A decision aid for intensity-modulated radiation-therapy plan selection in prostate cancer based on a prognostic Bayesian network and a Markov model.

The prognosis of cancer patients treated with intensity modulated radiation therapy (IMRT) is inherently uncertain, depends on many decision variables, and requires that a physician balance competing objectives: maximum tumor control with minimal treatment complications. In order to better deal with the complex and multiple objective nature of the problem we have combined a prognostic probabilistic model with multi-attribute decision theory which incorporates patient preferences for outcomes. The response to IMRT for prostate cancer was modeled. A Bayesian network was used for prognosis for each treatment plan. Prognoses included predicting local tumor control, regional spread, distant metastases, and normal tissue complications resulting from treatment. A Markov model was constructed and used to calculate a quality-adjusted life-expectancy which aids in the multi-attribute decision process. Our method makes explicit the tradeoffs patients face between quality and quantity of life. This approach has advantages over current approaches because with our approach risks of health outcomes and patient preferences determine treatment decisions.

接受调强放射治疗（IMRT）的癌症患者的预后本身是不确定的，取决于许多决策变量，并且要求医生平衡相互竞争的目标：以最小的治疗并发症实现最大程度的肿瘤控制。 为了更好地处理该问题的复杂性和多目标性质，我们将一种预后概率模型与多属性决策理论相结合，该理论纳入了患者对治疗结果的偏好。 对前列腺癌对IMRT的反应进行了建模。使用贝叶斯网络对每个治疗方案进行预后。预后包括预测局部肿瘤控制、区域扩散、远处转移以及治疗导致的正常组织并发症。构建了一个马尔可夫模型，并用于计算质量调整预期寿命，这有助于多属性决策过程。 我们的方法明确了患者在生活质量和数量之间面临的权衡。这种方法相对于当前的方法具有优势，因为使用我们的方法，健康结果的风险和患者的偏好决定治疗决策。