Personalized Risk Prediction for Cancer Survivors: A Bayesian Semi-parametric Recurrent Event Model with Competing Outcomes.
Personalized Risk Prediction for Cancer Survivors: A Bayesian Semi-parametric Recurrent Event Model with Competing Outcomes.
复制标题
癌症幸存者的个性化风险预测：具有竞争结果的贝叶斯半参数复发事件模型。
DOI：
10.1101/2023.02.28.530537
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发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Wang,Wenyi
中科院分区：
文献类型：
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作者：
Nguyen,NamH;Shin,SeungJun;Dodd-Eaton,ElissaB;Ning,Jing;Wang,Wenyi
由于癌症患者生存率的提高，多原发性癌症变得越来越常见。第一个原发癌症的特征在很大程度上影响随后发生原发癌症的风险。因此，制定医疗保健政策需要基于模型的癌症幸存者风险特征描述，以捕获患者特定的变量。我们提出了一个贝叶斯半参数框架，其中竞争癌症类型的发生过程遵循独立的非同质泊松过程，并调整协变量，包括首次原发性癌症诊断时的类型和年龄。将这个框架应用于历史上收集的队列，其中的家庭呈现出高度丰富的多种原发性肿瘤和不同癌症类型的历史，我们得出了一套癌症幸存者的年龄到发病外显率曲线。这包括第二原发性肺癌的外显率估计，可能对正在进行的癌症筛查决策产生影响。使用受试者工作特征 (ROC) 曲线，我们验证了我们的模型在预测第二原发性肺癌、肉瘤、乳腺癌和所有其他癌症组合方面的良好预测性能，曲线下面积 (AUC) 分别为 0.89、0.91、0.76 和 0.68。总之，我们的框架为癌症幸存者提供了协变量调整的定量风险评估，从而向这一独特人群的个性化健康管理迈进了一步。
Multiple primary cancers are increasingly more frequent due to improved survival of cancer patients. Characteristics of the first primary cancer largely impact the risk of developing subsequent primary cancers. Hence, model-based risk characterization of cancer survivors that captures patient-specific variables is needed for healthcare policy making. We propose a Bayesian semi-parametric framework, where the occurrence processes of the competing cancer types follow independent non-homogeneous Poisson processes and adjust for covariates including the type and age at diagnosis of the first primary. Applying this framework to a historically collected cohort with families presenting a highly enriched history of multiple primary tumors and diverse cancer types, we have derived a suite of age-to-onset penetrance curves for cancer survivors. This includes penetrance estimates for second primary lung cancer, potentially impactful to ongoing cancer screening decisions. Using Receiver Operating Characteristic (ROC) curves, we have validated the good predictive performance of our models in predicting second primary lung cancer, sarcoma, breast cancer, and all other cancers combined, with areas under the curves (AUCs) at 0.89, 0.91, 0.76 and 0.68, respectively. In conclusion, our framework provides covariate-adjusted quantitative risk assessment for cancer survivors, hence moving a step closer to personalized health management for this unique population.