Background:Computed tomography(CT)plays a great role in characterizing and quantifying changes in lung structure and function of chronic obstructive pulmonary disease(COPD).This study aimed to explore the performance ...Background:Computed tomography(CT)plays a great role in characterizing and quantifying changes in lung structure and function of chronic obstructive pulmonary disease(COPD).This study aimed to explore the performance of CT-based whole lung radiomic in discriminating COPD patients and non-COPD patients.Methods:This retrospective study was performed on 2785 patients who underwent pulmonary function examination in 5 hospitals and were divided into non-COPD group and COPD group.The radiomic features of the whole lung volume were extracted.Least absolute shrinkage and selection operator(LASSO)logistic regression was applied for feature selection and radiomic signature construction.A radiomic nomogram was established by combining the radiomic score and clinical factors.Receiver operating characteristic(ROC)curve analysis and decision curve analysis(DCA)were used to evaluate the predictive performance of the radiomic nomogram in the training,internal validation,and independent external validation cohorts.Results:Eighteen radiomic features were collected from the whole lung volume to construct a radiomic model.The area under the curve(AUC)of the radiomic model in the training,internal,and independent external validation cohorts were 0.888[95%confidence interval(CI)0.869–0.906],0.874(95%CI 0.844–0.904),and 0.846(95%CI 0.822–0.870),respectively.All were higher than the clinical model(AUC were 0.732,0.714,and 0.777,respectively,P<0.001).DCA demonstrated that the nomogram constructed by combining radiomic score,age,sex,height,and smoking status was superior to the clinical factor model.Conclusions:The intuitive nomogram constructed by CT-based whole-lung radiomic has shown good performance and high accuracy in identifying COPD in this multicenter study.展开更多
基金supported by the National Key Research and Development Program of China(2022YFC2010002,2022YFC2010000 and 2022YFC2010005)the National Natural Science Foundation of China(82171926,81930049 and 82202140)+3 种基金the Medical Imaging Database Construction Program of National Health Commission(YXFSC2022JJSJ002)the Clinical Innovative Project of Shanghai Changzheng Hospital(2020YLCYJ-Y24)the Program of Science and Technology Commission of Shanghai Municipality(21DZ2202600)the Shanghai Sailing Program(20YF1449000).
文摘Background:Computed tomography(CT)plays a great role in characterizing and quantifying changes in lung structure and function of chronic obstructive pulmonary disease(COPD).This study aimed to explore the performance of CT-based whole lung radiomic in discriminating COPD patients and non-COPD patients.Methods:This retrospective study was performed on 2785 patients who underwent pulmonary function examination in 5 hospitals and were divided into non-COPD group and COPD group.The radiomic features of the whole lung volume were extracted.Least absolute shrinkage and selection operator(LASSO)logistic regression was applied for feature selection and radiomic signature construction.A radiomic nomogram was established by combining the radiomic score and clinical factors.Receiver operating characteristic(ROC)curve analysis and decision curve analysis(DCA)were used to evaluate the predictive performance of the radiomic nomogram in the training,internal validation,and independent external validation cohorts.Results:Eighteen radiomic features were collected from the whole lung volume to construct a radiomic model.The area under the curve(AUC)of the radiomic model in the training,internal,and independent external validation cohorts were 0.888[95%confidence interval(CI)0.869–0.906],0.874(95%CI 0.844–0.904),and 0.846(95%CI 0.822–0.870),respectively.All were higher than the clinical model(AUC were 0.732,0.714,and 0.777,respectively,P<0.001).DCA demonstrated that the nomogram constructed by combining radiomic score,age,sex,height,and smoking status was superior to the clinical factor model.Conclusions:The intuitive nomogram constructed by CT-based whole-lung radiomic has shown good performance and high accuracy in identifying COPD in this multicenter study.
