Non-invasive evaluation of indeterminate pulmonary nodules

不确定肺结节的无创评估

• 批准号: 8890582
• 负责人: PIERRE P. MASSION
• 金额: $ 11.31万
• 依托单位: VANDERBILT UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-08-03 至 2016-04-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8890582
• 关键词: Address; Anxiety; Autoantibodies; Benign; Biological Markers; Biopsy; Blood; Cessation of life; Characteristics; Chest; Clinical; Clinical Management; Clinical Trials; Community Hospitals; Data; Data Set; Diagnostic; Discrimination; Disease; Dose; Epidemic; Evaluation; Goals; Health Care Costs; Healthcare; Image Analysis; Individual; Intervention; Investigation; Knowledge; Laboratories; Lung; Lung nodule; Malignant - descriptor; Malignant Neoplasms; Malignant neoplasm of lung; Modeling; Molecular; Molecular Profiling; Morbidity - disease rate; Nodule; Patients; Peptides; Performance; Population; Probability; Radiation; Research; Risk; Risk Estimate; Scanning; Techniques; Technology; Testing; Thoracotomy; Time; Validation; X-Ray Computed Tomography; base; cohort; cost; diagnostic accuracy; follow-up; high risk; imaging biomarker; improved; innovation; lung cancer screening; molecular imaging; novel; personalized care; programs; public health relevance; quantitative imaging; screening; tool; wasting

 DESCRIPTION (provided by applicant): We are facing an epidemic of indeterminate pulmonary nodules (IPNs), not only those found incidentally, but also through the proliferation of CT screening programs targeting high-risk individuals for lung cancer following the encouraging results of the National Lung Screening Trial (NLST). Although the large majority of IPNs are benign, there are currently no effective predictive tools to discriminate benign from malignant nodules, leading to a large number of follow-up CTs, unnecessary invasive biopsies, anxiety, and wasted healthcare spending. Our project addresses this deficiency in knowledge, which we expect will result in improved non-invasive testing and paradigm-shifting prediction models in the clinical management of patients with IPNs. This project has the potential to transform the management of individuals presenting with IPNs. We aim at obtaining significant gains in non-invasive diagnostic accuracy of screening-detected and incidentally-detected IPNs by integrating clinical variables, innovative quantitative structural CT variables with novel blood-based biomarker candidates of IPNs. We will produce actionable diagnostic prediction models to personalize care and identify the small subset of individuals who will need a definitive intervention for lung cancer, and conversely, others who are at very low risk and do not require extensive follow-up. Based on findings from this project, we anticipate 1) using longitudinal data to improve our prediction accuracy for IPNs over time; 2) reduction in the rate of unneeded thoracotomies, the burden of unnecessary follow-up including anxiety; 3) ultimately increase the rate of cure and reduction of unnecessary radiation and healthcare cost.

 描述（由申请人提供）：我们正面临着不确定的肺结节（IPN）的流行，不仅是偶然发现的，而且还通过国家肺筛查试验（NLST）令人鼓舞的结果后，针对肺癌高危人群的CT筛查计划的扩散。虽然绝大多数IPN是良性的，但目前还没有有效的预测工具来区分良性和恶性结节，导致大量的后续CT，不必要的侵入性活检，焦虑和浪费医疗保健支出。我们的项目解决了这方面的知识不足，我们预计这将导致改进的非侵入性测试和范式转移预测模型在患者的临床管理与IPNs。这个项目有可能改变与IPN提出的个人的管理。我们的目标是通过整合临床变量，创新的定量结构CT变量与新的基于血液的候选生物标志物的IPN，获得显着的收益，在非侵入性诊断准确性的筛选检测和偶然检测的IPN。我们将建立可操作的诊断预测模型，以个性化护理，并确定需要对肺癌进行明确干预的一小部分人，以及其他风险非常低且不需要广泛随访的人。基于该项目的发现，我们预计1）使用纵向数据来提高我们对IPN随时间推移的预测准确性; 2）减少不必要的开胸手术的发生率，减少不必要的随访负担，包括焦虑; 3）最终提高治愈率，减少不必要的放射和医疗费用。