.0009621192002668977,
0.00006227511039469391
] |
en
| 0.999997 |
These results suggest that transcript-based inferred phylogenies recapitulate conventional genomic phylogenies. As the tumour evolution community moves increasingly to single cell and spatial resolution, our ability to resolve clonal and subclonal tumour evolution patterns will greatly increase. Further work will need to be done to increase accuracy, genomic, and spatial resolution.
|
39752458_p32
|
39752458
|
Conclusions
| 3.969974 |
biomedical
|
Study
|
[
0.999692440032959,
0.00010495625610928982,
0.00020257357391528785
] |
[
0.9956024885177612,
0.002066710963845253,
0.0022117861080914736,
0.00011899923265445977
] |
en
| 0.999996 |
Lung cancer is the leading cause of cancer mortality in the United States (U.S.) . With an estimated 154,050 deaths per year, lung cancer accounts for approximately 25% of all cancer deaths . The prognosis for lung cancer differs significantly between cases depending on the progression of the disease and subtype. Lung cancer is categorized into two main types: non-small cell lung cancer and small cell lung cancer. Non-small cell lung cancer accounts for approximately 85% of lung cancer cases, with a reported 5-year survival rate of 26.5% . Small cell lung cancer, the less common type, accounts for only 15% but is more aggressive and metastasizes rapidly, resulting in a lower 5-year survival rate of 6.7% .
|
39752448_p0
|
39752448
|
Introduction
| 4.046522 |
biomedical
|
Review
|
[
0.9982561469078064,
0.0005267307278700173,
0.0012171893613412976
] |
[
0.13964983820915222,
0.015103117562830448,
0.8447575569152832,
0.0004895293386653066
] |
en
| 0.999997 |
Treatment of lung cancer varies from patient to patient depending on the stage at diagnosis, type, and progression . Treatment methods such as surgical resection, chemotherapy, and radiation therapy are all known to improve survival rates among patients, with surgical treatment demonstrating the best outcomes [ 5 – 7 ]. Delays in treatment refer to the time elapsed between a diagnosis a start of definitive treatment. Several studies have shown that early detection and treatment of lung cancer offers 65–90% 5-year survival duration, especially at the localized stage (small tumor lung cancer) . Studies have shown that the delay in initiating lung cancer treatment ranges from 2–10 weeks [ 10 – 12 ], which can impact patient survival. However, there are conflicting findings about the effect of treatment delays on lung cancer patient survival. Prolonged delays have been linked to accelerated disease progression and poorer survival outcomes . On the other hand, a retrospective study by Salomaa et al. found that a longer delay in a specialist treatment was associated with better survival in advanced-stage lung cancer . Similarly, Anggondowati et al. , used a national cancer database and found that a time to treatment of 4.1 to 6 weeks was associated with a lower risk of death for early-stage localized non-small cell lung cancer compared to treatment within 1 day to 4 weeks after diagnosis . However, a subset analysis of their findings revealed that an extended time to surgery for early-stage disease was associated with a higher risk of death .
|
39752448_p1
|
39752448
|
Introduction
| 4.085981 |
biomedical
|
Study
|
[
0.998824417591095,
0.0006289766170084476,
0.0005466227885335684
] |
[
0.784410834312439,
0.0007893173606134951,
0.2144051492214203,
0.000394730013795197
] |
en
| 0.999995 |
Despite the improving survival rates in lung cancer, health disparities in treatment persist. Using data from the National Center for Health Statistics, Yao et al. found that disparities continue in the U.S., with rural Appalachia region experiencing higher incidence and mortality rates of cancers than urban non-Appalachian region . While the rate of lung cancer among Blacks is lower than among Whites in Tennessee (68 per 100,000 people vs 77 per 100,000), Black patients have a lower 5-year survival rate than Whites (19% vs. 22%) . Recent studies in Tennessee and using national databases, respectively, have reported Black patients are significantly less likely to be recommended surgical treatment than White patients, resulting in lower survival rates than White individuals [ 16 – 19 ]. Beyond racial disparities, other sociodemographic factors substantially influence the time to treatment and prolonged delay [ 10 , 11 , 20 – 24 ]. In addition, higher socioeconomic status (SES) has been linked to better outcomes, while lower SES is associated with poorer outcomes . Although previous research has examined the impact of sociodemographic factors on treatment delays and survival rates among lung cancer patients, there remains a notable gap in understanding these dynamics among Tennessean patients. This is particularly concerning, as Tennessee reports significantly higher rates of new lung cancer cases compared to the national average (73% vs. 57%) . The lack of research focused on Tennessee is critical, given the state’s elevated lung cancer incidence and the potential for sociodemographic factors—such as race, income, and access to healthcare—to disproportionately affect treatment outcomes in this region. Expanding research efforts in Tennessee could provide valuable insights for addressing these disparities.
