The Role of Preoperative Computed Tomography Radiomics in Distinguishing Benign and Malignant Tumors of the Parotid Gland - Xu, Yuyun; Shu, Zhenyu; Song, Ge; Liu, Yijun; Pang, Peipei; Wen, Xuehua; Gong, Xiangyang (Frontiers in Oncology, 2021)
Objective: This study aimed to develop and validate an integrated prediction model based on clinicoradiological data and CT-radiomics for differentiating between benign and malignant PG tumours via multicentre cohorts. Materials and Methods: A cohort of 87 PG tumour patients from hospital #1 who were diagnosed between January 2017 and January 2020 were used for prediction model training. A total of 378 radiomic features were extracted from a single ROI. Imaging features were extracted from plain CT and contrast-enhanced CT (CECT) images. After dimensionality reduction, a radiomics signature was constructed. A combination model was constructed by incorporating the rad-score and CT radiological features. An independent group of 38 patients from hospital #2 was used to validate the prediction models. The model performances were evaluated by receiver operating characteristic (ROC) curve analysis, and decision curve analysis (DCA) was used to evaluate the clinical effectiveness of the models. The radiomics signature model was constructed and the rad-score was calculated based on selected imaging features from plain CT and CECT images. Results: Analysis of variance and multivariable logistic regression analysis showed that location, lymph nodes, and rad-score were independent predictors of tumour malignant status. The ROC curves showed that the accuracy of the support vector machine (SVM)-based prediction model, radiomics signature, location and lymph node status in the training set was 0.854, 0.772, 0.679 and 0.632, respectively; specificity was 0.869, 0.878, 0.734 and 0.773; and sensitivity was 0.731, 0.808, 0.723 and 0.742. In the test set, the accuracy was 0.835, 0.771, 0.653 and 0.608, respectively; the specificity was 0.741, 0.889, 0.852 and 0.812; and the sensitivity was 0.818, 0.790, 0.731 and 0.716. Conclusions: The combination model based on the radiomics signature and CT radiological features is capable of evaluating the malignancy of PG tumours and can help clinicians guide clinical tumour management.