Bayesian Framework to Augment Tumor Board Decision Making
| Autor: | Robert A. Gatenby, Stefano Pasetto, Heiko Enderling |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
0301 basic medicine
Oncologists medicine.medical_specialty Extramural Computer science Decision Making MEDLINE Cancer Bayes Theorem General Medicine medicine.disease Medical Oncology 03 medical and health sciences 030104 developmental biology 0302 clinical medicine Multidisciplinary approach 030220 oncology & carcinogenesis Neoplasms medicine Tumor board Humans Medical physics Bayesian framework Augment |
| Zdroj: | JCO clinical cancer informatics. 5 |
| ISSN: | 2473-4276 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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