Individualized estimates of treatment benefit from hormone therapy for men with prostate cancer

摘要 国民治疗指南，包括国家综合癌症网络(NCCN)的指南 患有局限性前列腺癌的男性基于前列腺癌特定死亡率(PCSM)风险和生命 预期由其他原因(非癌症)死亡率(OCM)驱动。虽然OCM是以下方面的主要风险 许多前列腺癌患者，在文献中受到的关注有限。我们开发并验证了 临床相关的OCM模型和预期寿命使用来自前列腺，肺， 结直肠癌和卵巢癌筛查试验(PLCO)试验。OCM因患者不同而有很大差异 人口统计学和合并症，并发现仅基于年龄的社会保障预期寿命估计 产生了相当大的偏差估计。我们的团队还开发并验证了一个临床预后模型 用于2020年发表在JAMA肿瘤学(STAR-CAP)上的PCSM。虽然竞争风险的重要性在 前列腺癌被广泛认识，到目前为止还没有提供PCSM风险估计的模型 根据OCM风险进行个性化。对局部性PC患者的许多常见和重要的治疗决定 包括是否接受激素治疗(ADT)以及治疗多长时间。指南建议许多人服用ADT 由于发病率和关于实际治疗的问题而未接受治疗的男性个人受益 病人。第三阶段试验提供了ADT在减少PCSM方面的益处的总体估计 超过大的患者群体(例如，中等风险)。然而，到目前为止，还没有工具可以提供 根据PCSM和OCM风险对个别患者的治疗收益进行估计。我们建议解决以下问题 这些缺陷通过两个目的来实现。在目标1中，我们将把我们的OCM风险模型与 远处转移(DM)和PCSM，以提供DM和PCSM的绝对风险估计， 使用PCSM和OCM风险因素进行个性化。我们假设综合模型估计 将不同于临床特别估计，并显著改善目前对PCSM的估计 考虑一下OCM风险。在目标2中，我们将开发一个Web应用程序，提供ADT的个性化评估 三种常见临床方案的治疗效果：中等风险的RT与RT+短期ADT， 高危患者RT+短期ADT与RT+长期ADT，抢救环境下RT与RT+ADT。至 要做到这一点，我们将结合来自目标1的集成多状态模型和公布的结果风险比 PCSM和DM。我们的团队处于开展这项工作的独特地位，因为我们已经开发了 组件模型，可以访问患者级别数据以进行其他分析，并具有以下跟踪记录 成功开发了帮助临床决策的网络应用程序。如果成功，在本系列中开发的工具 应用程序将为临床医生和患者提供进行知情治疗所需的关键信息 决定。

Abstract National treatment guidelines, including those from the National Comprehensive Cancer Network (NCCN), for men with localized prostate cancer are based on prostate cancer specific mortality (PCSM) risk and life expectancy which is driven by other cause (non-cancer) mortality (OCM). While OCM is the dominant risk for many prostate cancer patients, it has received limited attention in the literature. We developed and validated a clinically relevant model for OCM and life expectancy using prospective data from the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial (PLCO) trial. OCM varied substantially based on patient demographics and comorbidities and found that social security life expectancy estimations based on age alone generated substantially biased estimates. Our team also developed and validated a clinical prognostic model for PCSM published in JAMA Oncology in 2020 (STAR-CAP). While the importance of competing risks in prostate cancer is widely recognized, there are no models to date which provide PCSM risk estimates individualized by OCM risk. Many of the common and important treatment decisions for men with localized PC include whether to treat with hormone therapy (ADT) and for how long. Guidelines recommend ADT for many men who are treated without it due to morbidity and questions as to actual treatment benefit for individual patients. Phase III trials have provided overall estimates of the benefit of ADT in terms of reduction in PCSM over large groups of patients (e.g. intermediate risk). To date however, there are no tools which provide treatment benefit estimates for individual patients based on PCSM and OCM risk. We propose to address these shortcomings through two aims. In Aim 1 we will integrate our model for OCM risk with models for distant metastases (DM) and PCSM to provide estimates of the absolute risk of DM and PCSM that are personalized using both PCSM and OCM risk factors. We hypothesize that the integrated model estimates will differ from clinical ad hoc estimates and significantly improve upon current estimates of PCSM that do not consider OCM risk. In Aim 2 we will develop a web app which provides individualized estimates of ADT treatment effect in three common clinical scenarios: RT vs RT + short term ADT in intermediate risk, RT + short term ADT vs RT + long term ADT in high risk and RT vs RT + ADT in the salvage setting. To do this we will combine the integrated multistate model from Aim 1 with published hazard ratios for outcomes of PCSM and DM. Our group is uniquely positioned to carry out this work as we have already developed the component models, have access to the patient level data for additional analyses and have a track record of successful development of web apps to aid in clinical decision making. If successful, the tools developed in this application will provide clinicians and patients the key information they need to make informed treatment decisions.