An Approach for Clustering Subjects According to Similarities in Cell Distributions within Biopsies
This work addresses prognosis estimation for Stage I lung adenocarcinoma patients, but it is incremental as it builds on existing clustering methods with a new feature extraction approach.
The paper tackles the problem of clustering cancer patients based on cell distribution patterns in biopsies, using histograms to capture complex patterns and achieving results that match existing prognosis knowledge with high confidence.
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.