Work

Bayesian Framework to Augment Tumor Board Decision Making

Year: 2021

Type: article

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.

Source: JCO Clinical Cancer Informatics

DOI: 10.1200/cci.20.00085

Authors: Stefano Pasetto, Robert A. Gatenby, Heiko Enderling

Institution: Moffitt Cancer Center

Language: English

Topic: Mathematical Biology Tumor Growth

Subfield: Modeling and Simulation

Field: Mathematics

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: hybrid

Funder: National Cancer Institute