A CT-based radiomics nomogram for differentiating ovarian cystadenomas and endometriotic cysts

•Radiomics features can differentiate ovarian cystadenomas and endometriotic cysts. •The radiomoics nomogram show the best predictive value. •The radiomics nomogram can enhance the diagnostic accuracy of radiologists. AIM To construct and validate a computed tomography (CT)-based radiomics nomogram integrating radiomics signature and clinical factors to distinguish ovarian cystadenomas and endometriotic cysts. MATERIALS AND METHODS A total of 287 patients with ovarian cystadenomas (n=196) or endometriotic cysts (n=91) were divided randomly into a training cohort (n=200) and a validation cohort (n=87). Radiomics features based on the portal venous phase of CT images were extracted by PyRadiomics. The least absolute shrinkage and selection operation regression was applied to select the significant features and develop the radiomics signature. A radiomics score (rad-score) was calculated. The clinical model was built by the significant clinical factors. Multivariate logistic regression analysis was employed to construct the radiomics nomogram based on significant clinical factors and rad-score. The diagnostic performances of the radiomics nomogram, radiomics signature, and clinical model were evaluated and compared in the training and validation cohorts. Diagnostic confusion matrices of these models were calculated for the validation cohort and compared with those of the radiologists. RESULTS Seventeen radiomics features from CT images were used to build the radiomics signature. The radiomics nomogram incorporating cancer antigen 125 (CA-125) level and rad-score showed the best performance in both the training and validation cohorts with AUCs of 0.925 (95% confidence interval [CI]: 0.885–0.965), and 0.942 (95% CI: 0.891–0.993), respectively. The accuracy of radiomics nomogram in the confusion matrix outperformed the radiologists. CONCLUSIONS The radiomics nomogram performed well for differentiating ovarian cystadenomas and endometriotic cysts, and may help in clinical decision-making process. To construct and validate a computed tomography (CT)-based radiomics nomogram integrating radiomics signature and clinical factors to distinguish ovarian cystadenomas and endometriotic cysts. A total of 287 patients with ovarian cystadenomas (n=196) or endometriotic cysts (n=91) were divided randomly into a training cohort (n=200) and a validation cohort (n=87). Radiomics features based on the portal venous phase of CT images were extracted by PyRadiomics. The least absolute shrinkage and selection operation regression was applied to select the significant features and develop the radiomics signature. A radiomics score (rad-score) was calculated. The clinical model was built by the significant clinical factors. Multivariate logistic regression analysis was employed to construct the radiomics nomogram based on significant clinical factors and rad-score. The diagnostic performances of the radiomics nomogram, radiomics signature, and clinical model were evaluated and compared in the training and validation cohorts. Diagnostic confusion matrices of these models were calculated for the validation cohort and compared with those of the radiologists. Seventeen radiomics features from CT images were used to build the radiomics signature. The radiomics nomogram incorporating cancer antigen 125 (CA-125) level and rad-score showed the best performance in both the training and validation cohorts with AUCs of 0.925 (95% confidence interval [CI]: 0.885–0.965), and 0.942 (95% CI: 0.891–0.993), respectively. The accuracy of radiomics nomogram in the confusion matrix outperformed the radiologists. The radiomics nomogram performed well for differentiating ovarian cystadenomas and endometriotic cysts, and may help in clinical decision-making process.