2020/06/30 by Yassine El Ouahidi, Matis Feller, Ouahidi, Yassine El +5
Biochemistry, Genetics and Molecular Biology · Computer Science · #AI in cancer detection #FOS: Biological sciences #FOS: Computer and information sciences #Gene expression and cancer classification #Machine Learning (cs.LG) #Machine Learning in Bioinformatics #Quantitative Methods (q-bio.QM) #Tissues and Organs (q-bio.TO)
paper · pdf · doi:10.48550/arxiv.2007.00135
openalex publication_date 2020/06/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In this paper, we introduce a novel and interpretable methodology to cluster subjects suffering from cancer, based on features extracted from their biopsies. Contrary to existing approaches, we propose here to capture complex patterns in the repartitions of their cells using histograms, and compare subjects on the basis of these repartitions. We describe here our complete workflow, including creation of the database, cells segmentation and phenotyping, computation of complex features, choice of a distance function between features, clustering between subjects using that distance, and survival analysis of obtained clusters. We illustrate our approach on a database of hematoxylin and eosin (H&E)-stained tissues of subjects suffering from Stage I lung adenocarcinoma, where our results match existing knowledge in prognosis estimation with high confidence.