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LASSO-Based Machine Learning Algorithm for Prediction of Lymph Node Metastasis in T1 Colorectal Cancer

DC Field Value Language
dc.contributor.author강정현-
dc.contributor.author김남규-
dc.contributor.author김임경-
dc.contributor.author김호근-
dc.contributor.author백승혁-
dc.contributor.author이강영-
dc.contributor.author이혜선-
dc.contributor.author최윤정-
dc.date.accessioned2021-09-29T01:18:26Z-
dc.date.available2021-09-29T01:18:26Z-
dc.date.issued2021-07-
dc.identifier.issn1598-2998-
dc.identifier.urihttps://ir.ymlib.yonsei.ac.kr/handle/22282913/184299-
dc.description.abstractPurpose: The role of tumor-infiltrating lymphocytes (TILs) in predicting lymph node metastasis (LNM) in patients with T1 colorectal cancer (CRC) remains unclear. Furthermore, clinical utility of a machine learning-based approach has not been widely studied. Materials and methods: Immunohistochemistry for TILs against CD3, CD8, and forkhead box P3 in both center and invasive margin of the tumor were performed using surgically resected T1 CRC slides. Three hundred and sixteen patients were enrolled and categorized into training (n=221) and validation (n=95) sets via random sampling. Using clinicopathologic variables including TILs, the least absolute shrinkage and selection operator (LASSO) regression model was applied for variable selection and predictive signature building in the training set. The predictive accuracy of our model and the Japanese criteria were compared using area under the receiver operating characteristic (AUROC), net reclassification improvement (NRI)/integrated discrimination improvement (IDI), and decision curve analysis (DCA) in the validation set. Results: LNM was detected in 29 (13.1%) and 12 (12.6%) patients in training and validation sets, respectively. Nine variables were selected and used to generate the LASSO model. Its performance was similar in training and validation sets (AUROC, 0.795 vs. 0.765; p=0.747). In the validation set, the LASSO model showed better outcomes in predicting LNM than Japanese criteria, as measured by AUROC (0.765 vs. 0.518, p=0.003) and NRI (0.447, p=0.039)/IDI (0.121, p=0.034). DCA showed positive net benefits in using our model. Conclusion: Our LASSO model incorporating histopathologic parameters and TILs showed superior performance compared to conventional Japanese criteria in predicting LNM in patients with T1 CRC.-
dc.description.statementOfResponsibilityopen-
dc.languageEnglish, Korean-
dc.publisherOfficial journal of Korean Cancer Association-
dc.relation.isPartOfCANCER RESEARCH AND TREATMENT-
dc.rightsCC BY-NC-ND 2.0 KR-
dc.titleLASSO-Based Machine Learning Algorithm for Prediction of Lymph Node Metastasis in T1 Colorectal Cancer-
dc.typeArticle-
dc.contributor.collegeCollege of Medicine (의과대학)-
dc.contributor.departmentDept. of Surgery (외과학교실)-
dc.contributor.googleauthorJeonghyun Kang-
dc.contributor.googleauthorYoon Jung Choi-
dc.contributor.googleauthorIm-Kyung Kim-
dc.contributor.googleauthorHye Sun Lee-
dc.contributor.googleauthorHogeun Kim-
dc.contributor.googleauthorSeung Hyuk Baik-
dc.contributor.googleauthorNam Kyu Kim-
dc.contributor.googleauthorKang Young Lee-
dc.identifier.doi10.4143/crt.2020.974-
dc.contributor.localIdA00080-
dc.contributor.localIdA00353-
dc.contributor.localIdA00851-
dc.contributor.localIdA01183-
dc.contributor.localIdA01827-
dc.contributor.localIdA02640-
dc.contributor.localIdA03312-
dc.contributor.localIdA05985-
dc.relation.journalcodeJ00453-
dc.identifier.eissn2005-9256-
dc.identifier.pmid33421980-
dc.subject.keywordLASSO-
dc.subject.keywordLymph node-
dc.subject.keywordMachine learning-
dc.subject.keywordT1 colorectal cancer-
dc.subject.keywordTumor-infiltrating lymphocytes-
dc.contributor.alternativeNameKang, Jeonghyun-
dc.contributor.affiliatedAuthor강정현-
dc.contributor.affiliatedAuthor김남규-
dc.contributor.affiliatedAuthor김임경-
dc.contributor.affiliatedAuthor김호근-
dc.contributor.affiliatedAuthor백승혁-
dc.contributor.affiliatedAuthor이강영-
dc.contributor.affiliatedAuthor이혜선-
dc.contributor.affiliatedAuthor최윤정-
dc.citation.volume53-
dc.citation.number3-
dc.citation.startPage773-
dc.citation.endPage783-
dc.identifier.bibliographicCitationCANCER RESEARCH AND TREATMENT, Vol.53(3) : 773-783, 2021-07-
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Pathology (병리학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Surgery (외과학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Yonsei Biomedical Research Center (연세의생명연구원) > 1. Journal Papers

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