|
39752448_p2
|
39752448
|
Introduction
| 4.073739 |
biomedical
|
Study
|
[
0.998683512210846,
0.00044354426790960133,
0.0008729370892979205
] |
[
0.9766604900360107,
0.00046532225678674877,
0.02275172621011734,
0.0001225057931151241
] |
en
| 0.999995 |
The present study is the first to investigate the influence of sociodemographic factors, type of insurance coverage, cancer stage, and surgical treatment on time to treatment initiation disparities (i.e., treatment time from diagnosis) of invasive lung cancer in Tennessee comparing outcomes within and between Black and White patients. We hypothesized that Black patients with invasive lung cancer would experience longer times to treatment initiation than their White counterparts. This study has the potential to enhance treatment strategies for lung cancer, leading to improved quality of life and survival outcomes for patients diagnosed with the disease.
|
39752448_p3
|
39752448
|
Introduction
| 4.091619 |
biomedical
|
Study
|
[
0.9983275532722473,
0.0012425434542819858,
0.0004298612184356898
] |
[
0.9992031455039978,
0.00044868612894788384,
0.0002468475140631199,
0.00010140113590750843
] |
en
| 0.999995 |
We obtained a retrospective population-based registry data of 46,848 Tennessee residents diagnosed with histologically confirmed invasive (malignant) or non-invasive lung cancer as the primary site of diagnosis as coded by the International Classification of Diseases for Oncology, Third Edition (ICD-O-3), from January 1, 2005, to December 31, 2015. The data was reported by the Tennessee Cancer Registry (TCR), which is a population-based, central cancer registry established and responsible for collecting and monitoring cancer incidence by Tennessee law . We conducted a complete analysis of 42,970 cases of invasive lung cancer that received treatment or procedure within 12 months (52 weeks) and were diagnosed at the localized, regional, and distant stages, excluding the in-situ, and unknown stages of the lung cancer. The in-situ stage is considered pre-cancerous (i.e., not a true cancer) and non-invasive . We also excluded other races and all cases with missing data for any of the selected variables from our analysis. Therefore, a total of 8.3% of the data (3,878 cases) were excluded. See Fig 1 for the sampling inclusion and exclusion procedure of patients. We focused on invasive lung cancer, because it tends to spread to other lymph nodes and, therefore, can significantly impact patient survival , a crucial factor in lung cancer treatment. Data are available by request to the Tennessee Department of Health-TCR . All analytical files are also available by reasonable request and Tennessee Department of Health-TCR approval.
|
39752448_p4
|
39752448
|
Study population and data
| 4.108529 |
biomedical
|
Study
|
[
0.9989010095596313,
0.0007644135621376336,
0.0003346178273204714
] |
[
0.9994188547134399,
0.00024795442004688084,
0.0002485929580871016,
0.00008451322355540469
] |
en
| 0.999996 |
The study’s dependent variable is the time to treatment initiation of invasive lung cancer, defined as the time from diagnosis to the start of definitive treatment. The time from diagnosis to treatment was measured as the median time to definitive treatment (in weeks); with a treatment delay defined as >2.7 weeks (i.e., treatment delay) .
|
39752448_p5
|
39752448
|
Dependent variable or outcome
| 3.961029 |
biomedical
|
Study
|
[
0.9975501894950867,
0.0020642129238694906,
0.0003856223775073886
] |
[
0.9988089799880981,
0.0008649058290757239,
0.00019761767180170864,
0.0001285173639189452
] |
en
| 0.999996 |
The independent variables included sociodemographic characteristics, race, age at diagnosis, marital status, county of residence, type of health insurance, stage of invasive lung cancer, and whether patients received surgical treatment. Age was categorized into 5 groups: <45; 45–54; 55–64, 65–74, and ≥75 years based on the aging criteria by the National Institute of Aging , and race as Black and White. Marital status was classified as single/never married, married/common-law, divorced/separated, and widowed. Type of insurance was grouped as public (Medicaid, Medicare, Indian Health Service, Veterans’ Affairs), private (fee for service, Health Maintenance Organization [HMO], Managed Care, and Preferred Provider Organization [PPO]), and self-pay/uninsured. The place/county of residence included whether an individual patient lived in an Appalachian (i.e., 52 counties) or non-Appalachian (i.e., 43 counties) region in Tennessee . Stages of cancer included localized, regional, and distant stages. The Surgical procedures patients received included the following. Local tumor destruction, laser ablation or cryosurgery, laser excision, wedge resection, lobectomy with mediastinal lymph node dissection, lobe or bilobectomy with chest wall, extended pneumonectomy, extended pneumonectomy plus pleura or diaphragm, resection of lung, not otherwise specified (NOS), and surgery, NOS. Patients were recategorized as yes (having received surgery for the invasive lung cancer) or no surgery procedure.
|
39752448_p6
|
39752448
|
Independent variables
| 4.059039 |
biomedical
|
Study
|
[
0.9979257583618164,
0.001543538412079215,
0.0005307307583279908
] |
[
0.999262273311615,
0.0003740012471098453,
0.0002674424322322011,
0.0000963442143984139
] |
en
| 0.999996 |
The research protocol was approved by the Tennessee Department of Health Institutional Review Board on February 1st, 2018 , with continuation approval on June 15, 2023 . The National Institutes of Health–Intramural Research Program IRB–Human Research Protections Program–Office of Human Subjects Research Protections determined that the research protocol for this study did not involve human subjects, and thus was exempt from IRB review . The anonymized data was received from TDH on March 21, 2018.
