Navigation-based endoscopic enucleation (NBEE) of large mandibular cystic lesions involving the ramus.

OBJECTIVE: The aim of this study was to present an innovative surgical protocol, navigation-based endoscopic enucleation (NBEE) for the treatment of large mandibular cystic lesions involving the mandibular ramus. METHODS: Twelve patients who presented with a large mandibular cystic lesion involving the mandibular ramus were enrolled in this study. Preoperative planning and intraoperative navigation were performed in all 12 patients. RESULTS: All patients in this study were treated with navigation-based endoscopic enucleation successfully. The follow-up period ranged from 7 to 10 months. Bone regenerated was found in all patients postoperatively. Three patients experienced temporary mandibular nerve palsy, and all relieved within 2 months. No pathological bone fracture was found during surgery. CONCLUSIONS: The use of navigation-based endoscopic enucleation (NBEE) for the treatment of large mandibular cystic lesions involving the ramus proved to be an effective method for complete and precise enucleation of the cystic lesion that also preserved the surrounding tissue.

Machine Learning Model Based on Radiomics for Preoperative Differentiation of Jaw Cystic Lesions.

OBJECTIVE: This study aims to use machine learning techniques together with radiomics methods to build a preoperative predictive diagnostic model from spiral computed tomography (CT) images. The model is intended for the differential diagnosis of common jaw cystic lesions. STUDY DESIGN: Retrospective, case-control study. SETTING: This retrospective study was conducted at Sun Yat-sen Memorial Hospital of Sun Yat-sen University (Guangzhou, Guangdong, China). All the data used to build the predictive diagnostic model were collected from 160 patients, who were treated at the Department of Oral and Maxillofacial Surgery at Sun Yat-sen Memorial Hospital of Sun Yat-sen University between 2019 and 2023. METHODS: We included a total of 160 patients in this study. We extracted 107 radiomic features from each patient's CT scan images. After a feature selection process, we chose 15 of these radiomic features to construct the predictive diagnostic model. RESULTS: Among the preoperative predictive diagnostic models built using 3 different machine learning methods (support vector machine, random forest [RF], and multivariate logistic regression), the RF model showed the best predictive performance. It demonstrated a sensitivity of 0.923, a specificity of 0.643, an accuracy of 0.825, and an area under the receiver operating characteristic curve of 0.810. CONCLUSION: The preoperative predictive model, based on spiral CT radiomics and machine learning algorithms, shows promising differential diagnostic capabilities. For common jaw cystic lesions, this predictive model has potential clinical application value, providing a scientific reference for treatment decisions.