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

项目摘要/摘要-项目4 肺癌是全球癌症死亡的主要原因，仅在美国每年就有15.9万人死于肺癌。 国家肺部筛查试验(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)中对胸部进行肺癌筛查。这项研究证明了 与标准护理相比，接受CTS组的死亡率降低了20%，并导致 对重度吸烟者进行肺癌筛查的普遍接受。不幸的是，肺结节是一种 相对常见的发现，25-56%的吸烟者&50岁有CT可识别的肺结节 但其中真正致癌的不到2.5%。对于偶然发现的肺结节的诊断， 目前的指导方针要求进行额外的成像和/或侵入性活组织检查。对于这两种情况 我们建议结合两种新的方法来改善肺心病患者的风险分层。 模块。第一个涉及蛋白质组、糖链和自身抗体-抗原的抗体阵列平台 复杂的询问，产生了一个四标记面板，ROC曲线(AUC)下的面积为0.82in 诊断前样本和0.83个恶性和良性结节的验证诊断集。第二 新的组成部分是使用该方法分析从CT图像中提取的定量结节特征 “放射组学”。我们已经开发了一个经过验证的放射组学管道，它使用机器学习算法来 图像纹理特征，当与放射科医生描述的形状或语义特征相结合时，产生 使用上述相同的诊断样本集，AUC为0.82。我们已经创建了一条规则，它结合了 临床因素(年龄、吸烟等)、血浆生物标志物、放射CT图像语义和纹理特征 CT检出结节的分类为恶性或良性。放射组学和生物标记物的添加 该规则显著提高了AUC值(p&lt；0.005)，而不是单纯的临床和语义CT测量。这条规则将 首先在Vanderbilt CVC偶发/诊断队列中进行测试，然后在早期检测中修复和测试 军人中的肺癌研究1(DECAMP-1)队列(目标1)，目的是改善结节 评估。我们还将在NLST筛选队列(目标2)中测试该规则，以创建最终规则 肺癌早期发现。在目标3中，我们将测试科罗拉多大学目标1和目标2中的固定规则 分别是诊断组和DeCamp-2(诊断前)队列。

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.