TY - JOUR
T1 - Bayesian framework to augment tumor board decision making
AU - Pasetto, Stefano
AU - Gatenby, Robert A.
AU - Enderling, Heiko
N1 - Publisher Copyright:
© 2021 American Society of Clinical Oncology. All rights reserved.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
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U2 - 10.1200/CCI.20.00085
DO - 10.1200/CCI.20.00085
M3 - Article
C2 - 33974446
AN - SCOPUS:85105724753
SN - 2473-4276
VL - 5
SP - 508
EP - 517
JO - JCO Clinical Cancer Informatics
JF - JCO Clinical Cancer Informatics
ER -