BACKGROUND Pulmonary tuberculosis(TB)and lung cancer(LC)are common diseases with a high incidence and similar symptoms,which may be misdiagnosed by radiologists,thus delaying the best treatment opportunity for patient...BACKGROUND Pulmonary tuberculosis(TB)and lung cancer(LC)are common diseases with a high incidence and similar symptoms,which may be misdiagnosed by radiologists,thus delaying the best treatment opportunity for patients.AIM To develop and validate radiomics methods for distinguishing pulmonary TB from LC based on computed tomography(CT)images.METHODS We enrolled 478 patients(January 2012 to October 2018),who underwent preoperative CT screening.Radiomics features were extracted and selected from the CT data to establish a logistic regression model.A radiomics nomogram model was constructed,with the receiver operating characteristic,decision and calibration curves plotted to evaluate the discriminative performance.RESULTS Radiomics features extracted from lesions with 4 mm radial dilation distances outside the lesion showed the best discriminative performance.The radiomics nomogram model exhibited good discrimination,with an area under the curve of 0.914(sensitivity=0.890,specificity=0.796)in the training cohort,and 0.900(sensitivity=0.788,specificity=0.907)in the validation cohort.The decision curve analysis revealed that the constructed nomogram had clinical usefulness.CONCLUSION These proposed radiomic methods can be used as a noninvasive tool for differentiation of TB and LC based on preoperative CT data.展开更多
基金Supported by Youth Science and Technology Innovation Leader Support Project,No.RC170497Shenyang Municipal Science and Technology Project,No.F16-206-9-23+5 种基金Natural Science Foundation of Liaoning Province of China,No.201602450National Key R&D Program of Ministry of Science and Technology of China,No.2016YFC1303002National Natural Science Foundation of China,No.81872363Major Technology Plan Project of Shenyang,No.17-230-9-07Supporting Fund for Big data in Health Care,No.HMB2019031012018 Key Research and Guidance Project of Liaoning Province,No.2018225038.
文摘BACKGROUND Pulmonary tuberculosis(TB)and lung cancer(LC)are common diseases with a high incidence and similar symptoms,which may be misdiagnosed by radiologists,thus delaying the best treatment opportunity for patients.AIM To develop and validate radiomics methods for distinguishing pulmonary TB from LC based on computed tomography(CT)images.METHODS We enrolled 478 patients(January 2012 to October 2018),who underwent preoperative CT screening.Radiomics features were extracted and selected from the CT data to establish a logistic regression model.A radiomics nomogram model was constructed,with the receiver operating characteristic,decision and calibration curves plotted to evaluate the discriminative performance.RESULTS Radiomics features extracted from lesions with 4 mm radial dilation distances outside the lesion showed the best discriminative performance.The radiomics nomogram model exhibited good discrimination,with an area under the curve of 0.914(sensitivity=0.890,specificity=0.796)in the training cohort,and 0.900(sensitivity=0.788,specificity=0.907)in the validation cohort.The decision curve analysis revealed that the constructed nomogram had clinical usefulness.CONCLUSION These proposed radiomic methods can be used as a noninvasive tool for differentiation of TB and LC based on preoperative CT data.
