基于HRCT亚厘米肺磨玻璃结节良恶性预测模型建立与验证  

Establishment and Verification of Benign and Malignant Prediction Model of Subcentimeter Pulmonary Ground Glass Nodules Based on HRCT

作　　者：陈郑玮 王高祥 吴汉然 吴明胜 吴显宁 徐美青 解明然 Zhengwei CHEN;Gaoxiang WANG;Hanran WU;Mingsheng WU;Xianning WU;Meiqing XU;Mingran XIE(Wannan Medical College,Wuhu 241001,China;Department of Thoracic Surgery,The First Affiliated Hospital of University of Science and Technology of China,Hefei 230001,China)

机构地区：[1]皖南医学院,芜湖241001 [2]中国科学技术大学附属第一医院胸外科,合肥230001

出　　处：《中国肺癌杂志》2023年第5期377-385,共9页Chinese Journal of Lung Cancer

基　　金：国家自然科学基金(No.81973643);安徽省重点研发与开发计划项目(No.202004j070200017);安徽省高等学校教学研究项目(No.2020xjyxm089)资助。

摘　　要：背景与目的亚厘米磨玻璃结节(subcentimeter ground glass nodules,SGGNs)术前精准定性是临床工作的难点,目前关于SGGNs良恶性预测模型临床研究较少。本研究旨在基于高分辨率计算机断层扫描(high resolu-tion computed tomography,HRCT)影像学特征与患者一般临床资料,帮助鉴别SGGNs良恶性病变,并构建风险预测模型。方法回顾性分析2020年8月-2021年12月于中国科学技术大学附属第一医院接受手术切除并经组织学证实的483例SGGNs患者的临床资料,按7:3随机分配原则分为训练集(n=338)和验证集(n=145),根据术后组织学病理分为腺癌组和良性病变组。采用单因素分析和多因素Logistic回归分析独立危险因素和模型构建,受试者工作特征(receiver operator characteristic,ROC)曲线评估模型区分度,校准曲线评估模型的一致性,绘制临床决策曲线分析(decision curve analysis,DCA)评估模型的临床应用价值,并将验证集数据代入进行外部验证。结果多因素Logistic分析筛选出患者的年龄、血管征、分叶征、结节体积和mean-CT值是SGGNs的独立危险因素,基于多因素分析结果构建列线图预测模型,ROC曲线下面积为0.836(95%CI:0.794-0.879),最大约登指数所对应的临界值为0.483,此时敏感度为76.6%,特异度为80.1%,阳性预测值为86.5%,阴性预测值为68.7%。Bootstrap法抽样1,000次,校准曲线图预测的SGGNs良恶性风险与实际发生风险高度一致。DCA显示当预测模型概率的预概率为0.2-0.9,患者表现为正的净收益。结论通过术前病史及术前HRCT检查指标确立SGGNs良恶性风险预测模型具有较好的预测效能与临床应用价值,列线图的可视化展现形式有助于筛选出SGGNs的高危人群,为临床决策提供支持。Background and objective Pre-operative accuracy of subcentimeter ground glass nodules(SGGNs)is a difficult problem in clinical practice,but there are few clinical studies on the benign and malignant prediction model of SGGNs.The aim of this study was to help identify benign and malignant lesions of SGGNs based on the imaging features of high resolution computed tomography(HRCT)and the general clinical data of patients,and to build a risk prediction model.Methods This study retrospectively analyzed the clinical data of 483 patients with SGGNs who underwent surgical resection and were confirmed by histology from the First Affiliated Hospital of University of Science and Technology of China from Au-gust 2020 to December 2021.The patients were divided into the training set(n=338)and the validation set(n=145)according to 7:3 random assignment.According to the postoperative histology,they were divided into adenocarcinoma group and benign lesion group.The independent risk factors and models were analyzed by univariate analysis and multivariate Logistic regression.The receiver operator characteristic(ROC)curve was constructed to evaluate the model differentiation,and the calibration curve was used to evaluate the model consistency.The clinical application value of the decision curve analysis(DCA)evalua-tion model was drawn,and the validation set data was substituted for external verification.Results Multivariate Logistic analy-sis screened out patients'age,vascular sign,lobular sign,nodule volume and mean-CT value as independent risk factors for SGGNs.Based on the results of multivariate analysis,Nomogram prediction model was constructed,and the area under ROC curve was 0.836(95%CI:0.794-0.879).The critical value corresponding to the maximum approximate entry index was 0.483.The sensitivity was 76.6%,and the specificity was 80.1%.The positive predictive value was 86.5%,and the negative predictive value was 68.7%.The benign and malignant risk of SGGNs predicted by the calibration curve was highly consistent with the actua

关 键 词：高分辨率计算机断层扫描 亚厘米肺磨玻璃结节 良恶性病变 预测模型 列线图 

分 类 号：R73[医药卫生—肿瘤]