Novel Integrative Approach for the Early Detection of Lung Cancer using Repeated Measures

项目总结 在无症状个体中及早发现肺癌是降低死亡率的优先事项 一位全球癌症杀手。大多数肺癌首先被发现为不明肺结节(IPN)。 虽然绝大多数IPN是良性的，但那些恶性的IPN具有特定的特征，应该可以 对于早期的歧视和干预。我们最近完成了一项研究，证明了 结构成像特征分析在提高IPN癌症检测准确率中的作用 90%以上的准确率在NLST中接受过训练，并在两个独立的队列中得到验证。AUC从 使用临床参数(Mayo模型)估计疾病的基线风险0.78至0.84和0.82至0.92 两个独立的验证队列。同样，我们测试了我们的高灵敏度hsCYFRA 21-1的附加值 在三个肺结节人群中进行了检测，并获得了与Mayo模型类似的附加值。最后，我们 使用电子健康记录(EHR)中的大规模数据挖掘识别预测肺癌的签名。 已建立的成像预测指标、hsCyFRA浓度和EHR的性能 轨迹将在未来的队列中得到验证。在肺肿瘤学和肺部肿瘤学 范德比尔特的放射学、机器学习和数据科学专家，我们建议将临床层 在电子病历中可访问的信息，以提高无创性诊断的准确性。此外，我们建议 利用重复措施提高癌症预测的准确性，缩短预测时间 去做诊断。因此，我们提出以下目标。在目标1中，我们将验证高级定量成像 基于1000例患者重复测量的早期良性和恶性IPN的鉴别分析。在……里面 目的2.我们将在150名肺结节患者中测试hsCYFRA 21-重复测定的附加值。 1蛋白血液生物标记物在诊断准确性上超过基线浓度的生物标记物。在《目标3》中我们 将测试来自VUMC的20,000名患者的EHR的深度学习策略，以确定可能改善的模式 早期发现肺癌，在目标4中，我们将测试监测血清中 重复诊断前胸部CT检查、hsCYFRA 21-血清分析早期发现的标志 1，以及我们肺癌筛查计划中的EHR模式。建立在强大的初步数据和独特的 来自VUMC的资源，包括访问大型图像和她的数据来源这项新的综合研究 有可能产生极具影响力和可翻译的结果，以降低IPN中的假阳性率， 以及肺癌的发病率和死亡率。此应用程序使用低剂量肺对PAR 19-264做出响应 铅血清标记物与计算机断层扫描纵向分析联合筛查 人工智能挖掘EHR以发现一种新的早期检测综合策略 转移前肺癌。

PROJECT SUMMARY Early detection of lung cancer among asymptomatic individuals is a priority for reducing mortality of the number one cancer killer worldwide. Most lung cancers are first detected as indeterminate pulmonary nodules (IPNs). While the vast majority of IPNs are benign, those malignant ones present with specific features that should allow for the early discrimination and intervention. We have recently completed a study demonstrating the value of structural imaging features analysis in providing improved accuracy in detection of cancers among IPNs with accuracy of over 90% trained in the NLST and validated in two independent cohorts. The AUC increased from baseline risk estimate of disease using clinical parameters (Mayo model) 0.78 to 0.84 and from 0.82 to 0.92 in two independent validation cohorts. Similarly, we tested the added value of our high sensitivity hsCYFRA 21-1 assay in three populations of lung nodules and obtained similar added value to the MAYO model. Finally, we identified signatures predictive of lung cancer using large scale data mining in the electronic health record (EHR). The performance of the performance of the established imaging predictor, hsCYFRA concentrations and EHR trajectories will be validated in a prospective cohort. In an innovative partnership between pulmonary oncology, radiology, machine learning, and data science experts at Vanderbilt, we propose to integrate the layer of clinical information accessible in the EHR to improve the noninvasive diagnosis accuracy. In addition, we propose to take advantage of repeated measures to improve the accuracy of the prediction of cancer and to reduce the time to diagnosis. We therefore propose the following aims. In Aim 1 we will validate advanced quantitative imaging analyses to distinguish early benign from malignant IPNs based on repeated measures of 1000 individuals. In Aim 2. We will test in 150 individuals with lung nodules the added value of repeated measures of hsCYFRA 21- 1 protein blood biomarker in diagnostic accuracy over the baseline concentrations of the biomarker. In Aim 3 we will test a deep learning strategy from the EHR of 20,000 patients from VUMC to identify patterns likely to improve the early detection of lung cancer, and in Aim 4 we will test the added value of monitoring changes in levels of the markers for early detection using repeated pre-diagnosis chest CT studies, serum analysis of hsCYFRA 21- 1, and EHR patterns from our lung cancer screening program. Built upon strong preliminary data and unique resources from VUMC that include access to large imaging and HER data sources this novel integrative study has the potential to generate highly impactful and translatable results to reduce false positive rates among IPNs, and morbidity and mortality from lung cancer. This application responds to PAR 19-264 using low-dose lung screening computed tomography longitudinal analysis integrated with a lead serum biomarker and the power of artificial intelligence to mine the EHR for the discovery of a novel integrative strategy for the early detection of premetastatic lung cancer.