Abstract: Objective: To investigate the application value of CBCT radiomics features in preoperative diagnosis of ameloblastoma of the jaw. Methods: A retrospective analysis of the CBCT data of 104 patients pathologically diagnosed as cystic lesions of the jaw was conducted, including 45 cases of ameloblastoma and 59 cases of odontogenic cyst. The radiomics features of the lesion area were extracted by manually drawing lesion areas. Radiomics labels were established by feature screening, and support vector machine, random forest, and logistic regression classifier models were constructed. A comprehensive model was established by combining conventional radiologic characteristics. The data of the training set and validation set were used for training and evaluating, and the area under the curve (AUC) and accuracy rate (ACC) were used to evaluate the diagnostic performance of the model. Results: In the testing set, the accuracy of the three models based on radiomics features was all 81.3%, and the AUC values of the radiomics models were 0.849 (95%CI:0.707-0.991), 0.865 (95%CI:0.734-0.996), and 0.849 (95%CI:0.703-0.995), respectively. Combined with conventional radiologic features, the accuracy was 81.3%, 81.3%, and 84.4%, and the AUC values of the comprehensive models were 0.877 (95%CI:0.751-1.000), 0.873 (95%CI:0.747-0.999), and 0.889 (95%CI:0.765-1.000), respectively. However, neither between three models nor the radiomics and comprehensive models had a significant difference (P>0.05). Conclusion: The prediction model based on CBCT radiomics features has high diagnostic performance in preoperative diagnosis of ameloblastoma, which can be used to assist diagnosis of ameloblastoma and guide the selection of a treatment plan.

Key words: ameloblastoma, odontogenic cyst, radiomics