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Development of machine learning models for the prediction of positive surgical margins in transoral robotic surgery (TORS)
DC Field | Value | Language |
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dc.contributor.author | 김세헌 | - |
dc.date.accessioned | 2023-07-12T03:01:50Z | - |
dc.date.available | 2023-07-12T03:01:50Z | - |
dc.date.issued | 2023-03 | - |
dc.identifier.issn | 1043-3074 | - |
dc.identifier.uri | https://ir.ymlib.yonsei.ac.kr/handle/22282913/195474 | - |
dc.description.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. | - |
dc.description.statementOfResponsibility | restriction | - |
dc.language | English | - |
dc.publisher | John Wiley And Sons | - |
dc.relation.isPartOf | HEAD AND NECK-JOURNAL FOR THE SCIENCES AND SPECIALTIES OF THE HEAD AND NECK | - |
dc.rights | CC BY-NC-ND 2.0 KR | - |
dc.subject.MESH | Carcinoma, Squamous Cell* / pathology | - |
dc.subject.MESH | Head and Neck Neoplasms* / etiology | - |
dc.subject.MESH | Head and Neck Neoplasms* / surgery | - |
dc.subject.MESH | Humans | - |
dc.subject.MESH | Machine Learning | - |
dc.subject.MESH | Margins of Excision | - |
dc.subject.MESH | Oropharyngeal Neoplasms* / etiology | - |
dc.subject.MESH | Oropharyngeal Neoplasms* / surgery | - |
dc.subject.MESH | Retrospective Studies | - |
dc.subject.MESH | Robotic Surgical Procedures* / adverse effects | - |
dc.subject.MESH | Treatment Outcome | - |
dc.title | Development of machine learning models for the prediction of positive surgical margins in transoral robotic surgery (TORS) | - |
dc.type | Article | - |
dc.contributor.college | College of Medicine (의과대학) | - |
dc.contributor.department | Dept. of Otorhinolaryngology (이비인후과학교실) | - |
dc.contributor.googleauthor | Andrea Costantino | - |
dc.contributor.googleauthor | Claudio Sampieri | - |
dc.contributor.googleauthor | Francesca Pirola | - |
dc.contributor.googleauthor | Armando De Virgilio | - |
dc.contributor.googleauthor | Se-Heon Kim | - |
dc.identifier.doi | 10.1002/hed.27283 | - |
dc.contributor.localId | A00605 | - |
dc.relation.journalcode | J00963 | - |
dc.identifier.eissn | 1097-0347 | - |
dc.identifier.pmid | 36541686 | - |
dc.identifier.url | https://onlinelibrary.wiley.com/doi/10.1002/hed.27283 | - |
dc.subject.keyword | artificial intelligence | - |
dc.subject.keyword | head and neck cancer | - |
dc.subject.keyword | personalized medicine | - |
dc.subject.keyword | robotic surgical procedures | - |
dc.subject.keyword | squamous cell carcinoma | - |
dc.contributor.alternativeName | Kim, Se Heon | - |
dc.contributor.affiliatedAuthor | 김세헌 | - |
dc.citation.volume | 45 | - |
dc.citation.number | 3 | - |
dc.citation.startPage | 675 | - |
dc.citation.endPage | 684 | - |
dc.identifier.bibliographicCitation | HEAD AND NECK-JOURNAL FOR THE SCIENCES AND SPECIALTIES OF THE HEAD AND NECK, Vol.45(3) : 675-684, 2023-03 | - |
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