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Oncologic Outcomes in Nipple-sparing Mastectomy with Immediate Reconstruction and Total Mastectomy with Immediate Reconstruction in Women with Breast Cancer: A Machine-Learning Analysis

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dc.contributor.author김승일-
dc.contributor.author김지예-
dc.contributor.author박병우-
dc.contributor.author박세호-
dc.contributor.author박형석-
dc.date.accessioned2023-11-28T03:25:50Z-
dc.date.available2023-11-28T03:25:50Z-
dc.date.issued2023-11-
dc.identifier.issn1068-9265-
dc.identifier.urihttps://ir.ymlib.yonsei.ac.kr/handle/22282913/196805-
dc.description.abstractBackground: This study used a single-institution cohort, the Severance dataset, validated the results by using the surveillance, epidemiology, and end results (SEER) database, adjusted with propensity-score matching (PSM), and analyzed by using a machine learning method. To determine whether the 5-year, disease-free survival (DFS) and overall survival (OS) of patients undergoing nipple-sparing mastectomy (NSM) with immediate breast reconstruction (IBR) are not inferior to those of women treated with total mastectomy/skin-sparing mastectomy (TM/SSM). Methods: The Severance dataset enrolled 611 patients with early, invasive breast cancer from 2010 to 2017. The SEER dataset contained data for 485,245 patients undergoing TM and 14,770 patients undergoing NSM between 2000 and 2018. All patients underwent mastectomy and IBR. Intraoperative, frozen-section biopsy for the retro-areolar tissue was performed in the NSM group. The SEER dataset was extracted by using operation types, including TM/SSM and NSM. The primary outcome was DFS for the Severance dataset and OS for the SEER dataset. PSM analysis was applied. Survival outcomes were analyzed by using the Kaplan-Meier method and Cox proportional hazard (Cox PH) regression model. We implemented XGBSE to predict mortality with high accuracy and evaluated model prediction performance using a concordance index. The final model inspected the impact of relevant predictors on the model output using shapley additive explanation (SHAP) values. Results: In the Severance dataset, 151 patients underwent NSM with IBR and 460 patients underwent TM/SSM with IBR. No significant differences were found between the groups. In multivariate analysis, NSM was not associated with reduced oncologic outcomes. The same results were observed in PSM analysis. In the SEER dataset, according to the SHAP values, the individual feature contribution suggested that AJCC stage ranks first. Analyses from the two datasets confirmed no impact on survival outcomes from the two surgical methods. Conclusions: NSM with IBR is a safe and feasible procedure in terms of oncologic outcomes. Analysis using machine learning methods can be successfully applied to identify significant risk factors for oncologic outcomes. © 2023. Society of Surgical Oncology.-
dc.description.statementOfResponsibilityrestriction-
dc.languageEnglish-
dc.publisherSpringer-
dc.relation.isPartOfANNALS OF SURGICAL ONCOLOGY-
dc.rightsCC BY-NC-ND 2.0 KR-
dc.subject.MESHBreast Neoplasms* / pathology-
dc.subject.MESHFemale-
dc.subject.MESHHumans-
dc.subject.MESHMammaplasty* / methods-
dc.subject.MESHMastectomy / methods-
dc.subject.MESHMastectomy, Simple-
dc.subject.MESHMastectomy, Subcutaneous*-
dc.subject.MESHNipples / pathology-
dc.subject.MESHNipples / surgery-
dc.subject.MESHRetrospective Studies-
dc.titleOncologic Outcomes in Nipple-sparing Mastectomy with Immediate Reconstruction and Total Mastectomy with Immediate Reconstruction in Women with Breast Cancer: A Machine-Learning Analysis-
dc.typeArticle-
dc.contributor.collegeCollege of Medicine (의과대학)-
dc.contributor.departmentDept. of Surgery (외과학교실)-
dc.contributor.googleauthorJun-Ho Cho-
dc.contributor.googleauthorJung Mi Park-
dc.contributor.googleauthorHyung Seok Park-
dc.contributor.googleauthorHye Jin Kim-
dc.contributor.googleauthorDong-Min Shin-
dc.contributor.googleauthorJee Ye Kim-
dc.contributor.googleauthorSeho Park-
dc.contributor.googleauthorSeung Il Kim-
dc.contributor.googleauthorByeong-Woo Park-
dc.identifier.doi10.1245/s10434-023-13963-w-
dc.contributor.localIdA00658-
dc.contributor.localIdA00984-
dc.contributor.localIdA01475-
dc.contributor.localIdA01524-
dc.contributor.localIdA01753-
dc.relation.journalcodeJ00179-
dc.identifier.eissn1534-4681-
dc.identifier.pmid37587360-
dc.identifier.urlhttps://link.springer.com/article/10.1245/s10434-023-13963-w-
dc.contributor.alternativeNameKim, Seung Il-
dc.contributor.affiliatedAuthor김승일-
dc.contributor.affiliatedAuthor김지예-
dc.contributor.affiliatedAuthor박병우-
dc.contributor.affiliatedAuthor박세호-
dc.contributor.affiliatedAuthor박형석-
dc.citation.volume30-
dc.citation.number12-
dc.citation.startPage7281-
dc.citation.endPage7290-
dc.identifier.bibliographicCitationANNALS OF SURGICAL ONCOLOGY, Vol.30(12) : 7281-7290, 2023-11-
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Surgery (외과학교실) > 1. Journal Papers

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