  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.

Quantitative Methods;Machine Learning;Tissues and Organs