Cited 11 times in
Metabolic Subtyping of Adrenal Tumors: Prospective Multi-Center Cohort Study in Korea
DC Field | Value | Language |
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dc.contributor.author | 이유미 | - |
dc.date.accessioned | 2022-02-23T01:23:01Z | - |
dc.date.available | 2022-02-23T01:23:01Z | - |
dc.date.issued | 2021-10 | - |
dc.identifier.issn | 2093-596X | - |
dc.identifier.uri | https://ir.ymlib.yonsei.ac.kr/handle/22282913/187668 | - |
dc.description.abstract | Background: Conventional diagnostic approaches for adrenal tumors require multi-step processes, including imaging studies and dynamic hormone tests. Therefore, this study aimed to discriminate adrenal tumors from a single blood sample based on the combination of liquid chromatography-mass spectrometry (LC-MS) and machine learning algorithms in serum profiling of adrenal steroids. Methods: The LC-MS-based steroid profiling was applied to serum samples obtained from patients with nonfunctioning adenoma (NFA, n=73), Cushing's syndrome (CS, n=30), and primary aldosteronism (PA, n=40) in a prospective multicenter study of adrenal disease. The decision tree (DT), random forest (RF), and extreme gradient boost (XGBoost) were performed to categorize the subtypes of adrenal tumors. Results: The CS group showed higher serum levels of 11-deoxycortisol than the NFA group, and increased levels of tetrahydrocortisone (THE), 20α-dihydrocortisol, and 6β-hydroxycortisol were found in the PA group. However, the CS group showed lower levels of dehydroepiandrosterone (DHEA) and its sulfate derivative (DHEA-S) than both the NFA and PA groups. Patients with PA expressed higher serum 18-hydroxycortisol and DHEA but lower THE than NFA patients. The balanced accuracies of DT, RF, and XGBoost for classifying each type were 78%, 96%, and 97%, respectively. In receiver operating characteristics (ROC) analysis for CS, XGBoost, and RF showed a significantly greater diagnostic power than the DT. However, in ROC analysis for PA, only RF exhibited better diagnostic performance than DT. Conclusion: The combination of LC-MS-based steroid profiling with machine learning algorithms could be a promising one-step diagnostic approach for the classification of adrenal tumor subtypes. | - |
dc.description.statementOfResponsibility | open | - |
dc.format | application/pdf | - |
dc.language | English | - |
dc.publisher | Korean Endocrine Society | - |
dc.relation.isPartOf | Endocrinology and Metabolism(대한내분비학회지) | - |
dc.rights | CC BY-NC-ND 2.0 KR | - |
dc.title | Metabolic Subtyping of Adrenal Tumors: Prospective Multi-Center Cohort Study in Korea | - |
dc.type | Article | - |
dc.contributor.college | College of Medicine (의과대학) | - |
dc.contributor.department | Dept. of Internal Medicine (내과학교실) | - |
dc.contributor.googleauthor | Eu Jeong Ku | - |
dc.contributor.googleauthor | Chaelin Lee | - |
dc.contributor.googleauthor | Jaeyoon Shim | - |
dc.contributor.googleauthor | Sihoon Lee | - |
dc.contributor.googleauthor | Kyoung-Ah Kim | - |
dc.contributor.googleauthor | Sang Wan Kim | - |
dc.contributor.googleauthor | Yumie Rhee | - |
dc.contributor.googleauthor | Hyo-Jeong Kim | - |
dc.contributor.googleauthor | Jung Soo Lim | - |
dc.contributor.googleauthor | Choon Hee Chung | - |
dc.contributor.googleauthor | Sung Wan Chun | - |
dc.contributor.googleauthor | Soon-Jib Yoo | - |
dc.contributor.googleauthor | Ohk-Hyun Ryu | - |
dc.contributor.googleauthor | Ho Chan Cho | - |
dc.contributor.googleauthor | A Ram Hong | - |
dc.contributor.googleauthor | Chang Ho Ahn | - |
dc.contributor.googleauthor | Jung Hee Kim | - |
dc.contributor.googleauthor | Man Ho Choi | - |
dc.identifier.doi | 10.3803/EnM.2021.1149 | - |
dc.contributor.localId | A03012 | - |
dc.relation.journalcode | J00773 | - |
dc.identifier.eissn | 2093-5978 | - |
dc.identifier.pmid | 34674508 | - |
dc.subject.keyword | Adrenal neoplasms | - |
dc.subject.keyword | Cushing syndrome | - |
dc.subject.keyword | Primary hyperaldosteronism | - |
dc.subject.keyword | Steroid metabolism | - |
dc.subject.keyword | Supervised machine learning | - |
dc.contributor.alternativeName | Rhee, Yumie | - |
dc.contributor.affiliatedAuthor | 이유미 | - |
dc.citation.volume | 36 | - |
dc.citation.number | 5 | - |
dc.citation.startPage | 1131 | - |
dc.citation.endPage | 1141 | - |
dc.identifier.bibliographicCitation | Endocrinology and Metabolism(대한내분비학회지), Vol.36(5) : 1131-1141, 2021-10 | - |
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