|
39752448_p7
|
39752448
|
Ethical approval
| 0.994912 |
other
|
Other
|
[
0.1257607489824295,
0.003330870298668742,
0.8709083795547485
] |
[
0.003933975473046303,
0.9951643943786621,
0.0004449684638530016,
0.000456620124168694
] |
en
| 0.999996 |
We conducted frequencies to examine the sample descriptive characteristics of the independent variables. The median and interquartile range (IQR) were used to assess the distribution of age at diagnosis and time to treatment initiation (see Table 1 ). Next, we performed bivariate ANOVA tests to assess the variations of time to treatment initiation within factors (see Table 1 ). The ANOVA tests were conducted to examine the within-independent group variation of time to treatment initiation. The ANOVA test was validated using the test of homogeneity of variance based on the median and robust test, including the Welch and Brown-Forsythe test. Given the skewed data of time to treatment initiation, we repeated the ANOVA analysis using non-parametric Kruskal Wallis to test the difference or variation in time to treatment initiation within factors or independent variables (see S1 Table ). Multivariable Cox-Proportional Hazards (Cox-PH) model analyses were conducted to examine the influence of the independent factors on time to treatment initiation. The Cox-PH model was conducted to examine the likelihood of invasive lung cancer patients delaying time to treatment initiation or receiving treatment beyond the median weeks after diagnosis in the combined or overall sample (see Table 2 ) and among stratified subgroups of Black and White patients (see Table 3 ). To conduct the Cox-PH regression model of the delayed time to treatment initiation, we used the time from diagnosis to definitive treatment. The event interest was defined as a delay in treatment initiation beyond the median time of 2.7 weeks. Time to treatment initiation was coded as “0” if treatment was initiated within ≤2.7 weeks and “1” if after >2.7 weeks. This allows us to estimate the hazard ratio to assess the prognosis, direction, and magnitude of association between the independent variables and delayed time to treatment initiation beyond 2.7 weeks. Additionally, we repeated the analyses of Tables 2 and 3 using the IQR weeks as the event in a supplemental analysis (see S2 and S3 Tables). We then validated the Cox-PH models by conducting and assessing the Cox-PH assumptions (see S1 Methods ). A non-statistical significance (i.e., p>0.05) of independent variables (or covariates) and the GLOBAL test indicated the Cox-PH assumption was satisfied or valid. Additionally, Schoenfeld residual tests were visualized to assess influential observations and further validate the Cox-PH assumption. If the Schoenfeld residual test of independent variables shows most data are within a 95% confidence interval and non-statistical significance (i.e., p>0.05), it is also an indication of a satisfied or valid Cox-PH assumption. The final data analyzed was based on complete data for each of the included variables. The results from the statistical analysis are reported using the adjusted hazard ratio (aHR) with a 95% confidence interval (CI) and statistical significance at a p< α = 0.05 level of significance. We used figures to display hazard functions and further assess the association between independent factors and time to treatment initiation, ranking the HRs in descending order of the independent factors to determine the magnitude and extent of risk on time to treatment initiation within the subsample of Black and White patients . We also compared the predicted estimated likelihood proportion of time to treatment initiation of invasive lung cancer beyond 2.7 weeks from the joint influence of all the independent variables among Black and White patients . All analyses were conducted using IBM SPSS Statistics 28 Premium and R 4.0.2.
|
39752448_p8
|
39752448
|
Statistical analysis
| 4.213652 |
biomedical
|
Study
|
[
0.9990636706352234,
0.0006802324787713587,
0.0002560011052992195
] |
[
0.9989847540855408,
0.00038116276846267283,
0.0005296167219057679,
0.0001044581294991076
] |
en
| 0.999998 |
In Table 1 , we examined the population characteristics and statistical variation in time to treatment initiation within independent groups. Among the total patients ( N = 42,970) diagnosed with invasive lung cancer, 48.7% initiated treatment within 2.7 median weeks, while 51.3% started treatment after 2.7 weeks. In the subgroup analysis, nearly equal percentages of White (48.7%) and Black (48.5%) patients began treatment within 2.7 median weeks of diagnosis. A similar pattern was observed among those who initiated treatment after the 2.7 median weeks, with 51.3% being White and 51.5% being Black patients. The median age at diagnosis for the total sample was 67 years old (Whites = 68; Blacks = 65). The sample consisted of 24,911 (55.6%) males (Whites = 55.6%; Blacks = 55.8%) and 19,095 (44.4%) females (Whites = 44.4%; Blacks = 44.2%). Majority of the patients did not receive surgical treatment (72.0%; n = 30,947 [Whites = 71.7%; Blacks = 74.6%]), mostly married/common law (55.7%; n = 23,913 [Whites = 57.9%; Blacks = 36.1%]), resided in Appalachian county (54.6%; n = 23,470 [Whites = 58.6%]), enrolled in public insurance (70.4%; n = 30,230 [Whites = 70.8%; Blacks = 66.6%]), and were diagnosed with invasive lung cancer at the distant stage (49.80%; n = 21,384 [Whites = 49.3%]). Additionally, most Black patients lived in non-Appalachian county (79.3%) and were diagnosed with regional-stage lung cancer (54.0%). In the overall sample, the bivariate analysis showed a statistically significant difference in time to treatment initiation with age (p<0.001), race (p<0.001), marital status (p = 0.020), county of residence (p = 0.003), health insurance type (p<0.001), surgical treatment (p<0.001), and cancer stage (p<0.001), which is consistent with the White patients subgroup. Among Black patients, a statistically significant differences in time to treatment initiation were observed based on sex (p<0.001), age (p = 0.005), cancer stage (p<0.001), and surgical treatment (p<0.001), but not for marital status, county of residence, and health insurance type (see Table 1 ). The analysis in Table 1 was repeated using a non-parametric Kruskal-Wallis test to examine the differences in the time to treatment initiation of invasive lung cancer across the levels of independent variables, revealing consistent results (see S1 Table ).
