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: 10596570
• 负责人: Bennett A. Landman
• 金额: $ 65.12万
• 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/10596570
• 关键词: Academic Medical Centers; Address; Algorithms; Anxiety; Artificial Intelligence; Benign; Biological Assay; Biological Markers; Blood; Blood Proteins; Cancer Detection; Cessation of life; Classification; 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; 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 Assessment; Risk Estimate; Scanning; Series; Serum; Source; Structure; Testing; Thoracotomy; Time; Training; Validation; X-Ray Computed Tomography; biological specimen archives; biomedical informatics; blood-based biomarker; chest computed tomography; clinical predictive model; 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 population; 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; structural 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.

项目摘要 早期发现无症状的肺癌患者是降低死亡率的优先事项 一个癌症杀手。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，但那些恶性的IPN具有特定的特征， 早期的歧视和干预。我们最近完成了一项研究， 结构成像特征分析在IPN中提供改进的癌症检测准确性， 在NLST中训练的准确性超过90%，并在两个独立的队列中验证。AUC从 使用临床参数（马约模型）的疾病基线风险估计值为0.78至0.84， 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， 在三个群体的肺结节中进行测定，并获得了与马约模型相似的附加值。最后我们 在电子健康记录（EHR）中使用大规模数据挖掘识别预测肺癌的特征。 已建立的影像学预测指标、hsCYFRA浓度和EHR的表现 将在前瞻性队列中验证轨迹。在肺肿瘤学， 放射学，机器学习和数据科学专家在范德比尔特，我们建议整合层的临床 在EHR中可访问的信息，以提高非侵入性诊断的准确性。此外，我们建议 利用重复测量来提高癌症预测的准确性， 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 分析以基于1000个个体的重复测量区分早期良性与恶性IPN。在 目标2.我们将在150名肺结节患者中测试重复测量hsCYFRA 21- 22的附加值。 1蛋白质血液生物标志物在诊断准确性方面优于生物标志物的基线浓度。在目标3中， 将从VUMC的20，000名患者的EHR中测试深度学习策略，以确定可能改善的模式 在目标4中，我们将测试监测肺癌水平变化的附加值， 使用重复的诊断前胸部CT研究进行早期检测的标志物，hsCYFRA 21的血清分析， 1，和我们的肺癌筛查计划的EHR模式。建立在强大的初步数据和独特的 来自VUMC的资源，包括访问大型成像和HER数据源， 有可能产生高度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。该应用程序响应PAR 19-264使用低剂量肺 筛选计算机断层扫描纵向分析与铅血清生物标志物和功率集成 人工智能挖掘EHR，以发现一种新的早期检测EHR的综合策略。 转移前肺癌