生物学检测与影像学综合评估1017例肺部结节良恶性的回顾性队列研究  

Comprehensive evaluation of benign and malignant pulmonary nodules using combined biological testing and imaging assessment in 1017 patients:A retrospective cohort study

作　　者：张蕾[1] 李子豪 李楠 程钧[2] 张锋 夏平会 吕望[2] 胡坚[2] ZHANG Lei;LI Zihao;LI Nan;CHENG Jun;ZHANG Feng;XIA Pinghui;LÜWang;HU Jian(Cadre Health Center,The First Affiliated Hospital,School of Medicine,Zhejiang University,Hangzhou,310003,P.R.China;Department of Thoracic Surgery,The First Affiliated Hospital,School of Medicine,Zhejiang University,Hangzhou,310003,P.R.China;Health Management Center,The First Affiliated Hospital,School of Medicine,Zhejiang University,Hangzhou,310003,P.R.China)

机构地区：[1]浙江大学医学院附属第一医院干部保健中心,杭州310003 [2]浙江大学医学院附属第一医院普胸外科,杭州310003 [3]浙江大学医学院附属第一医院健康管理中心,杭州310003

出　　处：《中国胸心血管外科临床杂志》2025年第1期60-66,共7页Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

基　　金：国家重点研发计划项目(2022YFC2407303);浙江省肺部肿瘤诊治技术研究中心项目(JBZX-202007)。

摘　　要：目的通过联合生物学检测与影像学评估,在大型队列基础上构建临床预测模型,以提高肺结节良恶性鉴别的准确性。方法回顾性分析浙江大学医学院附属第一医院2020年1月—2024年4月接受胸部CT和7种肺癌相关血清自身抗体(7-AABs)检测32627例肺结节患者的临床资料。通过单因素和多因素logistic回归分析筛选肺结节良恶性的独立风险因素,并构建列线图模型。通过受试者工作特征(receiver operating characteristic,ROC)曲线、校准曲线及决策曲线分析(decision curve analysis,DCA)评估模型性能。结果共纳入1017例肺结节患者。训练集共712例,其中男291例、女421例,平均年龄(58±12)岁;验证集共305例,其中男129例、女176例,平均年龄(58±13)岁。单因素ROC曲线分析显示,CT联合7-AABs检测的诊断效能[曲线下面积(area under the curve,AUC)=0.794],高于单独使用CT(AUC=0.667)或7-AABs(AUC=0.514)。多因素logistic回归分析显示,影像学结节直径、结节性质及CT联合7-AABs检测为肺结节良恶性诊断的独立预测因子,以此构建列线图预测模型。该模型在训练集和验证集的AUC值分别为0.826和0.862。DCA结果显示,该模型能够为临床决策提供较高的净收益。结论联合7-AABs与CT能够显著提高肺结节良恶性鉴别的准确性。构建的预测模型为临床决策提供了有力支持,有助于肺结节的精准诊断与治疗。Objective By combining biological detection and imaging evaluation,a clinical prediction model is constructed based on a large cohort to improve the accuracy of distinguishing between benign and malignant pulmonary nodules.Methods A retrospective analysis was conducted on the clinical data of the 32627 patients with pulmonary nodules who underwent chest CT and testing for 7 types of lung cancer-related serum autoantibodies(7-AABs)at our hospital from January 2020 to April 2024.The univariate and multivariate logistic regression models were performed to screen independent risk factors for benign and malignant pulmonary nodules,based on which a nomogram model was established.The performance of the model was evaluated using receiver operating characteristic(ROC)curves,calibration curves,and decision curve analysis(DCA).Results A total of 1017 patients with pulmonary nodules were included in the study.The training set consisted of 712 patients,including 291 males and 421 females,with a mean age of(58±12)years.The validation set included 305 patients,comprising 129 males and 176 females,with a mean age of(58±13)years.Univariate ROC curve analysis indicated that the combination of CT and 7-AABs testing achieved the highest area under the curve(AUC)value(0.794),surpassing the diagnostic efficacy of CT alone(AUC=0.667)or 7-AABs alone(AUC=0.514).Multivariate logistic regression analysis showed that radiological nodule diameter,nodule nature,and CT combined with 7-AABs detection were independent predictors,which were used to construct a nomogram prediction model.The AUC values for this model were 0.826 and 0.862 in the training and validation sets,respectively,demonstrating excellent performance in DCA.Conclusion The combination of 7-AABs with CT significantly enhances the accuracy of distinguishing between benign and malignant pulmonary nodules.The developed predictive model provides strong support for clinical decision-making and contributes to achieving precise diagnosis and treatment of pulmonary nodules.

关 键 词：肺结节 自身抗体 胸部影像学 预测模型 

分 类 号：R734.2[医药卫生—肿瘤]