Development and validation of CT‐based radiomics nomogram for the classification of benign parotid gland tumors

摘要

Abstract Purpose Accurate preoperative diagnosis of parotid tumor is essential for the formulation of optimal individualized surgical plans. The study aims to investigate the diagnostic performance of radiomics nomogram based on contrast‐enhanced computed tomography (CT) images in the differentiation of the two most common benign parotid gland tumors. Methods One hundred and ten patients with parotid gland tumors including 76 with pleomorphic adenoma (PA) and 34 with adenolymphoma (AL) confirmed by histopathology were included in this study. Radiomics features were extracted from contrast‐enhanced CT images of venous phase. A radiomics model was established and a radiomics score (Rad‐score) was calculated. Clinical factors including clinical data and CT features were assessed to build a clinical factor model. Finally, a nomogram incorporating the Rad‐score and independent clinical factors was constructed. Receiver operator characteristics (ROC) curve was generated and the area under the ROC curve (AUC) was calculated to quantify the discriminative performance of each model on both the training and validation cohorts. Decision curve analysis (DCA) was conducted to evaluate the clinical usefulness of each model. Results The radiomics model showed good discrimination in the training cohort [AUC, 0.89; 95% confidence interval (CI), 0.80–0.98] and validation cohort (AUC, 0.89; 95% CI, 0.77–1.00). The radiomics nomogram showed excellent discrimination in the training cohort (AUC, 0.98; 95% CI, 0.96–1.00) and validation cohort (AUC, 0.95; 95% CI, 0.88–1.00) and displayed better discrimination efficacy compared with the clinical factor model (AUC, 0.93; 95% CI, 0.88–0.99) in the training cohort (p?