Abstract
BACKGROUND: Diagnosis and treatment of Carcinoma Buccal Mucosa is dependent on imaging techniques such as contrast-enhanced computed tomography (CECT), which is primarily used to stage the disease and predict resectability. Recent studies have identified a 'Borderline Resectable' subgroup in these patients who benefit with induction chemotherapy prior to surgery. MATERIALS AND METHODS: This prospective observational pilot study, from April 2022 to March 2024, curated a dataset of 256 preoperative CECT scans of patients with stage IVA and IVB squamous cell carcinomas of the buccal mucosa, which were integrated into a novel artificial intelligence-based machine learning model designed to predict resectability for upfront surgery. We developed a Convolutional Neural Network-based predictive model to distinguish between "Borderline Resectable" and "Resectable Upfront" disease. RESULTS: The model displayed high performance with an overall F1 score of 0.8, efficiently stratifying tumors based on resectability. Integration with Gradio allowed access to run the model on a local server, which allowed real-time execution of the model. The area under the curve (AUC) for the training set was 0.9652, with 50.39% sensitivity, 96.65% specificity, 65.75% negative predictive value, and 94.20% positive predictive value. The validation set had an AUC of 0.9735, along with 98.40% specificity, 67.96% negative predictive value, 55.73% sensitivity, and 97.33% positive predictive value. CONCLUSION: This study represents a first step toward the use of artificial intelligence to aid in the treatment to of patients with carcinoma buccal mucosa, allowing us to avoid the possibility of margin positive resection with upfront surgery.