Combined model of radiomics and clinical features for differentiating pneumonic-type mucinous adenocarcinoma from lobar pneumonia: An exploratory study
Affiliations 1 Department of Radiology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, China. 2 GE Healthcare, Precision Health Institution, Shanghai, China.
Purpose: The purpose of this study was to distinguish pneumonic-type mucinous adenocarcinoma from lobar pneumonia by pre-treatment CT radiological and clinical or radiological parameters. Methods: A total of 199 patients were retrospectively evaluated and assigned to either the training cohort or the validation cohort . Radiomics features were extracted from chest CT plain images. Multivariate logistic regression analysis was conducted to develop a radiomics model and a nomogram model, and t...更多
Purpose: The purpose of this study was to distinguish pneumonic-type mucinous adenocarcinoma from lobar pneumonia by pre-treatment CT radiological and clinical or radiological parameters. Methods: A total of 199 patients were retrospectively evaluated and assigned to either the training cohort or the validation cohort . Radiomics features were extracted from chest CT plain images. Multivariate logistic regression analysis was conducted to develop a radiomics model and a nomogram model, and their clinical utility was assessed. The performance of the constructed models was assessed with the receiver operating characteristic curve and the area under the curve . The clinical application value of the models was comprehensively evaluated using decision curve analysis . Results: The radiomics signature, consisting of 14 selected radiomics features, showed excellent performance in distinguishing between PTMA and LP, with an AUC of 0.90 in the training cohort and 0.88 in the validation cohort. A nomogram model was developed based on the radiomics signature and clinical features. It had a powerful discriminative ability, with the highest AUC values of 0.94 and 0.91 in the training cohort and validation cohort, respectively, which were significantly superior to the clinical model alone. There were no significant differences in calibration curves from Hosmer-Lemeshow tests between training and validation cohorts , which indicated the good performance of the nomogram model. DCA indicated that the nomogram model exhibited better performance than the clinical model. Conclusions: The nomogram model based on radiomics signatures of CT images and clinical risk factors could help to differentiate PTMA from LP, which can provide appropriate therapy decision support for clinicians, especially in situations where differential diagnosis is difficult.收起
发文期刊《Combined model of radiomics and clinical features for differentiating pneumonic-type mucinous adenocarcinoma from lobar pneumonia: An exploratory study》历年引证文献趋势图
