PROFUSEme: PROstate Cancer Biochemical Recurrence Prediction via FUSEd Multi-modal Embeddings
This work addresses early prediction of recurrence for prostate cancer patients to improve clinical decision-making, but it is incremental as it builds on existing multi-modal fusion methods.
The paper tackles the problem of predicting biochemical recurrence in prostate cancer patients after radical prostatectomy by fusing clinical, radiology, and pathology data, achieving a mean C-index of 0.861 on internal validation and 0.7107 on an external challenge.
Almost 30% of prostate cancer (PCa) patients undergoing radical prostatectomy (RP) experience biochemical recurrence (BCR), characterized by increased prostate specific antigen (PSA) and associated with increased mortality. Accurate early prediction of BCR, at the time of RP, would contribute to prompt adaptive clinical decision-making and improved patient outcomes. In this work, we propose prostate cancer BCR prediction via fused multi-modal embeddings (PROFUSEme), which learns cross-modal interactions of clinical, radiology, and pathology data, following an intermediate fusion configuration in combination with Cox Proportional Hazard regressors. Quantitative evaluation of our proposed approach reveals superior performance, when compared with late fusion configurations, yielding a mean C-index of 0.861 ($σ=0.112$) on the internal 5-fold nested cross-validation framework, and a C-index of 0.7107 on the hold out data of CHIMERA 2025 challenge validation leaderboard.