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Αλέξανδρος Γ. Σφακιανάκης

Wednesday, December 21, 2022

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

alexandrossfakianakis shared this article with you from Inoreader

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.

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