Computed tomography-based radiomics nomogram model for predicting adherent perinephric fat

Aims: We investigated the predictive value of a computed tomography (CT)-based radiomics nomogram model for adherent perinephric fat (APF). Materials and Methods: The data of 220 renal carcinoma patients were collected retrospectively. Patients were divided into training (n = 153) and validation cohorts (n = 67). Radiomics features were extracted from plain CT scans, while radscore was generated by a linear combination of selected radiomics features and their weighting coefficients. Univariate logistic regression was used to screen clinical risk factors. Multivariate logistic regression combined with radscore was used to screen final predictors to construct a radiomics nomogram model. Receiver Operating Characteristic curves were used to evaluate the predictive performance of models. Results: Thirteen radiomics features associated with APF achieved a good predictive effect. The overall area under the curve (AUC) of the radscore model was 0.966, and that of the training and validation cohorts was 0.969 and 0.956, respectively. Gender, age, hypertension, size, perinephric fat thickness, Mayo Adhesive Probability score, neutrophil-to-lymphocyte ratio, monocyte-to-lymphocyte ratio, systemic inflammation response index, and systemic immune-inflammation index were risk factors for APF (P < 0.05). The overall AUC of the radiomics nomogram model based on radiomics features and clinical factors, the training, and validation cohorts was 0.981, 0.997, and 0.949, respectively. Both models had high diagnostic efficiency. However, their differential diagnostic accuracy was higher than that of the clinical model. Additionally, the radiomics nomogram model had higher AUC and specificity. Conclusions: The radiomics nomogram model is a prediction tool based on radiomics features and clinical risk factors and has high prediction ability and clinical application value for APF.