Paper: Multi-task Bayesian Model Combining FDG-PET/CT Imaging and Clinical Data for Interpretable High-Grade Prostate Cancer Prognosis
Date
2024-06-20
Authors
- Maxence Larose1
- Louis Archambault1,2
- Nawar Touma2
- Raphael Brodeur1,2
- Félix Desroches1,2
- Nicolas Raymond3
- Daphnée Bédard-Tremblay2
- Danahé LeBlanc1,2
- Fatemeh Rasekh2
- Hélène Hovington2
- Bertrand Neveu2
- Martin Vallières3
- Frédéric Pouliot2
1 Département de physique, de génie physique et d’optique, et Centre de recherche sur le cancer, Université Laval, Québec (QC), Canada.
2 CHU de Québec – Université Laval et CRCHU de Québec, Québec (QC), Canada.
3 Department of Computer Science, Université de Sherbrooke, Sherbrooke (QC), Canada.
Abstract
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 clinico-pathological data to predict poor outcome patients that need treatment intensification with loco-regional or systemic adjuvant therapy for high-risk PCa.