CBCT radiomics features combine machine learning to diagnose cystic lesions in the jaw.

Abstract

Objective: The aim of this study was to develop a radiomics model based on cone beam computed tomography (CBCT) to differentiate odontogenic cysts (OC), odontogenic keratocysts (OKC) and ameloblastomas (AB).

Methods: In this retrospective study, CBCT images were collected from 300 patients diagnosed with OC, OKC and AB who underwent histopathological diagnosis. These patients were randomly divided into training (70%) and test (30%) cohorts. Radiomics features were extracted from the images, and the optimal features were incorporated into Random Forest model, Support Vector Classifier (SVC) model, Logistic Regression model and a soft VotingClassifier based on the above three algorithms. The performance of the models was evaluated using a receiver operating characteristic (ROC) curve and the area under the curve (AUC). The optimal model among these was then used to establish the final radiomics prediction model, whose performance was evaluated using the sensitivity, accuracy, precision, specificity and F1 score in both the training cohort and the test cohort.

Results: The six optimal radiomics features were incorporated into a soft VotingClassifier. Its performance was the best overall. The AUC values of the One-vs-Rest (OvR) multiclassification strategy were AB-vs-Rest 0.963; OKC-vs-Rest 0.928; OC-vs-Rest 0.919 in the training cohort and AB-vs-Rest 0.814; OKC-vs-Rest 0.781; OC-vs-Rest 0.849 in the test cohort. The overall accuracy of the model in the training cohort was 0.757, and in the test cohort was 0.711.

Conclusions: The VotingClassifier model demonstrated the ability of the CBCT radiomics to distinguish the multiple types of diseases (OC, OKC and AB) in the jaw and may have the potential to diagnose accurately under non-invasive conditions.