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

使用重复测量早期检测肺癌的新综合方法

• 批准号: 10322712
• 负责人: Bennett A. Landman
• 金额: $ 65.99万
• 依托单位: VANDERBILT UNIVERSITY MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-01-01 至 2025-12-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10322712
• 关键词: Academic Medical Centers; Address; Algorithms; Anxiety; Artificial Intelligence; Benign; Biological Assay; Biological Markers; Blood; Blood Proteins; Cancer Detection; Cessation of life; Clinical; Clinical Data; Clinical Management; Clinical Markers; Community Hospitals; Data; Data Science; Data Sources; Diagnosis; Diagnostic; Discrimination; Disease; Dose; Early Diagnosis; Electronic Health Record; Enrollment; Funding; Gaussian model; Goals; Health Care Costs; Healthcare; Image; Individual; Intervention; Joints; Lead; Lung; Lung nodule; Machine Learning; Malignant - descriptor; Malignant Neoplasms; Malignant neoplasm of lung; Measurement; Measures; Modeling; Modernization; Monitor; Morbidity - disease rate; Nodule; Oncology; Operative Surgical Procedures; Outcome; Patients; Pattern; Performance; Phenotype; Population; Predictive Value; Procedures; Prospective cohort; Radiation; Radiology Specialty; Research; Resources; Risk; Risk Estimate; Scanning; Series; Serum; Source; Structure; Study of serum; Testing; Thoracotomy; Time; Training; Validation; X-Ray Computed Tomography; base; biological specimen archives; biomedical informatics; blood-based biomarker; chest computed tomography; clinical predictors; cohort; computed tomography screening; convolutional neural network; cost; data mining; deep learning; deep learning model; demographics; diagnostic accuracy; early detection biomarkers; high risk; imaging biomarker; improved; innovation; large scale data; learning strategy; longitudinal analysis; low dose computed tomography; lung cancer screening; lung lesion; machine learning method; molecular marker; mortality; neural network algorithm; noninvasive diagnosis; novel; patient oriented; predictive modeling; predictive signature; premalignant; programs; quantitative imaging; rate of change; research clinical testing; screening program; serial imaging

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.

项目概要 在无症状个体中及早发现肺癌是降低无症状个体死亡率的首要任务 全球癌症杀手之一。大多数肺癌首先被检测为不确定的肺结节（IPN）。 虽然绝大多数 IPN 是良性的，但那些恶性 IPN 具有特定的特征，应该允许 以便及早歧视和干预。我们最近完成了一项研究，展示了 结构成像特征分析可提高 IPN 中癌症检测的准确性 在 NLST 中训练并在两个独立队列中验证的准确率超过 90%。 AUC 增加自 使用临床参数（Mayo 模型）进行疾病基线风险评估 0.78 至 0.84，以及 0.82 至 0.92 两个独立的验证队列。同样，我们测试了高灵敏度 hsCYFRA 21-1 的附加值 在三个肺结节群体中进行了测定，并获得了与 MAYO 模型相似的附加值。最后，我们 使用电子健康记录 (EHR) 中的大规模数据挖掘来识别肺癌的预测特征。 建立的影像预测器的性能、hsCYFRA浓度和EHR的性能 轨迹将在前瞻性队列中得到验证。在肺肿瘤学之间的创新合作伙伴关系中， 范德堡大学的放射学、机器学习和数据科学专家，我们建议整合临床层 EHR 中可访问的信息可提高无创诊断的准确性。此外，我们建议 利用重复措施提高癌症预测的准确性并减少时间 来诊断。因此，我们提出以下目标。在目标 1 中，我们将验证先进的定量成像 基于对 1000 名个体的重复测量，进行分析以区分早期良性和恶性 IPN。在 目标 2. 我们将在 150 名患有肺结节的个体中测试重复测量 hsCYFRA 21- 的附加值 1 蛋白质血液生物标志物的诊断准确性高于生物标志物基线浓度。在目标 3 中，我们 将测试来自 VUMC 20,000 名患者 EHR 的深度学习策略，以识别可能改善的模式 肺癌的早期发现，在目标 4 中，我们将测试监测肺癌水平变化的附加值 使用重复诊断前胸部 CT 研究、hsCYFRA 21-血清分析来进行早期检测的标志物 1，以及我们肺癌筛查计划的 EHR 模式。建立在强大的初步数据和独特的 VUMC 的资源，包括访问大型成像和 HER 数据源这项新颖的综合研究 有潜力产生具有高度影响力和可转化的结果，以减少 IPN 中的误报率， 以及肺癌的发病率和死亡率。该应用程序使用低剂量肺响应 PAR 19-264 与铅血清生物标志物相结合的筛查计算机断层扫描纵向分析以及 人工智能挖掘电子病历，发现一种早期检测的新型综合策略 转移前肺癌。