Three artificial intelligence data challenges based on CT and MRI

International audiencePurpose: The second edition of the artificial intelligence (AI) data challenge was organized by the French Society of Radiology with the aim to: (i), work on relevant public health issues; (ii), build large, mul-ticentre, high quality databases; and (iii), include three-dimensional (3D) information and prognostic questions. Materials and methods: Relevant clinical questions were proposed by French subspecialty colleges of radiology. Their feasibility was assessed by experts in the field of AI. A dedicated platform was set up for inclusion centers to safely upload their anonymized examinations in compliance with general data protection regulation. The quality of the database was checked by experts weekly with annotations performed by radiologists. Multidisciplinary teams competed between September 11 th and October 13 th 2019. Results: Three questions were selected using different imaging and evaluation modalities, including: pulmonary nodule detection and classification from 3D computed tomography (CT), prediction of expanded disability status scale in multiple sclerosis using 3D magnetic resonance imaging (MRI) and segmentation of muscular surface for sarcopenia estimation from two-dimensional CT. A total of 4347 examinations were gathered of which only 6% were excluded. Three independent databases from 24 individual centers were created. A total of 143 participants were split into 20 multidisciplinary teams. Conclusion: Three data challenges with over 1200 general data protection regulation compliant CT or MRI examinations each were organized. Future challenges should be made with more complex situations combining histopathological or genetic information to resemble real life situations faced by radiologists in routine practice

Circulating tumour cells as a potential biomarker for lung cancer screening: a prospective cohort study

International audienceBackground Lung cancer screening with low-dose chest CT (LDCT) reduces the mortality of eligible individuals. Bloodsignatures might act as a standalone screening tool, refine the selection of patients at risk, or help to classifyundetermined nodules detected on LDCT. We previously showed that circulating tumour cells (CTCs) could bedetected, using the isolation by size of epithelial tumour cell technique (ISET), long before the cancer was diagnosedradiologically. We aimed to test whether CTCs could be used as a biomarker for lung cancer screening.Methods We did a prospective, multicentre, cohort study in 21 French university centres. Participants had to beeligible for lung cancer screening as per National Lung Screening Trial criteria and have chronic obstructivepulmonary disease with a fixed airflow limitation defined as post-bronchodilator FEV1/FVC ratio of less than 0∙7.Any cancer, other than basocellular skin carcinomas, detected within the previous 5 years was the main exclusioncriterion. Participants had three screening rounds at 1-year intervals (T0 [baseline], T1, and T2), which involved LDCT,clinical examination, and a blood test for CTCs detection. Participants and investigators were masked to the results ofCTC detection, and cytopathologists were masked to clinical and radiological findings. Our primary objective was totest the diagnostic performance of CTC detection using the ISET technique in lung cancer screening, compared withcancers diagnosed by final pathology, or follow up if pathology was unavailable as the gold standard. This studyis registered with ClinicalTrials.gov identifier, number NCT02500693.Findings Between Oct 30, 2015, and Feb 2, 2017, we enrolled 614 participants, predominantly men (437 [71%]), aged65∙1 years (SD 6∙5), and heavy smokers (52∙7 pack-years [SD 21∙5]). 81 (13%) participants dropped out betweenbaseline and T1, and 56 (11%) did between T1 and T2. Nodules were detected on 178 (29%) of 614 baseline LDCTs.19 participants (3%) were diagnosed with a prevalent lung cancer at T0 and 19 were diagnosed with incident lungcancer (15 (3%) of 533 at T1 and four (1%) of 477 at T2). Extrapulmonary cancers were diagnosed in 27 (4%) ofparticipants. Overall 28 (2%) of 1187 blood samples were not analysable. At baseline, the sensitivity of CTC detectionfor lung cancer detection was 26∙3% (95% CI 11∙8–48∙8). ISET was unable to predict lung cancer or extrapulmonarycancer development