BayesiaLab
Bayesian Networks and Precision Oncology Application to Prostate Cancer

Bayesian Networks and Precision Oncology: Application to Prostate Cancer

Presented at the 10th Annual BayesiaLab Conference on Tuesday, October 25, 2022.

Abstract

Comprehensive tools to drive decision-making are new challenges for the revolution of precision and preventive medicine. These tools could optimize management or assess better the risk of having or developing diseases and help practitioners make decisions to perform deep (invasive or costly) diagnosis procedures. So, Bayesian statistical methods and modeling techniques provide a powerful approach to integrate knowledge and new markers to refine the probability of outcome and decision-making in clinical practice. Bayesian networks through BayesiaLab offer the opportunity to search for better, more informative factors or a combination of these factors to enhance endpoint prediction. For example, we present some applications for risk prediction and personalized management in prostate cancer. In this way, Bayesian approaches, using BayesiaLab, have been shown a powerful strategy to explore, validate, and translate useful multifactorial predictors for precision medicine to clinical practice.

Presentation Video

Presentation Slides

About the Presenter

Professor Olivier Cussenot, MD, Ph.D., is a full professor at Sorbonne University. He is qualified as a urological surgeon, oncologist, and geneticist and has a minimum of two decades of experience in molecular/translational prostate and urological cancer research.

As head of the department, he managed a research unit on predictive oncology and personalized prevention strategies. He was also the principal investigator of many national and European research programs on urological and prostate cancers. His research programs are mainly on the clinicopathological and molecular heterogeneity of cancers related to the germline genetic background (family history or ancestry) and the interaction with the environment. He led the French Institute of Cancer (INCa) and the French Cancer Research League, the national programs on prostate cancer genomics (French part of the ICGC and molecular tumor ID). He also led the first national program, which links genetic markers to the national health database to model the different life pathways according to the different prostate cancer management, co-morbidities, and individual genetic or mesological factors.

He is the author of more than 480 scientific articles referenced as “Cussenot O.” They are available on the PubMed data search engine. He wrote more than 60 didactic chapters in books and provides translational seminars on genomics and artificial intelligence /decision-making in urologic oncology.


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