Assessment of a Radiomics-Based Computer-Aided Diagnosis Tool for Cancer Risk Stratification of Pulmonary Nodules

基于放射组学的计算机辅助诊断工具对肺结节癌症风险分层的评估

• 批准号: 10644765
• 负责人: Roger Yeon-Kyu Kim
• 金额: $ 24.96万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-01 至 2028-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10644765
• 关键词: Address; Adult; Advisory Committees; Agreement; Award; Benign; Biometry; Biopsy; Cancer Center; Classification; Clinic; Clinical; Clinical Management; Clinical Oncology; Clinical Research; Clinical Trials; Clinical Trials Design; Clinical effectiveness; Complex; Computer-Assisted Diagnosis; Cost Effectiveness Analysis; Data; Development Plans; Diagnosis; Diagnostic; Early Diagnosis; Effectiveness; Epidemiology; Evaluation; Eye; Face; Financial Support; Goals; Human; Image; Individual; Institution; Intervention; K-Series Research Career Programs; Knowledge; Lung; Lung nodule; Malignant - descriptor; Malignant Neoplasms; Malignant neoplasm of lung; Malignant neoplasm of thorax; Mentors; Mentorship; Methods; Modeling; Outcome; Patient-Focused Outcomes; Patients; Pennsylvania; Performance; Physicians; Positioning Attribute; Postdoctoral Fellow; Probability; Procedures; Process; Publishing; Pulmonary Challenge; Pulmonology; Quality-Adjusted Life Years; Research; Research Personnel; Research Training; Retrospective cohort; Retrospective cohort study; Risk; Risk Estimate; Scientist; Testing; Thoracic Oncology; Time; Training; Universities; Use Effectiveness; Vision; X-Ray Computed Tomography; base; cancer care; cancer diagnosis; cancer risk; career; career development; clinical practice; clinical risk; clinically significant; cohort; collaborative environment; comparative effectiveness trial; cost; cost effective; cost effectiveness; cost-effectiveness evaluation; diagnostic accuracy; effectiveness evaluation; experience; improved; indexing; innovation; lung cancer screening; multidisciplinary; new technology; novel; participant enrollment; primary outcome; radiomics; randomized, clinical trials; research clinical testing; risk prediction model; risk stratification; serial imaging; skill acquisition; supportive environment; tool; treatment as usual; uptake

PROJECT SUMMARY/ABSTRACT This proposal describes a comprehensive 5-year mentored career development plan with the goal of training the candidate to become a leading independent physician-scientist focused on improving the diagnostic evaluation and management of pulmonary nodules (PNs) to optimize early detection of thoracic cancer and minimize unnecessary harms to patients. The candidate is currently a Post-Doctoral Research Fellow and Attending Pulmonologist at the University of Pennsylvania (Penn). The proposal builds upon Dr. Kim’s previous research training in epidemiology and biostatistics and clinical experience in thoracic oncology. PNs are commonly detected by computed tomography (CT). Lung biopsy, a highly invasive procedure, is required for a definitive diagnosis but carries significant risks and costs. Thus, clinicians face the diagnostic challenge of PN malignancy risk estimation when deciding which patients should undergo a biopsy, and which should be surveilled with repeat imaging. The overall goal of this project is to address the current inadequacy of estimating malignancy risk within the diagnostic process of PN evaluation by assessing the clinical utility and effectiveness of a radiomics-based computer-aided diagnosis (CAD) tool. This novel technology synthesizes quantitative features from raw CT imaging data invisible to the human eye and has been previously demonstrated by the candidate’s team to improve clinicians’ PN diagnostic accuracy. This project’s goal will be accomplished via three complementary specific aims. In Aim 1, a retrospective cohort study will be performed to determine the clinical utility of a CAD-based risk stratification strategy using net reclassification indices, decision curve analysis, and relative utility curves. In Aim 2, a pilot, single-center pragmatic randomized clinical trial will be conducted to compare the clinical effectiveness of a CAD-based risk stratification strategy to usual care for appropriate management of PNs, defined as biopsy or empiric treatment for malignant PNs and surveillance for benign PNs. Finally, in Aim 3, the cost-effectiveness of a CAD-based risk stratification strategy will be evaluated using decision analytic models for a simulated cohort of individuals with newly detected PNs. Dr. Kim has outlined a rigorous training plan of coursework, skills acquisition (with a focus on clinical utility analysis, clinical trial design, decision analytic modeling, and cost-effectiveness analysis), and professional career development. To realize this vision, he has assembled a distinguished, multidisciplinary mentorship and advisory team, led by his primary mentor, Dr. Anil Vachani, the Director of Clinical Research in the Section of Interventional Pulmonology and Thoracic Oncology at Penn, and co-mentor, Dr. Katharine Rendle, Deputy Director for Research at the Penn Center for Cancer Care Innovation. Penn provides an outstanding intellectual, collaborative, and supportive environment for this proposal, positioning Dr. Kim to successfully complete the aims and training plan so that he will be a competitive applicant for an R01 award to perform a multicenter comparative effectiveness trial assessing PN malignancy risk stratification strategies.

项目总结/摘要 本建议书描述了一个全面的5年职业发展计划，其目标是培训 候选人成为一个领先的独立的医生，科学家专注于改善诊断 肺结节（PN）的评估和管理，以优化胸部癌症的早期检测， 尽量减少对患者的不必要伤害。候选人目前是博士后研究员， 宾夕法尼亚大学（Penn）的主治肺病学家。该提案建立在金博士以前的 流行病学和生物统计学的研究培训以及胸部肿瘤学的临床经验。PN是 通常通过计算机断层扫描（CT）检测。肺活检是一种高度侵入性的手术， 明确的诊断，但具有重大的风险和成本。因此，临床医生面临着诊断PN的挑战 在决定哪些患者应该接受活检时， 通过重复成像进行监视。该项目的总体目标是解决当前的不足之处 通过评估临床效用，在PN评价的诊断过程中估计恶性肿瘤风险， 基于放射学的计算机辅助诊断（CAD）工具的有效性。这项新技术合成了 从原始CT成像数据中提取人眼不可见的定量特征，并且先前已经被 候选人的团队证明，以提高临床医生的PN诊断准确性。该项目的目标将是 通过三个互补的具体目标来实现。在目标1中，将进行回顾性队列研究 为了使用净重新分类指数确定基于CAD的风险分层策略的临床效用， 决策曲线分析和相对效用曲线。在目标2中，一项试点、单中心、实用的随机临床研究 将进行一项试验，以比较基于CAD的风险分层策略与常规风险分层策略的临床有效性。 护理PN的适当管理，定义为恶性PN的活检或经验性治疗， 监测良性PN。最后，在目标3中，基于CAD的风险分层策略的成本效益 将使用决策分析模型对具有新检测到的PN的个体的模拟队列进行评估。 博士Kim概述了严格的课程培训计划，技能获取（重点是临床实用性 分析、临床试验设计、决策分析建模和成本效益分析），以及专业 职业发展。为了实现这一愿景，他组建了一个杰出的、多学科的导师队伍， 顾问团队，由他的主要导师Anil Vachani博士领导，他是该部门的临床研究主任。 宾夕法尼亚大学介入肺病学和胸部肿瘤学，以及共同导师，副教授Katharine Rendle博士 宾州癌症护理创新中心研究主任。宾州大学提供了一个杰出的 智力，协作和支持环境，为这项建议，定位金博士成功地 完成目标和培训计划，以便他将成为R 01奖的竞争性申请人， 评估PN恶性肿瘤风险分层策略的多中心比较有效性试验。