Multi-task Bayesian model combining FDG-PET/CT imaging and clinical data for interpretable high-grade prostate cancer prognosis.

We propose a fully automatic multi-task Bayesian model, named Bayesian Sequential Network (BSN), for predicting high-grade (Gleason  ≥  8) prostate cancer (PCa) prognosis using pre-prostatectomy FDG-PET/CT images and clinical data. BSN performs one classification task and five survival tasks: predicting lymph node invasion (LNI), biochemical recurrence-free survival (BCR-FS), metastasis-free survival, definitive androgen deprivation therapy-free survival, castration-resistant PCa-free survival, and PCa-specific survival (PCSS). Experiments are conducted using a dataset of 295 patients. BSN outperforms widely used nomograms on all tasks except PCSS, leveraging multi-task learning and imaging data. BSN also provides automated prostate segmentation, uncertainty quantification, personalized feature-based explanations, and introduces dynamic predictions, a novel approach that relies on short-term outcomes to refine long-term prognosis. Overall, BSN shows great promise in its ability to exploit imaging and clinicopathological data to predict poor outcome patients that need treatment intensification with loco-regional or systemic adjuvant therapy for high-risk PCa.

Scientific reports. 2024 Nov 06*** epublish ***

Maxence Larose, Louis Archambault, Nawar Touma, Raphaël Brodeur, Félix Desroches, Nicolas Raymond, Daphnée Bédard-Tremblay, Danahé LeBlanc, Fatemeh Rasekh, Hélène Hovington, Bertrand Neveu, Martin Vallières, Frédéric Pouliot

Département de physique, de génie physique et d'optique, et Centre de recherche sur le cancer, Université Laval, Québec, QC, Canada. ., Département de physique, de génie physique et d'optique, et Centre de recherche sur le cancer, Université Laval, Québec, QC, Canada. ., CHU de Québec - Université Laval et CRCHU de Québec, Québec, QC, Canada., Département de physique, de génie physique et d'optique, et Centre de recherche sur le cancer, Université Laval, Québec, QC, Canada., Department of Computer Science, Université de Sherbrooke, Sherbrooke, QC, Canada., Department of Computer Science, Université de Sherbrooke, Sherbrooke, QC, Canada. ., CHU de Québec - Université Laval et CRCHU de Québec, Québec, QC, Canada. .