Non-invasive evaluation of indeterminate pulmonary nodules

 描述(申请人提供)：我们面临着不明肺结节(IPN)的流行，不仅是偶然发现的，而且是由于国家肺部筛查试验(NLST)取得令人鼓舞的结果后，针对肺癌高危人群的CT筛查计划的激增。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恶性结节，导致大量后续CT、不必要的侵入性活检、焦虑和浪费医疗费用。我们的项目解决了这一知识不足的问题，我们预计这将导致在IPN患者的临床管理中改进非侵入性测试和范式转换预测模型。该项目有可能改变对携带IPN的个人的管理。我们的目标是通过将临床变量、创新的量化结构CT变量与IPNS的新的基于血液的生物标记物候选者相结合，在筛查检测到的和偶然检测到的IPNS的非侵入性诊断准确性方面获得显著提高。我们将生产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，反之，识别其他风险非常低、不需要广泛随访的人。根据该项目的发现，我们预计1)使用纵向数据随着时间的推移提高我们对IPN的预测准确性；2)降低不必要的开胸手术的比率，减轻包括焦虑在内的不必要的后续行动的负担；3)最终提高治愈率，减少不必要的辐射和医疗费用。

 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.