Bayesian Framework to Augment Tumor Board Decision Making. (2021)

• Record Type: Journal Article
• Authors: Pasetto, Stefano; Gatenby, Robert A.; Enderling, Heiko
• Is Part Of: JCO Clinical Cancer Informatics. Volume 5(2021)
• Publication Date: 2021
• Subjects: 616.994
• DOI: 10.1200/CCI.20.00085
• Languages: English
• ISSNs: 2473-4276

Abstract

Abstract : PURPOSE: Ideally, specific treatment for a cancer patient is decided by a multidisciplinary tumor board, integrating prior clinical experience, published data, and patient-specific factors to develop a consensus on an optimal therapeutic strategy. However, many oncologists lack access to a tumor board, and many patients have incomplete data descriptions so that tumor boards must act on imprecise criteria. We propose these limitations to be addressed through a flexible but rigorous mathematical tool that can define the probability of success of given therapies and be made readily available to the oncology community. METHODS: We present a Bayesian approach to tumor forecasting using a multimodel framework to predict patient-specific response to different targeted therapies even when historical data are incomplete. RESULTS: We demonstrate that the Bayesian decision theory's integrative power permits the simultaneous assessment of a range of therapeutic options. CONCLUSION: This methodology proposed, built upon a robust and well-established mathematical framework, can play a crucial role in supporting patient-specific clinical decisions by individual oncologists and multispecialty tumor boards.