|
39752448_p9
|
39752448
|
Population characteristics and bivariate assessment of independent factors
| 4.147957 |
biomedical
|
Study
|
[
0.9982295632362366,
0.0014691106043756008,
0.00030127190984785557
] |
[
0.9991374015808105,
0.00036241216002963483,
0.0003764677094295621,
0.00012367739691399038
] |
en
| 0.999995 |
Table 2 assesses the influence of the independent factors on time to treatment initiation after 2.7 median weeks in the entire sample of invasive lung cancer patients in Tennessee. Patients aged <45 years were 14% more likely to start treatment after 2.7 weeks (aHR = 1.14; 95% CI = 1.02–1.27). Black patients were 18% less likely to experience delayed treatment (aHR = 0.82; 95% CI = 0.78–0.85) compared to White patients. Married patients had a 13% higher risk of beginning treatment after 2.7 weeks (aHR = 1.13; 95% CI = 1.08–1.19) compared to single patients. Patients with private insurance were 8% more likely to start treatment start after 2.7 weeks (aHR = 1.08; 95% CI = 1.01–1.16) than those who were self-pay/uninsured. Individuals diagnosed with lung cancer at localized (aHR = 0.66; 95% CI = 0.64–0.68) and regional (aHR = 0.82; 95% CI = 0.80–0.85) stages were less likely to start treatment late compared to those diagnosed at the distant stage. In S2 Table , we repeated the Table 2 analysis to examine the influence of the independent factors on time to treatment initiation beyond 4.8 IQR weeks. Overall, we observed similar trends and statistical significance for most factors.
|
39752448_p10
|
39752448
|
Multivariable analyses of independent factors and time to treatment initiation of invasive lung cancer in the general patient population
| 4.101938 |
biomedical
|
Study
|
[
0.9985756874084473,
0.0010958441998809576,
0.0003284260747022927
] |
[
0.9992913007736206,
0.00026244414038956165,
0.00035627378383651376,
0.00008988857734948397
] |
en
| 0.999995 |
Table 3 examines the disparities and the influence of independent factors on the time to treatment initiation of invasive lung cancer among Black and White patients. Black married patients (aHR = 1.16; 95% CI = 1.04–1.28) and those aged <45 years (aHR = 1.40; 95% CI = 1.00–1.94) were more likely to start treatment after 2.7 weeks. Similarly, White married individuals (aHR = 1.13; 95% CI = 1.07–1.18), those aged <45 (aHR = 1.11; 95% CI = 0.98–1.24), and those aged 65–74 (aHR = 1.05; 95% CI = 1.00–1.08) were at an increased risk of late treatment beyond 2.7 weeks. There was an increased risk of starting treatment late for White (aHR = 1.02; 95% CI = 0.99–1.05) and Black (aHR = 1.05; 95% CI = 0.94–1.16) patients residing in Appalachian counties; however, this did not have a statistically significant influence on time to treatment initiation after 2.7 weeks.
|
39752448_p11
|
39752448
|
Independent factors influencing time to treatment initiation of invasive lung cancer among White and Black patients
| 4.116791 |
biomedical
|
Study
|
[
0.9984415173530579,
0.0011304435320198536,
0.00042797025525942445
] |
[
0.9992496371269226,
0.00029173638904467225,
0.00037826114566996694,
0.00008043673733482137
] |
en
| 0.999997 |
Among both Black and White patients with private health insurance coverage, there was a higher risk of late treatment initiation; however, these findings ere not statistically significant. Notably, there was a significantly decreased risk of delayed treatment initiation for localized stage cancer among both Black patients (aHR = 0.68; 95% CI = 0.59–0.76) and White patients (aHR = 0.65; 95% CI = 0.63–0.68). For regional stage lung cancer, both Black (aHR = 0.78; 95% CI = 0.69–0.86) and White patients (aHR = 0.83; 95% CI = 0.79–0.85) were at a reduced risk of late treatment after 2.7 weeks. Surgical treatment had no statistically significant influence on time to treatment initiation after 2.7 weeks. However, Black patients who underwent surgical procedures had a greater decreased risk of late treatment compared to White patients (7% vs. 1%, respectively). In S3 Table , we repeated the Table 3 analysis to examine the disparities and influence of the independent factors on time to treatment initiation beyond the IQR weeks among White patients (4.7 weeks) and Black patients (5.5 weeks). We observed a similar magnitude, direction, and statistical significance in the associations for most independent factors, especially among White patients.
