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

Andrea Costantino; Claudio Sampieri; Francesca Pirola; Armando De Virgilio; Se-Heon Kim

doi:10.1002/hed.27283

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

Head Neck. 2023 Mar;45(3):675-684. doi: 10.1002/hed.27283. Epub 2022 Dec 21.

Authors

Andrea Costantino^{1

2

3}, Claudio Sampieri^{3

4

5}, Francesca Pirola^{1

2

3}, Armando De Virgilio^{1

2}, Se-Heon Kim³

Affiliations

¹ Department of Biomedical Sciences, Humanitas University, Pieve Emanele (MI), Italy.
² Otorhinolaryngology Unit, IRCCS Humanitas Research Hospital, Rozzano (MI), Italy.
³ Department of Otorhinolaryngology, Yonsei University College of Medicine, Seoul, Korea.
⁴ Unit of Otorhinolaryngology - Head and Neck Surgery, IRCCS Ospedale Policlinico San Martino, Genoa, Italy.
⁵ Department of Surgical Sciences and Integrated Diagnostics (DISC), University of Genoa, Genoa, Italy.

PMID: 36541686
DOI: 10.1002/hed.27283

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.

Keywords: artificial intelligence; head and neck cancer; personalized medicine; robotic surgical procedures; squamous cell carcinoma.

MeSH terms

Carcinoma, Squamous Cell* / pathology
Head and Neck Neoplasms* / etiology
Head and Neck Neoplasms* / surgery
Humans
Machine Learning
Margins of Excision
Oropharyngeal Neoplasms* / etiology
Oropharyngeal Neoplasms* / surgery
Retrospective Studies
Robotic Surgical Procedures* / adverse effects
Treatment Outcome