Multimodal prediction of metastatic relapse using federated deep learning in soft-tissue sarcoma with a complex genomic profile.

ABSTRACT: Soft Tissue sarcomas (STS) are a group of heterogeneous and complex diseases where being able to predict the appearance of metastases is key to inform clinical decisions, especially the prescription of adjuvant chemotherapy. We developed SarcNet: a multimodal deep-learning algorithm based on histological whole-slides and clinical variables to predict metastatic relapse in patients affected with limbs and trunk wall STS. Two independent series were investigated simultaneously in a privacy-enhanced multicentric setup using Federated Learning: Centre Léon Bérard (n = 221) and Institut Bergonié (n = 390). We then collected two additional validation cohorts from Centre Léon Bérard (n = 93) and Institut Bergonié (n = 124) to validate the performance of the trained algorithm. Evaluated in cross-v

SUBMITTER: Maussion C 

PROVIDER: S-EPMC12540783 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature