Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer & Research Institute, Tampa, FL.
Department of Radiology, H. Lee Moffitt Cancer & Research Institute, Tampa, FL.
JCO Clin Cancer Inform. 2021 May;5:508-517. doi: 10.1200/CCI.20.00085.
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.
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.
We demonstrate that the Bayesian decision theory's integrative power permits the simultaneous assessment of a range of therapeutic options.
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.
理想情况下，癌症患者的具体治疗方案由多学科肿瘤委员会决定，该委员会综合了先前的临床经验、已发表的数据以及患者的具体情况，以就最佳治疗策略达成共识。然而，许多肿瘤学家无法获得肿瘤委员会的支持，并且许多患者的数据描述不完整，因此肿瘤委员会必须根据不精确的标准进行决策。我们提出通过灵活但严格的数学工具来解决这些限制，该工具可以定义给定治疗方法的成功概率，并可供肿瘤学界随时使用。
我们提出了一种基于贝叶斯理论的肿瘤预测方法，该方法使用多模型框架来预测患者对不同靶向治疗的特定反应，即使在历史数据不完整的情况下也是如此。
我们证明了贝叶斯决策理论的综合能力允许同时评估一系列治疗选择。
该方法基于稳健且成熟的数学框架构建，可以在支持个体肿瘤学家和多学科肿瘤委员会的患者特定临床决策方面发挥关键作用。
