摘要
目的探讨基于临床特征和术前胸部CT影像特征构建的模型预测肺癌合并慢性阻塞性肺疾病(COPD)的价值。方法回顾性分析2014年6月至2021年3月在海军军医大学第二附属医院就诊且经病理确诊的444例肺癌患者的临床(年龄、性别、吸烟史、吸烟指数等)及影像资料(病灶大小、位置、密度、分叶征等)。其中男279例、女165例,年龄23~85岁。444例患者以7∶3的比例使用python中的random函数随机分为训练集(310例)和内部测试集(134例),并根据肺功能检查将患者进一步分为肺癌合并COPD组和肺癌非COPD组。首先将单因素分析中2组间差异有统计学意义的临床特征纳入二元logistic回归分析,筛选出预测肺癌合并COPD的独立影响因子构建临床特征模型。使用最小绝对收缩和选择算子对影像特征进行特征筛选,并用5次留P交叉验证法判断其可靠性,构建影像特征标签。临床特征联合影像特征标签建立综合模型。使用受试者操作特征(ROC)曲线和决策曲线分析(DCA)评估各个模型的预测能力和临床使用价值。各模型预测肺癌合并COPD的曲线下面积(AUC)比较采用DeLong检验。结果训练集中肺癌合并COPD组182例,肺癌非COPD组128例,综合模型预测肺癌合并COPD的AUC为0.89,临床模型为0.82,影像特征标签为0.85。测试集中肺癌合并COPD组78例,肺癌非COPD组56例,综合模型预测肺癌合并COPD的AUC为0.85,临床模型为0.77,影像特征标签为0.83。影像特征模型与临床特征模型的AUC差异无统计学意义(Z=1.40,P=0.163),综合模型与临床特征模型、影像特征模型的AUC的差异有统计学意义(Z分别为-4.01、-2.57,P分别为0.010、<0.001)。DCA示综合模型的净收益最大。结论利用CT的影像学特征和临床特征构建的综合诊断模型能有效地预测肺癌合并COPD。
Objective To assess the effectiveness of a model created using clinical features and preoperative chest CT imaging features in predicting the chronic obstructive pulmonary disease(COPD)among patients diagnosed with lung cancer.Methods A retrospective analysis was conducted on clinical(age,gender,smoking history,smoking index,etc.)and imaging(lesion size,location,density,lobulation sign,etc.)data from 444 lung cancer patients confirmed by pathology at the Second Affiliated Hospital of Naval Medical University between June 2014 and March 2021.These patients were randomly divided into a training set(310 patients)and an internal test set(134 patients)using a 7∶3 ratio through the random function in Python.Based on the results of pulmonary function tests,the patients were further categorized into two groups:lung cancer combined with COPD and lung cancer non-COPD.Initially,univariate analysis was performed to identify statistically significant differences in clinical characteristics between the two groups.The variables showing significance were then included in the logistic regression analysis to determine the independent factors predicting lung cancer combined with COPD,thereby constructing the clinical model.The image features underwent a filtering process using the minimum absolute value convergence and selection operator.The reliability of these features was assessed through leave-P groups-out cross-validation repeated five times.Subsequently,a radiological model was developed.Finally,a combined model was established by combining the radiological signature with the clinical features.Receiver operating characteristic(ROC)curves and decision curve analysis(DCA)curves were plotted to evaluate the predictive capability and clinical applicability of the model.The area under the curve(AUC)for each model in predicting lung cancer combined with COPD was compared using the DeLong test.Results In the training set,there were 182 cases in the lung cancer combined with COPD group and 128 cases in the lung cancer non-COPD grou
作者
周陶胡
涂文婷
周秀秀
黄文君
刘甜
冯岩
张含笑
望云
管宇
蒋欣昂
董鹏
刘士远
范丽
Zhou Taohu;Tu Wenting;Zhou Xiuxiu;Huang Wenjun;Liu Tian;Feng Yan;Zhang Hanxiao;Wang Yun;Guan Yu;Jiang Xin′ang;Dong Peng;Liu Shiyuan;Fan Li(School of Medical Imaging,Weifang Medical University,Weifang 261000,China;Department of Radiology,Changzheng Hospital,Naval Medical University,Shanghai 200003,China;School of Medical Imaging,Xuzhou Medical University,Xuzhou 221000,China)
出处
《中华放射学杂志》
CAS
CSCD
北大核心
2023年第8期889-896,共8页
Chinese Journal of Radiology
基金
国家自然科学基金(81930049,82171926,81871321)
科技部重点研发计划(2022YFC2010002,2022YFC2010000)
国家卫生健康委放射影像数据库建设项目(YXFSC2022JJSJ002)
上海长征医院创新型临床研究项目(2020YLCYJ-Y24)
上海科委技术标准项目(21DZ2202600)。
关键词
肺肿瘤
肺疾病
慢性阻塞性
体层摄影术
X线计算机
模型构建
效能评价
Lung neoplasms
Pulmonary disease,chronic obstructive
Tomography,X-ray computed
Model development
Performace evaluation
