Article : Multi-task Bayesian Model Combining FDG-PET/CT Imaging and Clinical Data for Interpretable High-Grade Prostate Cancer Prognosis

arXiv

Date

2024-06-20

Auteurs

  • 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.

Résumé

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.

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