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.

Prediction of postoperative recurrence of oral cancer by artificial intelligence model: Multilayer perceptron.

BACKGROUND: Postoperative recurrence of oral cancer is an important factor affecting the prognosis of patients. Artificial intelligence is used to establish a machine learning model to predict the risk of postoperative recurrence of oral cancer. METHODS: The information of 387 patients with postoperative oral cancer were collected to establish the multilayer perceptron (MLP) model. The comprehensive variable model was compared with the characteristic variable model, and the MLP model was compared with other models to evaluate the sensitivity of different models in the prediction of postoperative recurrence of oral cancer. RESULTS: The overall performance of the MLP model under comprehensive variable input was the best. CONCLUSION: The MLP model has good sensitivity to predict postoperative recurrence of oral cancer, and the predictive model with variable input training is better than that with characteristic variable input.

Application of indocyanine green in near-infrared fluorescence imaging to detect necrotic bone associated with osteoradionecrosis of the jaws / 口腔疾病防治

Objective @# To investigate the application of indocyanine green in near-infrared fluorescence imaging to determine the scope of necrotic bone resection in osteoradionecrosis of the jaw and to provide a reference for clinicians@*Methods @#Eight patients with osteoradionecrosis of the jaws were enrolled. Indocyanine green was intravenously injected through the elbow vein 10 minutes before osteotomy. After conservative resection of necrotic bone lesions based on imaging results, the scope of potential dead bone resection in the area of low fluorescence intensity was gradually expanded at an initial distance of 0.3 cm. Near-infrared fluorescence imaging and fluorescence intensity determination of bone cross-section were performed before and after extended resection. Statistical differences were analyzed. All patients with osteonecrosis underwent regular follow-up to evaluate the postoperative efficacy@*Results@#Indocyanine green was injected into all 8 patients with osteoradionecrosis for near-infrared fluorescence imaging and the scans were clear; the fluorescence intensity of fresh bone wounds with an expanded mandibular resection range of (0.95 ± 0.14) cm was (226.2 ± 15.8) au, which was higher than that based on intraoperative macroscopic observation and radiological results (108.8 ± 3.4) au, (t = 20.718, P<0.001). The postoperative follow-up improvement rate of 8 patients was 87.5%.@* Conclusion @#Near-infrared fluorescence imaging with indocyanine green can assist in the successful removal of necrotic bone until fresh bleeding of the jaw wound occurs, which has important clinical value in defining the resection range of osteoradionecrosis of the jaw.