Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Machine Intelligence-Driven Classification of Cancer Patients-Derived
  Extracellular Vesicles using Fluorescence Correlation Spectroscopy: Results
  from a Pilot Study   Patient-derived extracellular vesicles (EVs) that contains a complex
biological cargo is a valuable source of liquid biopsy diagnostics to aid in
early detection, cancer screening, and precision nanotherapeutics. In this
study, we predicted that coupling cancer patient blood-derived EVs to
time-resolved spectroscopy and artificial intelligence (AI) could provide a
robust cancer screening and follow-up tools. Methods: Fluorescence correlation
spectroscopy (FCS) measurements were performed on 24 blood samples-derived EVs.
Blood samples were obtained from 15 cancer patients (presenting 5 different
types of cancers), and 9 healthy controls (including patients with benign
lesions). The obtained FCS autocorrelation spectra were processed into power
spectra using the Fast-Fourier Transform algorithm and subjected to various
machine learning algorithms to distinguish cancer spectra from healthy control
spectra. Results and Applications: The performance of AdaBoost Random Forest
(RF) classifier, support vector machine, and multilayer perceptron, were tested
on selected frequencies in the N=118 power spectra. The RF classifier exhibited
a 90% classification accuracy and high sensitivity and specificity in
distinguishing the FCS power spectra of cancer patients from those of healthy
controls. Further, an image convolutional neural network (CNN), ResNet network,
and a quantum CNN were assessed on the power spectral images as additional
validation tools. All image-based CNNs exhibited a nearly equal classification
performance with an accuracy of roughly 82% and reasonably high sensitivity and
specificity scores. Our pilot study demonstrates that AI-algorithms coupled to
time-resolved FCS power spectra can accurately and differentially classify the
complex patient-derived EVs from different cancer samples of distinct tissue
subtypes.

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The categories of this paper are: hep-ph
2
The categories of this paper are: math
9
The categories of this paper are: nlin
16
The categories of this paper are: stat
37
The categories of this paper are: supr-con
31
The categories of this paper are: ao-sci
32
The categories of this paper are: atom-ph
3
The categories of this paper are: physics
17
The categories of this paper are: q-fin
29
The categories of this paper are: funct-an
26
The categories of this paper are: bayes-an
28
The categories of this paper are: alg-geom
11
The categories of this paper are: hep-lat
13
The categories of this paper are: quant-ph
35
The categories of this paper are: mtrl-th
14
The categories of this paper are: math-ph