Modeling Precision Interventions for Prostate Cancer Control

前列腺癌控制的精准干预建模

• 批准号: 10601453
• 负责人: RUTH D ETZIONI
• 金额: $ 65.41万
• 依托单位: FRED HUTCHINSON CANCER CENTER
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-10 至 2025-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10601453
• 关键词: Modeling; Precision; Interventions; Prostate; Cancer

PROJECT SUMMARY/ABSTRACT Prostate cancer is the most common solid tumor in men and the second most common cause of cancer death in the United States. The Cancer Intervention and Surveillance Modeling Network (CISNET) Prostate Working Group (PWG) was formed in the year 2000 to address a wide range of questions about effective prostate cancer control. The PWG studied the rapid increase in prostate cancer diagnoses after PSA screening started in the late 1980s to estimate lead time and overdiagnosis associated with the test. The PWG studied the decline in prostate cancer mortality that began in the early 1990s to quantify the plausible contributions of PSA screening and changes in primary treatments. The PWG also studied how to interpret trends in racial disparities in incidence and survival, how to manage men with low-risk disease on active surveillance, and how to reconcile apparently discordant randomized trials of PSA screening and radical prostatectomy. In recent years, technologies surrounding prostate cancer screening and treatment have evolved rapidly, and opportunities to improve patient care using personalized data abound. Genetic testing can identify men at increased risk for developing aggressive disease, new biomarkers and imaging tools can help men avoid unnecessary biopsies, and new hormonal treatments can lengthen survival for men with advanced disease. The objective of this application is to extend PWG models to evaluate optimal ways to utilize personalized data to improve patient care while limiting harms and costs. We will determine whether we can improve early detection using novel stratification approaches and whether we can safely limit overtreatment and other harms by tailored choices of primary and secondary therapies. These approaches will be applied in the United States and in international cancer control settings with different resources and priorities. Our specific aims are as follows. Aim 1: Precision early detection, including risk-stratified screening and biopsy using genetic tests, novel biomarkers, and imaging technology. Aim 2: Precision active surveillance, including adaptive biopsy intervals and imaging technology. Aim 3: Precision treatment, including type and timing of initial and salvage therapies. Aim 4: Targeting screening, biopsy, and treatment policies to reduce racial disparities. Aim 5: Prioritizing screening and treatment interventions in international settings. These aims are highly responsive to the funding opportunity announcement, addressing 7 of the 9 targeted priority areas to varying degrees. Our cumulative expertise in prostate modeling, our existing models, and our close ties with clinical experts who provide access to large, high-quality datasets for model validation and calibration put us in a strong position to answer critical and impactful questions about how best to control this most common cancer in men.

项目总结/摘要 前列腺癌是男性最常见的实体瘤，也是癌症死亡的第二大常见原因 在美国癌症干预和监测建模网络（CISNET）前列腺工作 PWG成立于2000年，旨在解决有关有效前列腺治疗的广泛问题。 癌症控制PWG研究了PSA筛查开始后前列腺癌诊断的快速增加 在20世纪80年代后期，估计与测试相关的前置时间和过度诊断。PWG研究了 20世纪90年代初开始的前列腺癌死亡率下降，以量化PSA的合理贡献 筛选和主要治疗的变化。PWG还研究了如何解释种族趋势， 发病率和生存率的差异，如何管理男性低风险疾病的积极监测，以及如何 协调PSA筛查和根治性前列腺切除术的明显不一致的随机试验。 近年来，前列腺癌筛查和治疗技术发展迅速， 利用个性化数据改善患者护理的机会比比皆是。基因检测可以识别男性， 发展侵袭性疾病的风险增加，新的生物标志物和成像工具可以帮助男性避免 不必要的活组织检查和新的激素治疗可以延长晚期疾病患者的生存时间。 这个应用程序的目的是扩展PWG模型，以评估利用个性化数据的最佳方法 改善病人护理，同时限制伤害和成本。我们将确定我们是否能及早改进 使用新的分层方法进行检测，以及我们是否可以安全地限制过度治疗和其他危害 通过量身定制的选择主要和次要治疗。这些方法将在美国应用 以及在资源和优先事项不同的国际癌症控制环境中。 我们的具体目标如下。目标1：精确的早期检测，包括风险分层筛查和活检 使用基因测试、新的生物标记和成像技术。目标2：精确主动监测，包括 自适应活检间隔和成像技术。目标3：精确治疗，包括治疗的类型和时机 初始和挽救治疗。目标4：有针对性的筛查，活检和治疗政策，以减少种族歧视 差距。目标5：在国际环境中优先进行筛查和治疗干预。这些目的是 高度响应筹资机会公告，解决9个目标优先领域中的7个， 不同程度的我们在前列腺建模方面积累的专业知识，我们现有的模型，以及我们与 临床专家为模型验证和校准提供大型高质量数据集， 一个强有力的立场，以回答关键和有影响力的问题，如何最好地控制这种最常见的癌症 在男人身上。