|
39752448_p12
|
39752448
|
Independent factors influencing time to treatment initiation of invasive lung cancer among White and Black patients
| 4.079014 |
biomedical
|
Study
|
[
0.9986274242401123,
0.0009540998726151884,
0.00041847306420095265
] |
[
0.9993927478790283,
0.0002581527514848858,
0.0002808067365549505,
0.0000682521058479324
] |
en
| 0.999995 |
Fig 2 ranks the independent factors in order of their impact on time to treatment initiation beyond 2.7 weeks, from the most to the least impactful. Among Black patients, those aged <45 years were at the highest risk of delaying treatment after 2.7 weeks, followed by widowed individuals . Among White patients, married individuals were ranked as most at risk of delaying treatment, followed by those aged <45 years .
|
39752448_p13
|
39752448
|
Ranking of independent factors influencing time to treatment initiation of invasive lung cancer among Black and White patients
| 2.55523 |
biomedical
|
Study
|
[
0.9910366535186768,
0.004816352855414152,
0.004146983381360769
] |
[
0.9919360876083374,
0.007102909963577986,
0.0006637582555413246,
0.00029728817753493786
] |
en
| 0.999997 |
Both Black and White patients with private insurance were ranked third highest for delaying treatment in their populations. Appalachian Black and White patients were ranked fifth and seventh, respectively, while publicly insured Black and White patients were ranked eighth and twelfth, respectively, for delayed treatment.
|
39752448_p14
|
39752448
|
Ranking of independent factors influencing time to treatment initiation of invasive lung cancer among Black and White patients
| 1.634163 |
other
|
Other
|
[
0.3919803202152252,
0.015092296525835991,
0.5929273366928101
] |
[
0.26040369272232056,
0.736051619052887,
0.0019651558250188828,
0.001579473027959466
] |
en
| 0.999995 |
Fig 3 displays the estimated predicted likelihood proportion of time to treatment initiation beyond 2.7 weeks after diagnosis among Black and White invasive lung cancer patients, respectively, as derived from the Cox-PHs models in Table 3 . The hazard curve for Black patients mostly lies above that of White patients, considering all analyzed factors (sociodemographic, health insurance, stage of cancer, and surgical treatment) that influence the time to treatment initiation after 2.7 weeks. This implies that Black patients are generally at a higher risk of delaying treatment for invasive lung cancer (i.e., they had a higher predicted proportion time at a given treatment delay time beyond 2.7 weeks after diagnosis) than their White counterparts. For instance, the predicted likelihood of receiving treatment 10 weeks post-diagnosis is approximately 20% for Black patients and 17% for White patients.
|
39752448_p15
|
39752448
|
Ranking of independent factors influencing time to treatment initiation of invasive lung cancer among Black and White patients
| 4.11484 |
biomedical
|
Study
|
[
0.9988994598388672,
0.0007726537878625095,
0.00032778774038888514
] |
[
0.9993007183074951,
0.0003073978878092021,
0.00031569943530485034,
0.00007622714474564418
] |
en
| 0.999995 |
The current study focuses on factors that influence the time to treatment initiation of lung cancer beyond 2.7 median weeks after diagnosis (i.e., treatment delay), considering the disparities between and within Black and White patients. We found that patients’ sociodemographic factors (age, race, marital status, and county of residence), health insurance status, and cancer stage (localized and regional) had a statistically significant influence on delayed time to treatment initiation after 2.7 weeks, while sex and surgical treatment did not. Our findings are consistent with past studies on lung cancer treatment delays . In the general Tennessee invasive lung cancer population, patients aged <45 years had a 14% increased risk, and those aged 65–74 years had a 4% increased risk of delayed treatment initiation beyond 2.7 weeks compared to those aged ≥75 years. However, in the stratified subgroup, only White patients aged 65–74 years showed a statistically significant increased risk, similar to what was observed in the general population sample.
|
39752448_p16
|
39752448
|
Discussion
| 4.094605 |
biomedical
|
Study
|
[
0.9988536834716797,
0.00082497822586447,
0.00032135454239323735
] |
[
0.9993313550949097,
0.00020791908900719136,
0.0003744007262866944,
0.0000862738597788848
] |
en
| 0.999997 |
Among Black patients, only those aged <45 years were statistically significantly associated with delayed treatment initiation beyond 2.7 weeks, showing a 40% increased risk compared to those aged ≥75 years. Additionally, compared to the general population, Black patients aged <45 years had a substantially higher risk difference of 26%, indicating that younger Black patients (<45 years) are at a greater risk of delaying treatment for invasive lung cancer. Contrary to our findings, a previous study by Samson et al. using the National Cancer Data Base reported that delayed treatment was associated with increasing age in their analysis of late surgery initiation and its impact on both short-term and long-term outcomes in early-stage non-small cell lung cancer . The reasons behind the substantial delays in treatment among young Black patients in our study in Tennessee remain unclear. Interestingly, a multisite community intervention assessing cancer prevention educational messages in predominantly Black areas of Georgia (Atlanta & Decatur) and Tennessee (Chattanooga & Nashville) showed little or no effect on knowledge or attitudes in these intervention cities . This adds to the uncertainty about why young Black patients face a higher risk of delaying treatment. Therefore, we recommend conducting an in-depth study to explore this issue further.
