Personalized Risk Prediction to Reduce Cardiovascular Disease in Childhood Cancer Survivors

个性化风险预测可减少儿童癌症幸存者的心血管疾病

基本信息

批准号：
10666533
负责人：
Rebecca Maureen Howell
金额：
$ 70.12万
依托单位：
UNIVERSITY OF TX MD ANDERSON CAN CTR
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-07-15 至 2026-04-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10666533
关键词：
Accounting Address Adult Adult Hodgkin&apos s Lymphoma Aftercare Age Anthracycline Arrhythmia Cancer Patient Cancer Survivor Cardiac Cardiomyopathies Cardiovascular Diseases Cardiovascular system Caring Case/Control Studies Cause of Death Cessation of life Chest Child Childhood Childhood Cancer Survivor Study Chronic Clinic Clinical Clinical Trials Cohort Studies Collaborations Communities Coronary Arteriosclerosis Coronary artery Counseling Data Data Reporting Diabetes Mellitus Disease Outcome Dose Event Foundations Future Goals Health Heart Heart Atrium Heart Valve Diseases Heart failure Heterogeneity Hodgkin Lymphoma survivors Hypertension Institution Investigation Left ventricular structure Life Long-Term Survivors Malignant Neoplasms Malignant neoplasm of lung Methods Modality Modeling Morbidity - disease rate Newly Diagnosed Obesity Organ Outcome Patient Self-Report Patients Pediatric Oncology Population Premature Mortality Radiation Radiation Tolerance Radiation therapy Recommendation Reporting Risk Risk Assessment Risk Factors Risk Reduction Saint Jude Children&apos s Research Hospital Smoking Survival Rate Survivors System Translating Ventricular Work adolescent patient cancer therapy cardiovascular disorder risk care providers chemotherapy childhood cancer survivor clinical care clinical practice cohort demographics evidence base in silico modifiable risk mortality novel personalized medicine personalized risk prediction predictive modeling premature prospective reconstruction response risk mitigation risk prediction risk prediction model survivorship tool treatment optimization treatment planning web app

项目摘要

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.

项目概要/摘要在当今美国 50 万儿童癌症幸存者中，最常报告的非癌症幸存者是癌症严重的、危及生命的或致命的慢性疾病是心血管疾病（CVD）。它是领先的非癌症是该人群过早死亡的原因。心脏辐射和蒽环类药物暴露与多种 CVD 结局相关，包括心肌病、冠状动脉疾病 (CAD) 和心脏瓣膜疾病。放射治疗 (RT) 相关 CVD 的调查通常已确定仅基于全心剂量指标的关联；因此，忽视了器官及其异质性子结构。我们的团队是第一个报告证明 CVD 子结构水平剂量反应的数据的团队儿童癌症幸存者的风险。尽管建立了不同的放射敏感性，心脏亚结构剂量由于缺乏经过验证的风险，限制因素通常不会纳入 RT 治疗计划中预测模型，因此错过了前瞻性优化 RT 规划和回顾性的机会为当前和未来的癌症幸存者提供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颖的 CVD 风险预测模型，其中包含心脏亚结构剂量。此外，我们建议开发工具将这些模型临床转化为有效的个性化治疗范例，为护理提供者提供前瞻性和回顾性应用以降低 CVD 风险。我们将：(1) 开发并验证心肌病、冠心病和心脏病的风险预测模型心脏瓣膜疾病合并心脏 RT 亚结构剂量，根据人口统计数据进行调整化疗暴露； (2) 将 CVD 风险预测模型纳入商业 RT 治疗计划系统和基于网络的应用程序，并通过对当代患者的计算机研究确定其用途采用 RT 治疗。这将是首次利用不同心脏亚结构的独特放射敏感性作为为新诊断癌症儿童预测特定类型 CVD 风险的模型奠定了基础以及长期幸存者中。将子结构剂量纳入预测模型将显着推进前瞻性 RT 治疗计划和回顾性风险的临床护理评估。前瞻性地，通过优化交付，可以降低未来幸存者的晚期 CVD 风险具有心脏亚结构剂量限制的胸部定向放疗，并选择提供最低剂量的计划风险，同时保持最佳的临床目标体积覆盖。临床团队回顾性治疗后可以根据个性化的风险概况提供循证的个性化风险缓解咨询根据根据化疗暴露调整的递送心脏亚结构剂量确定，并且人口统计。拟议项目的成功执行有可能改变临床实践治疗儿童和青少年癌症患者。