Project 4: Risk stratification for pulmonary nodules detected by CT imaging using plasma and imaging biomarkers

项目 4：使用血浆和成像生物标志物通过 CT 成像检测肺结节的风险分层

• 批准号: 10700904
• 负责人: PAUL D. LAMPE
• 金额: $ 51.62万
• 依托单位: FRED HUTCHINSON CANCER CENTER
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-08-01 至 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10700904
• 关键词: Age; Age Years; Antibodies; Antigens; Area; Autoantibodies; Benign; Biological Markers; Biopsy; Cancer Etiology; Cancerous; Cessation of life; Chest imaging; Classification; Clinical; Colorado; Complex; Detection; Diagnosis; Diagnostic; Evaluation; Fred Hutchinson Cancer Research Center; Goals; Guidelines; Image; Lung; Lung nodule; Malignant - descriptor; Malignant neoplasm of lung; Measures; Methods; Military Personnel; Modeling; Nodule; Patient Care; Patients; Plasma; Procedures; Proteomics; Quality of life; ROC Curve; Sampling; Semantics; Smoker; Smoking; Testing; Texture; Universities; Validation; X-Ray Computed Tomography; aged; biomarker panel; care costs; clinical biomarkers; cohort; cost; high risk population; imaging biomarker; improved; low dose computed tomography; lung cancer screening; machine learning algorithm; mortality; novel; novel strategies; radiologist; radiomics; risk stratification; screening; shape analysis; standard care

Project Summary/Abstract – Project 4 Lung cancer is the leading cause of cancer deaths worldwide with >159,000 deaths annually in the US alone. The National Lung Screening Trial (NLST) employed low-dose Computed Tomography (CT) imaging of the chest to screen for lung cancer in a high-risk population (smokers aged 55-74). This study demonstrated a 20% reduction in mortality in the group receiving CTs when compared to standard care and has led to generalized acceptance of lung cancer screening in heavy smokers. Unfortunately, pulmonary nodules are a relatively common finding with 25-56% of smokers >50 years of age having CT identifiable pulmonary nodules but less than 2.5% of these actually were cancerous. For diagnosis of incidentally detected pulmonary nodules, current guidelines call for additional imaging and/or invasive biopsy procedures. For both of these scenarios we propose to combine two novel approaches to improve risk stratification for subjects with pulmonary modules. The first involves an antibody array platform for proteomic, glycomic, and autoantibody-antigen complex interrogation that has yielded a four-marker panel with an area under the ROC curve (AUC) of 0.82 in prediagnostic samples and 0.83 in a validation diagnostic set of malignant and benign nodules. The second novel component is the analysis of quantitative nodule features extracted from CT images using the methods of 'radiomics'. We have developed a validated radiomics pipeline that used machine learning algorithms for image texture features that when combined with radiologist-described shape, or semantic features yielded an AUC of 0.82 using the same diagnostic sample set described above. We have created a rule that combines clinical factors (age, smoking etc.), plasma biomarkers, radiomic CT image semantic and texture features for classification of CT-detected nodules as malignant or benign. The addition of both radiomic and biomarkers to the rule significantly increase the AUC (p<0.005) over clinical and semantic CT measures alone. This rule will be tested first in a Vanderbilt CVC incidental/diagnostic cohort, then fixed and tested in the Detection of Early lung Cancer Among Military Personnel Study 1 (DECAMP-1) cohort (Aim 1) with the goal of improving nodule evaluation. We will also test the rule in the NLST screening cohort (Aim 2) to create a final rule that models lung cancer early detection. In Aim 3 we will test the fixed rules from aims 1 and 2 in University of Colorado diagnostic and DECAMP-2 (prediagnostic) cohorts, respectively.

项目摘要/摘要 - 项目4 肺癌是全世界癌症死亡的主要原因，仅在美国，每年每年死亡。 国家肺筛查试验（NLST）携带低剂量计算机断层扫描（CT）成像 在高危人群中肺癌的胸部到筛查（55-74岁的吸烟者）。这项研究证明了 与标准护理相比，接受CTS的组的死亡率降低了20％，并导致 在大烟中，对肺癌筛查的普遍接受。不幸的是，肺结节是 与25-56％的吸烟者> 50岁的肺部结节相关的共同发现 但实际上不到2.5％是取消的。对于偶然检测到的肺结核的诊断， 当前的指南要求其他成像和/或侵入性活检程序。对于这两种情况 我们建议将两种新型方法结合起来，以改善肺部受试者的风险分层 模块。第一个涉及用于蛋白质组学，糖和自身抗体抗体的抗体阵列平台 复杂的询问已经产生了一个四标记面板，在ROC曲线（AUC）下为0.82 IN的面积 在验证的诊断和良性结节的验证诊断集中，预诊断样品和0.83。第二个 新组件是使用这些方法从CT图像中提取的定量结节特征的分析 的“放射素”。我们已经开发了经过验证的放射线管道，该管道使用机器学习算法作为 图像纹理特征与放射科医生描述的形状混合或语义特征会产生 使用上述相同的诊断样品集的AUC为0.82。我们创建了一个结合的规则 临床因素（年龄，吸烟等），等离子体生物标志物，放射线CT图像语义和纹理特征 CT检测结节的分类为恶性或良性。将放射线和生物标志物添加到 仅临床和语义CT测量，该规则显着增加了AUC（P <0.005）。这个规则将 首先在Vanderbilt CVC偶然/诊断队列中进行测试，然后在早期检测中进行固定并测试 军事人事研究1（DECAMP-1）队列中的肺癌（AIM 1），目的是改善结节 评估。我们还将在NLST筛选队列（AIM 2）中测试规则，以创建模型的最终规则 肺癌早期检测。在AIM 3中，我们将测试科罗拉多大学AIMS 1和2的固定规则 诊断和DECAMP-2（诊断性）人群分别。