Machine learning approach using radiomics features to distinguish odontogenic cysts and tumours.

Abstract

Although most odontogenic lesions in the jaw are benign, treatment varies widely depending on the nature of the lesion. This study was performed to assess the ability of a machine learning (ML) model using computed tomography (CT) and magnetic resonance imaging (MRI) radiomic features to classify odontogenic cysts and tumours. CT and MRI data from patients with odontogenic lesions including dentigerous cysts, odontogenic keratocysts, and ameloblastomas were analysed. Manual segmentation of the CT image and the apparent diffusion coefficient (ADC) map from diffusion-weighted MRI was performed to extract radiomic features. The extracted radiomic features were split into training (70%) and test (30%) sets. The random forest model was adjusted or optimized using 5-fold stratified cross-validation within the training set and assessed on a separate hold-out test set. Analysis of the CT-based ML model showed cross-validation accuracy of 0.59 and 0.60 for the training set and test set, respectively, with precision, recall, and F1 score all being 0.57. Analysis of the ADC-based ML model showed cross-validation accuracy of 0.90 and 0.94 for the training set and test set, respectively; the precision, recall, and F1 score were all 0.87. ML models, particularly when using MRI radiological features, can effectively classify odontogenic lesions.