|
39752448_p17
|
39752448
|
Discussion
| 4.088635 |
biomedical
|
Study
|
[
0.998865008354187,
0.0007260299171321094,
0.0004090195579919964
] |
[
0.9991545677185059,
0.0002866719150915742,
0.0004851164994761348,
0.00007357367576332763
] |
en
| 0.999997 |
In the general (unstratified) population of Tennessee invasive lung cancer patients, Black patients were 18% less likely than White patients to delay treatment beyond 2.7 weeks. However, the stratified subpopulation analysis revealed that Black patients were at greater risk of late treatment compared to White patients across almost all independent factors considered. This finding is consistent with the predicted likelihood delayed treatment initiation beyond 2.7 weeks, which considered all the independent variables investigated. This underscores the critical importance of conducting disaggregated racial subgroup analysis in lung cancer treatment, rather than relying solely on aggregated general population data. Additionally, combining racial and general population findings for health policies and interventions may obscure the differential subgroup and racial differences necessary for effective and efficient policy decision-making. For instance, Holmes & Cohen used a nationally representative sample from the National Cancer Database, 2008–2013, and found that the median time to treatment initiation for non-small cell lung cancer was 8.2 days longer for African American patients compared to White patients . Similarly, Cushman et al. , which utilized the same dataset from 2004 to 2013, found that the median time to treatment initiation for African Americans and Hispanics was longer compared to other racial group . Braithwaite et al. reported that Black communities in the U.S. faced more inadequate health care, low health education, and a shorter life expectancy for lung cancer than their White counterparts . Addressing these disparities in Black communities could help reduce the risk of delayed treatment for invasive lung cancer. Additionally, the differences between aggregated population and subgroup analyses highlight the importance of examining disaggregated data to inform, targeted policy interventions, particularly with racial considerations. While general population analysis provides an overall view of lung cancer treatment initiation times, subgroup analysis reveals race-specific perspectives and disparities, enabling more tailored and effective policy interventions. Consistent with our findings, Roshini et al. highlighted the importance of disaggregating data to identify potential disparities in risk and protective factors, which can lead to better-informed, targeted interventions . Our results further reinforce the need for additional research into subgroup disaggregation in population-based public health studies.
|
39752448_p18
|
39752448
|
Discussion
| 4.204332 |
biomedical
|
Study
|
[
0.9990257024765015,
0.0005226628272794187,
0.0004515918844845146
] |
[
0.99793541431427,
0.00029204951715655625,
0.0016897398745641112,
0.00008281493501272053
] |
en
| 0.999997 |
Married invasive lung cancer patients were 13% more likely to start treatment after 2.7 weeks compared to single or never-married patients in the general population. In the stratified racial subgroup analyses, married White patients had a 13% increased risk of delayed treatment initiation beyond 2.7 weeks, while married Black patients had a 16% increased risk compared to their single or never-married counterparts. Notably, the risk increase for married White patients was consistent with that observed in the general population. However, Black patients experienced a 3% higher risk of delayed treatment initiation compared to the general population. In contrast, Chen et al. found that marriage was associated with improved cancer-specific survival among patients who received early diagnosis and treatment with surgery . This underscores the importance of early treatment for married individuals with invasive lung cancer. Although this study did not investigate the impact of financial stress or marital burden on treatment initiation, it is plausible that financial factors may contribute to treatment delays in Tennessee. Future research should explore this potential effect. Additionally, providing financial assistance to married patients could help reduce delays in treatment initiation and improve survival outcomes.
|
39752448_p19
|
39752448
|
Discussion
| 4.086666 |
biomedical
|
Study
|
[
0.9989483952522278,
0.0006593329017050564,
0.0003922595642507076
] |
[
0.9967969059944153,
0.00032138967071659863,
0.0027914855163544416,
0.00009026029874803498
] |
en
| 0.999996 |
The county of residence (Appalachian or non-Appalachian) of invasive lung cancer patients in Tennessee had a significant influence on the time to treatment initiation. There was a statistically significant variation in the time to treatment initiation between the Appalachian and non-Appalachian counties in the general population sample and among White patients, but not among Black patients. Although no significant association was found between time to treatment initiation and the type of county of residence in Tennessee, patients in the Appalachian counties were 2% more likely to delay treatment or begin treatment after 2.7 weeks than those in non-Appalachian counties. Additionally, in the Appalachian county, White patients had a lower increased risk of late treatment compared to Black patients (2% vs. 5%). Research has shown that the Appalachian region of Tennessee experiences significant healthcare disparities, with the highest incidence of cancer and cancer mortality rates . The variance in time to treatment between the two counties could be related to a lack of adequate cancer care resources, as observed in other Appalachian regions . A Study by Atkins et al. ) revealed that lung cancer mortality increased with rurality, with rural patients diagnosed with non-small cell lung cancer undergoing fewer surgeries, leading to shorter median survival compared to urban patients . These findings highlight the influence of place, particularly in rural communities, on lung cancer treatment. There is a need for tailored educational and early detection programs targeting at-risk populations to ensure equitable access to cancer care resources, as factors such as distance to care centers may impact treatment timing. In addition, interventions such as local community resources and telemedicine have yielded success in improving rural cancer care [ 46 – 48 ], and could be adopted in Tennessee. Furthermore, streamlining care coordination efforts may aid in addressing the timeliness of treatment for patients, especially in rural and minority communities who may have difficulties navigating the health systems and care processes.
