def aoec_lung_concepts2labels(report_concepts):
	"""
	Convert the concepts extracted from lung reports to the set of pre-defined labels used for classification

	Params:
		report_concepts (dict(list)): the dict containing for each lung report the extracted concepts

	Returns: a dict containing for each lung report the set of pre-defined labels where 0 = absence and 1 = presence
	"""

	report_labels = dict()
	# loop over reports
	for rid, rconcepts in report_concepts.items():
		# assign pre-defined set of labels to current report
		report_labels[rid] = {
			'cancer_scc': 0, 'cancer_nscc_adeno': 0, 'cancer_nscc_squamous': 0, 'cancer_nscc_large': 0, 'no_cancer': 0}
		# make diagnosis section a set
		diagnosis = set([concept[1].lower() for concept in rconcepts['Diagnosis']])
		# update pre-defined labels w/ 1 in case of label presence
		for d in diagnosis:
			if 'small cell lung carcinoma' == d:
				report_labels[rid]['cancer_scc'] = 1
			if 'lung adenocarcinoma' == d or 'clear cell adenocarcinoma' == d or 'metastatic neoplasm' == d:
				report_labels[rid]['cancer_nscc_adeno'] = 1
			if 'non-small cell squamous lung carcinoma' == d:
				report_labels[rid]['cancer_nscc_squamous'] = 1
			if 'lung large cell carcinoma' == d:
				report_labels[rid]['cancer_nscc_large'] = 1
		# update when no label has been set to 1
		if sum(report_labels[rid].values()) == 0:
			report_labels[rid]['no_cancer'] = 1
	return report_labels