Development of machine learning models for the prediction of positive surgical margins in transoral robotic surgery (TORS). Issue 3 (21st December 2022) - Costantino, Andrea; Sampieri, Claudio; Pirola, Francesca; De Virgilio, Armando; Kim, Se‐Heon
Abstract: Purpose: To develop machine learning (ML) models for predicting positive margins in patients undergoing transoral robotic surgery (TORS). Methods: Data from 453 patients with laryngeal, hypopharyngeal, and oropharyngeal squamous cell carcinoma were retrospectively collected at a tertiary referral center to train ( n = 316) and validate ( n = 137) six two‐class supervised ML models employing 14 variables available pre‐operatively. Results: The accuracy of the six ML models ranged between 0.67 and 0.75, while the measured AUC between 0.68 and 0.75. The ML algorithms showed high specificity (range: 0.75–0.89) and low sensitivity (range: 0.26–0.64) in detecting patients with positive margins after TORS. NPV was higher (range: 0.73–0.83) compared to PPV (range: 0.45–0.63). T classification and tumor site were the most important predictors of positive surgical margins. Conclusions: ML algorithms can identify patients with low risk of positive margins and therefore amenable to TORS.