Personalized Risk Prediction to Reduce Cardiovascular Disease in Childhood Cancer Survivors

项目摘要/摘要 在今天美国活着的50万儿童癌症幸存者中，最常见的非 癌症严重、危及生命或致命的慢性疾病是心血管疾病(CVD)。它是领先的非 癌症是导致这一人群过早死亡的原因。心脏辐射和蒽环类药物暴露一直是 与各种心血管疾病结局相关，包括心肌病、冠状动脉疾病(CAD)和 心脏瓣膜病。与放射治疗(RT)相关的心血管疾病的研究通常已建立 仅基于整个心脏剂量度量的关联；因此，忽略了器官和其 子结构。我们的团队是第一个报告CVD亚结构水平剂量反应的数据 儿童癌症幸存者的风险。尽管建立了不同的放射敏感性，但心脏亚结构剂量 由于缺乏验证的风险，限制通常不被纳入RT治疗计划 因此，预测模型错失了前瞻性优化RT规划和回溯的机会 对目前和未来的癌症幸存者进行个性化的风险咨询和长期心血管监测。 拟议项目的目标是开发和验证新的心血管疾病风险预测模型，该模型将 心脏亚结构剂量。此外，我们建议开发工具来将这些模型临床转化为 护理提供者的前瞻性和回溯性应用的有效个性化治疗范例 以降低心血管疾病的风险。我们将：(1)开发和验证心肌病、冠心病和 心脏瓣膜疾病合并心脏RT亚结构剂量，根据人口统计学和 (2)将心血管疾病风险预测模型整合到商业RT治疗计划中 系统和基于Web的应用程序，并通过对当代患者的电子计算机研究来确定它们的使用 用RT治疗。 这将是第一次使用不同心脏亚结构的独特放射敏感性作为 为预测新诊断为癌症儿童的特定类型心血管疾病风险的模型奠定基础 以及在长期幸存者中。将子结构剂量纳入预测模型将 显著推进前瞻性RT治疗计划和回顾风险的临床护理 评估。前瞻性地，未来幸存者的晚期心血管疾病风险可以通过优化 有心脏亚结构剂量限制的胸部定向放射治疗和选择最低剂量的方案 风险，同时保持最佳的临床靶量覆盖。治疗后回顾，临床团队 可以根据个性化的风险状况，提供基于证据的个性化风险缓解咨询 根据根据化疗暴露调整后的心脏亚结构剂量确定 人口统计数据。拟议项目的成功执行有可能改变临床实践 儿童和青少年癌症患者的治疗。

PROJECT SUMMARY/ABSTRACT Among the half a million childhood cancer survivors alive in the US today, the most commonly reported non- cancer severe, life-threatening, or fatal chronic condition is cardiovascular disease (CVD) . It is the leading non- cancer cause of premature death in this population. Heart radiation and anthracycline exposure have been associated with a variety of CVD outcomes including cardiomyopathy, coronary artery disease (CAD), and heart valve disease. Investigations of radiation therapy (RT)-related CVD have typically established associations based solely on whole heart dose metrics; thus, overlooking the heterogeneity of the organ and its substructures. Our team was the first to report data demonstrating substructure-level dose response of CVD risk in childhood cancer survivors. Despite establishing distinct radiosentivities, cardiac substructure dose constraints are not commonly incorporated into RT treatment planning due to the lack o f validated risk prediction models, thus, missing opportunities to prospectively optimize RT planning and retrospectively personalize risk-counseling and long-term cardiovascular surveillance in current and future cancer survivors. The goal of the proposed project is to develop and validate novel CVD risk prediction models that incorporate cardiac substructure doses. Further, we propose to develop tools to clinically translate these models into effective personalized treatment paradigms with prospective and retrospective applications for care providers to reduce CVD risk. We will: (1) develop and validate risk prediction models for cardiomyopathy, CAD, and heart valve disease incorporating cardiac RT substructure doses, adjusting for demographics and chemotherapy exposures; and (2) integrate CVD risk prediction models into commercial RT treatment planning systems and web-based applications, and establish their use via in-silico studies of contemporary patients treated with RT. This will be the first investigation to use the unique radiosensitivity of different cardiac substructures as the foundation for models that can predict the risk of specific types of CVD in children newly diagnosed with cancer as well as among long-term survivors. Incorporating the substructure doses into prediction models will significantly advance clinical care for both prospective RT treatment planning and retrospect ive risk assessments. Prospectively, late CVD risk could be decreased in future survivors by optimizing delivery of chest-directed RT with cardiac substructure dose constraints and selecting the plan that confers the lowest risk, while maintaining optimal clinical target volume coverage. Retrospectively post treatment, the clinical team can provide evidence-based personalized risk mitigation counseling, based on individualized risk profiles determined from delivered cardiac substructure doses adjusted for chemotherapy exposures and demographics. Successful execution of the proposed project has the potential to transform clinical practice for treatment of childhood and adolescent patients with cancer.