Objective This study aimed to explore the differential application of pleomorphic adenoma (PA) and adenolymphoma (AL) in parotid gland on the basis of the morphological characteristics of CT plain scan and gender combined with radiomic model. Methods The morphological features of 56 cases of PA and 49 cases of AL confirmed by pathology were analyzed retrospectively. The morphological characteristics of shape, boundary, cystic degeneration, multiple occurrence, and location of the tumors were observed and analyzed. Six kinds of radiologic features of tumors in CT plain scan images were extracted and analyzed, including gray histogram, absolute gradient, gray-level co-occurrence matrix, autoregressive model, gray-level run length matrix, and wavelet transform. They were used to screen the statistically significant radiomic characteristic parameters between groups. The radial basis function kernel (RBFK), polynomial kernel (PK), and linear kernel (LK) of the support vector machine (SVM) classification model were established for the screened radiomic features. A joint model combined with gender and morphological features was also established. The receiver operator characteristic curve was used to evaluate the diagnostic efficiency of classification models and joint model. Results A total of 12 features were screened out from 287 radiomic feature parameters to establish classification models. The classification model with RBFK as core had the highest diagnostic efficiency, and the corresponding sensitivity, specificity, accuracy, and area under the curve (AUC) were 90.2%, 82.5%, 89.6%, and 0.883, respectively. PA was more common in women, whereas AL was more common in men. AL was more prone to multiple and cystic degeneration than PA (P<0.05). No significant difference was observed between the two groups in terms of boundary, shape, and the location of the tumor (P>0.05). The sensitivity, specificity, accuracy, and AUC of the combined model based on the radiomic characteristics of RBFK, gender, and morphological characteristics (multiple and cystic changes) were 95.1%, 87.6%, 92.8%, and 0.963, respectively. Conclusion The combination of morphological characteristics based on radiomics characteristics, gender and morphological characteristics could effectively distinguish PA and AL before operation.