|
39752448_p20
|
39752448
|
Discussion
| 4.165255 |
biomedical
|
Study
|
[
0.9986883997917175,
0.0008751426357775927,
0.0004364956694189459
] |
[
0.9980181455612183,
0.0003239919024053961,
0.0015597030287608504,
0.00009808927279664204
] |
en
| 0.999997 |
Health insurance status or coverage was associated with the time to treatment initiation of lung cancer. Patients with private insurance were 8% more likely to delay treatment for invasive lung cancer beyond 2.7 weeks than self-pay/uninsured patients in the general sample. This trend was also observed among White and Black patient subgroups, although not statistically significant. Conversely, patients with public insurance had a 6% decreased risk of delaying treatment compared to those with self-pay/uninsured status. This suggests that individuals with private insurance are more likely to delay treatment, possibly due to the cost of deductibles associated with private insurance . Future studies should investigate this further, particularly assessing out-of-pocket costs and streamline approval processes for tests and procedures necessary for cancer treatment.
|
39752448_p21
|
39752448
|
Discussion
| 4.01665 |
biomedical
|
Study
|
[
0.9980383515357971,
0.0010891463607549667,
0.0008726214873604476
] |
[
0.999018669128418,
0.00048227811930701137,
0.0004324731999076903,
0.00006658205529674888
] |
en
| 0.999998 |
Patients diagnosed with localized and regional stages of invasive lung cancer experienced a decreased risk of delaying treatment after 2.7 weeks (i.e., 34% and 18%, respectively) compared with those diagnosed at the distant stage. This finding may reflect the clinical urgency associated with distant stage or metastatic cancer, as observed in a systematic review by Hall et al. . While White patients with localized stage invasive lung cancer had a decreased likelihood of delaying treatment compared to their Black counterparts (35% vs. 32%), Black patients had a decreased risk of late treatment for regional stage invasive lung cancer compared to White patients (17% vs. 22%). These disparities are concerning given that approximately 75% of lung cancers are diagnosed at the advanced or distant stage with a poor survival rate . Despite significant improvements have been made in the oncological management of distant-stage lung cancer in recent years, more efforts are needed to facilitate early treatment.
|
39752448_p22
|
39752448
|
Discussion
| 4.000611 |
biomedical
|
Study
|
[
0.998737633228302,
0.0009182392968796194,
0.00034415017580613494
] |
[
0.895790159702301,
0.0012961359461769462,
0.102509044110775,
0.00040465316851623356
] |
en
| 0.999997 |
This study is not without limitations. The data used is cross-sectional data, which presents some weaknesses in terms of making strong and accurate conclusions or decisions. Nonetheless, this research outlines tremendous findings about the time to treatment initiation of invasive lung cancer in Tennessee and provides the need for prospective cohort studies to enhance further understanding. We were also limited by some administrative variables such as SES data (e.g., individual-level educational attainment, income), healthcare access, and other lung cancer treatments received by patients beside surgery. Additionally, the data did not specify the type of lung cancer patients were diagnosed, which can be either small cell lung cancer or non-small cell lung cancer, each requiring different treatment modalities. Also, delay from diagnosis to treatment of lung cancer may occur in different ways, including delays in the first appointment of with a general practitioner, referral delays, and delays from referral to the first visit of with a specialist, as well as delays in treatment initiation. Unfortunately, we lacked specific data on the types of delays encountered by patients. Examination of specific types of treatment delay in future studies can help design a more specific tailored intervention to reduce the disparities in invasive lung cancer treatment initiation in Tennessee. Importantly, our study examined the time from diagnosis to definitive treatment of invasive lung cancer, focusing on periods beyond the median time of 2.7 weeks. Previous studies have reported this timeframe as a treatment delay for lung cancer [ 10 – 12 ]. However, this study did not examine the clinical implications of not receiving treatment within the median time of 2.7 weeks after diagnosis. Further, there are conflicting findings regarding whether the 2.7 weeks treatment delay time is detrimental to patient survival [ 12 – 14 ]. Despite these limitations, our findings emphasize the need for further research to investigate whether a 2.7 weeks delay in treatment initiation negatively affects the survival of invasive lung cancer patients in Tennessee.
