Development of machine learning models for the prediction of positive surgical margins in transoral robotic surgery (TORS).

Abstract

To develop machine learning (ML) models for predicting positive margins in patients undergoing transoral robotic surgery (TORS). 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. 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. ML algorithms can identify patients with low risk of positive margins and therefore amenable to TORS.