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.