|
39752448_p23
|
39752448
|
Limitations
| 4.122025 |
biomedical
|
Study
|
[
0.999005138874054,
0.0006982235354371369,
0.0002966150641441345
] |
[
0.9990429282188416,
0.00019441026961430907,
0.000658891221974045,
0.00010379122977610677
] |
en
| 0.999996 |
The varied factors impacting diagnosis and the complexities associated with lung cancer treatment underscore the need to understand how time to treatment affects survival outcomes, especially given the diversity in care resources across Appalachian counties in Tennessee. Invasive lung cancer patients in Tennessee who experienced delays in treatment initiation were more likely to be Blacks, <45 years old, married, and have private insurance. The findings from this study can aid clinicians and care coordination teams in identifying high-risk populations and developing comprehensive, tailored care plans based on patient demographics and rural-urban residency. Finally, this study highlights the need for improved cancer care resources in Black communities in Tennessee to ensure timely treatment of invasive lung cancer and equity of care for all cancer patients.
|
39752448_p24
|
39752448
|
Conclusions
| 3.996134 |
biomedical
|
Study
|
[
0.9980732202529907,
0.0013207242591306567,
0.0006060299929231405
] |
[
0.9985764026641846,
0.0008860016823746264,
0.00043246394488960505,
0.00010511209984542802
] |
en
| 0.999997 |
Childhood cancer remains a significant public health concern worldwide, despite substantial progress in its management over the last 5 decades. Advances in diagnostic tests, risk stratification, and therapeutic interventions have improved survival of many pediatric malignancies . However, the incidence of specific cancer types and disparities in outcomes among different population groups persist . Additionally, long-term sequelae affect the quality of life of cancer survivors, emphasizing the need for further research to minimize treatment-related toxicities .
|
39752445_p0
|
39752445
|
Introduction
| 3.883487 |
biomedical
|
Review
|
[
0.9950037598609924,
0.0029645608738064766,
0.002031767275184393
] |
[
0.01077865157276392,
0.010109140537679195,
0.9786040782928467,
0.0005080488626845181
] |
en
| 0.999997 |
Assessing trends in childhood cancer incidence, survival, and mortality is crucial to understanding the effectiveness of current interventions and identifying areas where additional efforts are needed. Monitoring these trends can also help to identify potential risk factors, allocate healthcare resources, and guide public health policies. Furthermore, understanding the disparities in outcomes across different populations will enable us to better address the inequity in cancer care and in the implementation of targeted interventions.
|
39752445_p1
|
39752445
|
Introduction
| 3.852938 |
biomedical
|
Review
|
[
0.9974812865257263,
0.0011752791469916701,
0.0013434377033263445
] |
[
0.09166529029607773,
0.23363028466701508,
0.673565685749054,
0.001138751395046711
] |
en
| 0.999997 |
Over the last fifty years, the landscape of pediatric oncology has undergone significant evolution, marked by the introduction and refinement of chemotherapy, spearheaded by collaborative groups across North America and Europe. These efforts have led to the development of more effective treatment regimens that optimize the use of established drugs, resulting in markedly improved outcomes for almost all types of pediatric cancer. Enhancements in supportive care have rendered intensive treatments more manageable. Advances in stem cell transplantation techniques have become pivotal in rescuing patients who do not respond to initial treatments. Diagnostic progress, including molecular stratification, detection of minimal residual disease, and sophisticated genetic profiling, has refined therapeutic approaches, allowing for more tailored and effective treatments. Improvements in imaging technologies, such as advanced CT scanners and the advent of nuclear scanning, have significantly improved the detection of metastatic disease. Surgical and radiation oncology techniques have also seen substantial advancements, improving the precision and efficacy of tumor resection and control. The introduction of targeted therapies and immunotherapies has opened new avenues for treating specific patient subsets, including those with acute lymphoblastic leukemia (ALL), high-risk neuroblastoma, relapsed Hodgkin lymphoma, and others, marking a shift towards precision medicine. The integration of multidisciplinary care teams has further optimized treatment outcomes and patient care, emphasizing the importance of a holistic approach in the management of pediatric cancers .
|
39752445_p2
|
39752445
|
Introduction
| 4.212173 |
biomedical
|
Review
|
[
0.9490859508514404,
0.03010374866425991,
0.020810291171073914
] |
[
0.0025453141424804926,
0.008329436182975769,
0.9873680472373962,
0.0017571933567523956
] |
en
| 0.999997 |
The Surveillance, Epidemiology, and End Results (SEER) registry, a comprehensive source of cancer statistics in the United States, provides a unique opportunity to examine the changing trends and mortality rates in childhood cancer over an extended period . By analyzing data from the SEER registry, we can assess the impact of advancements in diagnostics and therapeutics on the epidemiology and outcomes of pediatric malignancies.
|
39752445_p3
|
39752445
|
Introduction
| 3.551787 |
biomedical
|
Study
|
[
0.9979912042617798,
0.0003812615177594125,
0.0016274768859148026
] |
[
0.749774694442749,
0.2290738821029663,
0.020604010671377182,
0.000547428207937628
] |
en
| 0.999996 |
Subsets and Splits
SQL Console for rntc/test-pp-aa
The query retrieves a sample of documents that are clinical cases with an educational score above 3, providing limited analytical value.
Clinical Cases Sample
Returns a sample of 100 clinical case documents, providing a basic overview of the document type's content.