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: 10601291
• 负责人: PAUL D. LAMPE
• 金额: $ 12万
• 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 2024-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10601291
• 关键词: Project; Risk; stratification; pulmonary; nodules

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.

项目概要/摘要-项目4 肺癌是全世界癌症死亡的主要原因，仅在美国每年就有> 159，000人死亡。 国家肺筛查试验（NLST）采用低剂量计算机断层扫描（CT）成像， 在高危人群（55-74岁的吸烟者）中进行肺癌筛查。这项研究表明， 与标准治疗相比，接受CT治疗组的死亡率降低20%， 在重度吸烟者中普遍接受肺癌筛查。不幸的是，肺结节是 相对常见的发现，25-56%>50岁的吸烟者具有CT可识别的肺结节 但实际上只有不到2.5%是癌症。对于偶然检测到的肺结节的诊断， 目前的指南要求额外的成像和/或侵入性活组织检查程序。对于这两种情况， 我们建议联合收割机结合两种新方法来改善肺部疾病受试者的风险分层 模块。第一个涉及用于蛋白质组学、糖组学和自身抗体抗原的抗体阵列平台 复杂的询问产生了四个标志物组，ROC曲线下面积（AUC）为0.82， 诊断前样本和0.83的恶性和良性结节的验证诊断集。第二 新的组成部分是分析使用这些方法从CT图像中提取的定量结节特征 关于“放射学”我们已经开发了一个经过验证的放射组学管道，该管道使用机器学习算法， 当与放射科医生描述的形状或语义特征结合时， AUC为0.82，使用上述相同的诊断样品集。我们制定了一条规则 临床因素（年龄、吸烟等），血浆生物标志物、放射组学CT图像语义和纹理特征， 将CT检测到的结节分类为恶性或良性。放射组学和生物标志物的增加， 与单独的临床和语义CT测量相比，该规则显著增加AUC（p<0.005）。此规则将 首先在范德比尔特CVC偶发/诊断队列中进行测试，然后在早期CVC检测中进行固定和测试。 军事人员肺癌研究1（DECAMP-1）队列（目的1），目的是改善结节 评价我们还将在NLST筛选队列（目标2）中测试该规则，以创建一个最终规则， 肺癌早期诊断在目标3中，我们将在科罗拉多大学测试目标1和目标2中的固定规则 诊断和DECAMP-2（诊断前）队列。