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Clinical decision support algorithm based on machine learning to assess the clinical response to anti-programmed death-1 therapy in patients with non-small-cell lung cancer

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dc.contributor.author김창곤-
dc.contributor.author김혜련-
dc.contributor.author안병철-
dc.contributor.author임선민-
dc.contributor.author조병철-
dc.contributor.author표경호-
dc.contributor.author홍민희-
dc.contributor.author김혜련-
dc.contributor.author안병철-
dc.contributor.author임선민-
dc.contributor.author조병철-
dc.contributor.author표경호-
dc.contributor.author홍민희-
dc.contributor.author김재환-
dc.contributor.author김영섭-
dc.contributor.author변영선-
dc.contributor.author허성구-
dc.date.accessioned2021-09-29T02:07:35Z-
dc.date.available2021-09-29T02:07:35Z-
dc.date.issued2021-08-
dc.identifier.issn0959-8049-
dc.identifier.urihttps://ir.ymlib.yonsei.ac.kr/handle/22282913/184726-
dc.description.abstractObjective: Anti-programmed death (PD)-1 therapy confers sustainable clinical benefits for patients with non-small-cell lung cancer (NSCLC), but only some patients respond to the treatment. Various clinical characteristics, including the PD-ligand 1 (PD-L1) level, are related to the anti-PD-1 response; however, none of these can independently serve as predictive biomarkers. Herein, we established a machine learning (ML)-based clinical decision support algorithm to predict the anti-PD-1 response by comprehensively combining the clinical information. Materials and methods: We collected clinical data, including patient characteristics, mutations and laboratory findings, from the electronic medical records of 142 patients with NSCLC treated with anti-PD-1 therapy; these were analysed for the clinical outcome as the discovery set. Nineteen clinically meaningful features were used in supervised ML algorithms, including LightGBM, XGBoost, multilayer neural network, ridge regression and linear discriminant analysis, to predict anti-PD-1 responses. Based on each ML algorithm's prediction performance, the optimal ML was selected and validated in an independent validation set of PD-1 inhibitor-treated patients. Results: Several factors, including PD-L1 expression, tumour burden and neutrophil-to-lymphocyte ratio, could independently predict the anti-PD-1 response in the discovery set. ML platforms based on the LightGBM algorithm using 19 clinical features showed more significant prediction performance (area under the curve [AUC] 0.788) than on individual clinical features and traditional multivariate logistic regression (AUC 0.759). Conclusion: Collectively, our LightGBM algorithm offers a clinical decision support model to predict the anti-PD-1 response in patients with NSCLC.-
dc.description.statementOfResponsibilityrestriction-
dc.languageEnglish-
dc.publisherElsevier Science Ltd-
dc.relation.isPartOfEUROPEAN JOURNAL OF CANCER-
dc.rightsCC BY-NC-ND 2.0 KR-
dc.titleClinical decision support algorithm based on machine learning to assess the clinical response to anti-programmed death-1 therapy in patients with non-small-cell lung cancer-
dc.typeArticle-
dc.contributor.collegeCollege of Medicine (의과대학)-
dc.contributor.departmentDept. of Internal Medicine (내과학교실)-
dc.contributor.googleauthorBeung-Chul Ahn-
dc.contributor.googleauthorJea-Woo So-
dc.contributor.googleauthorChun-Bong Synn-
dc.contributor.googleauthorTae Hyung Kim-
dc.contributor.googleauthorJae Hwan Kim-
dc.contributor.googleauthorYeongseon Byeon-
dc.contributor.googleauthorYoung Seob Kim-
dc.contributor.googleauthorSeong Gu Heo-
dc.contributor.googleauthorSan-Duk Yang-
dc.contributor.googleauthorMi Ran Yun-
dc.contributor.googleauthorSangbin Lim-
dc.contributor.googleauthorSu-Jin Choi-
dc.contributor.googleauthorWongeun Lee-
dc.contributor.googleauthorDong Kwon Kim-
dc.contributor.googleauthorEun Ji Lee-
dc.contributor.googleauthorSeul Lee-
dc.contributor.googleauthorDoo-Jae Lee-
dc.contributor.googleauthorChang Gon Kim-
dc.contributor.googleauthorSun Min Lim-
dc.contributor.googleauthorMin Hee Hong-
dc.contributor.googleauthorByoung Chul Cho-
dc.contributor.googleauthorKyoung-Ho Pyo-
dc.contributor.googleauthorHye Ryun Kim-
dc.identifier.doi10.1016/j.ejca.2021.05.019-
dc.contributor.localIdA05991-
dc.contributor.localIdA01166-
dc.contributor.localIdA05556-
dc.contributor.localIdA03369-
dc.contributor.localIdA03822-
dc.contributor.localIdA04809-
dc.contributor.localIdA04393-
dc.contributor.localIdA01166-
dc.contributor.localIdA05556-
dc.contributor.localIdA03369-
dc.contributor.localIdA03822-
dc.contributor.localIdA04809-
dc.contributor.localIdA04393-
dc.relation.journalcodeJ00809-
dc.identifier.eissn1879-0852-
dc.identifier.pmid34182269-
dc.identifier.urlhttps://www.sciencedirect.com/science/article/pii/S0959804921003282-
dc.subject.keywordAnti–programmed death-1-
dc.subject.keywordClinical decision support system-
dc.subject.keywordImmune checkpoint inhibitor-
dc.subject.keywordLung cancer-
dc.subject.keywordMachine learning-
dc.subject.keywordNon-invasive biomarker-
dc.contributor.alternativeNameKim, Chang Gon-
dc.contributor.affiliatedAuthor김창곤-
dc.contributor.affiliatedAuthor김혜련-
dc.contributor.affiliatedAuthor안병철-
dc.contributor.affiliatedAuthor임선민-
dc.contributor.affiliatedAuthor조병철-
dc.contributor.affiliatedAuthor표경호-
dc.contributor.affiliatedAuthor홍민희-
dc.contributor.affiliatedAuthor김혜련-
dc.contributor.affiliatedAuthor안병철-
dc.contributor.affiliatedAuthor임선민-
dc.contributor.affiliatedAuthor조병철-
dc.contributor.affiliatedAuthor표경호-
dc.contributor.affiliatedAuthor홍민희-
dc.citation.volume153-
dc.citation.startPage179-
dc.citation.endPage189-
dc.identifier.bibliographicCitationEUROPEAN JOURNAL OF CANCER, Vol.153 : 179-189, 2021-08-
Appears in Collections:
1. College of Medicine (의과대학) > BioMedical Science Institute (의생명과학부) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Internal Medicine (내과학교실) > 1. Journal Papers

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