%0 Journal Article
%T Application of machine learning in the preoperative radiomic diagnosis of ameloblastoma and odontogenic keratocyst based on cone-beam CT.
%A Song Y
%A Ma S
%A Mao B
%A Xu K
%A Liu Y
%A Ma J
%A Jia J
%J Dentomaxillofac Radiol
%V 53
%N 5
%D 2024 Jun 28
%M 38627247
%F 3.525
%R 10.1093/dmfr/twae016
%X <B>OBJECTIVE: </B>Preoperative diagnosis of oral ameloblastoma (AME) and odontogenic keratocyst (OKC) has been a challenge in dentistry. This study uses radiomics approaches and machine learning (ML) algorithms to characterize cone-beam CT (CBCT) image features for the preoperative differential diagnosis of AME and OKC and compares ML algorithms to expert radiologists to validate performance.<BR><B>METHODS: </B>We retrospectively collected the data of 326 patients with AME and OKC, where all diagnoses were confirmed by histopathologic tests. A total of 348 features were selected to train six ML models for differential diagnosis by a 5-fold cross-validation. We then compared the performance of ML-based diagnoses to those of radiologists.<BR><B>RESULTS: </B>Among the six ML models, XGBoost was effective in distinguishing AME and OKC in CBCT images, with its classification performance outperforming the other models. The mean precision, recall, accuracy, F1-score, and area under the curve (AUC) were 0.900, 0.807, 0.843, 0.841, and 0.872, respectively. Compared to the diagnostics by radiologists, ML-based radiomic diagnostics performed better.<BR><B>CONCLUSIONS: </B>Radiomic-based ML algorithms allow CBCT images of AME and OKC to be distinguished accurately, facilitating the preoperative differential diagnosis of AME and OKC.<BR><B>CONCLUSIONS: </B>ML and radiomic approaches with high-resolution CBCT images provide new insights into the differential diagnosis of AME and OKC.