Lesion graph neural networks for 2-year progression free survival classification of Diffuse Large B-Cell Lymphoma patients

International audience; Survival analysis of DLBCL patients requires the interpretation of PET images characterised by multiple small lesions. Current machine-learning approaches addressing similar problems consider as input the cropped image of a single lesion or the whole volume. In this paper, we incorporate the information of all lesions by modeling their joint survival analysis with a graph learning approach. We propose a compact graph representation of the segmented lesions enriched by radiomics features and edge weights. The representation is fed to a graph attention network to predict the 2-year Progression-Free Survival of a DLBCL patient, formalised as a graph classification problem. Experimental results on a clinical prospective database with 583 patients show that our method improves over three baseline fusion approaches.