In Head & neck ; h5-index 50.0
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.
Costantino Andrea, Sampieri Claudio, Pirola Francesca, De Virgilio Armando, Kim Se-Heon
2022-Dec-21
artificial intelligence, head and neck cancer, personalized medicine, robotic surgical procedures, squamous cell carcinoma
