Liu Ying, Wang Hua, Li Qian, McGettigan Melissa J, Balagurunathan Yoganand, Garcia Alberto L, Thompson Zachary J, Heine John J, Ye Zhaoxiang, Gillies Robert J, Schabath Matthew B
From the Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer; Key Laboratory of Cancer Prevention and Therapy, Tianjin; and Tianjin's Clinical Research Center for Cancer, Tianjin, China (Y.L., H.W., Q.L., Z.Y.); and Departments of Cancer Imaging and Metabolism (Y.L., Q.L., Y.B., A.L.G., R.J.G.), Diagnostic Imaging and Interventional Radiology (M.J.M.), Biostatistics and Bioinformatics (Z.J.T.), Cancer Epidemiology (J.J.H., M.B.S.), and Thoracic Oncology (M.B.S.), H. Lee Moffitt Cancer Center and Research Institute, 12902 Magnolia Dr, MRC-CANCONT, Tampa, FL 33612.
Radiology. 2018 Jan;286(1):298-306. doi: 10.1148/radiol.2017161458. Epub 2017 Aug 24.
Purpose To extract radiologic features from small pulmonary nodules (SPNs) that did not meet the original criteria for a positive screening test and identify features associated with lung cancer risk by using data and images from the National Lung Screening Trial (NLST). Materials and Methods Radiologic features in SPNs in baseline low-dose computed tomography (CT) screening studies that did not meet NLST criteria to be considered a positive screening examination were extracted. SPNs were identified for 73 incident case patients who were given a diagnosis of lung cancer at either the first or second follow-up screening study and for 157 control subjects who had undergone three consecutive negative screening studies. Multivariable logistic regression was used to assess the association between radiologic features and lung cancer risk. All statistical tests were two sided. Results Nine features were significantly different between case patients and control subjects. Backward elimination followed by bootstrap resampling identified a reduced model of highly informative radiologic features with an area under the receiver operating characteristic curve of 0.932 (95% confidence interval [CI]: 0.88, 0.96), a specificity of 92.38% (95% CI: 52.22%, 84.91%), and a sensitivity of 76.55% (95% CI: 87.50%, 95.35%) that included total emphysema score (odds ratio [OR] = 1.71; 95% CI: 1.39, 2.01), attachment to vessel (OR = 2.41; 95% CI: 0.99, 5.81), nodule location (OR = 3.25; 95% CI: 1.09, 8.55), border definition (OR = 7.56; 95% CI: 1.89, 30.8), and concavity (OR = 2.58; 95% CI: 0.89, 5.64). Conclusion A set of clinically relevant radiologic features were identified that that can be easily scored in the clinical setting and may be of use to determine lung cancer risk among participants with SPNs. RSNA, 2017 Online supplemental material is available for this article.
目的 通过使用国家肺癌筛查试验（NLST）的数据和图像，从小型肺结节（SPN）中提取不符合阳性筛查试验原始标准的放射学特征，并识别与肺癌风险相关的特征。材料与方法 从基线低剂量计算机断层扫描（CT）筛查研究中不符合NLST标准而不被视为阳性筛查检查的SPN中提取放射学特征。在首次或第二次随访筛查研究中被诊断为肺癌的73例发病病例患者以及连续接受三次阴性筛查研究的157例对照受试者中识别出SPN。使用多变量逻辑回归评估放射学特征与肺癌风险之间的关联。所有统计检验均为双侧检验。结果 病例患者与对照受试者之间有9个特征存在显著差异。通过向后消除法然后进行自助重采样，确定了一个信息量丰富的放射学特征简化模型，其受试者操作特征曲线下面积为0.932（95%置信区间[CI]：0.88，0.96），特异性为92.38%（95%CI：52.22%，84.91%），敏感性为76.55%（95%CI：87.50%，95.35%），该模型包括全肺气肿评分（优势比[OR]=1.71；95%CI：1.39，2.01）、与血管的附着情况（OR=2.41；95%CI：0.99，5.81）、结节位置（OR=3.25；95%CI：1.09，8.55）、边界清晰度（OR=7.56；95%CI：1.89，30.8）和凹陷情况（OR=2.58；95%CI：0.89，5.64）。结论 确定了一组临床相关的放射学特征，这些特征在临床环境中易于评分，可能有助于确定SPN参与者的肺癌风险。RSNA，2017 本文提供在线补充材料。
