Prognostic model construction
REGRESSION
We used univariate analysis and multivariate regression to select optimal clinical factors. Then these significant factors were used to construct the clinical model by logistic regression. Lastly, these clinical independent risk factors and Radscore were utilized to build a combined model in the training cohort. The discriminative power of the model was evaluated by the area under the ROC curve with 95% confidence intervals. In addition, the accuracy, sensitivity, specificity, positive-predictive value (PPV), and negative-predictive value (NPV) for the clinical model, radiomics score, and combined model, were calculated in both cohorts. Then, a nomogram was established based on the most efficient model. Furthermore, the prediction accuracy of this nomogram model was explored using the calibration curve. And the decision curve analysis (DCA) was applied to assess the actual usefulness of the nomogram.Figure Workflow of the study: 