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Metabolic Subtyping of Adrenal Tumors: Prospective Multi-Center Cohort Study in Korea

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Author(s)
Eu Jeong KuChaelin LeeJaeyoon ShimSihoon LeeKyoung-Ah KimSang Wan KimYumie RheeHyo-Jeong KimJung Soo LimChoon Hee ChungSung Wan ChunSoon-Jib YooOhk-Hyun RyuHo Chan ChoA Ram HongChang Ho AhnJung Hee KimMan Ho Choi
Keimyung Author(s)
Cho, Ho Chan
Department
Dept. of Internal Medicine (내과학)
Journal Title
Endocrinol Metab
Issued Date
2021
Volume
36
Issue
5
Keyword
Steroid metabolismSupervised machine learningAdrenal neoplasmsCushing syndromePrimary hyperaldosteronism
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.
Keimyung Author(s)(Kor)
조호찬
Publisher
School of Medicine (의과대학)
Citation
Eu Jeong Ku et al. (2021). Metabolic Subtyping of Adrenal Tumors: Prospective Multi-Center Cohort Study in Korea. Endocrinol Metab, 36(5), 1131–1141. doi: 10.3803/EnM.2021.1149
Type
Article
ISSN
2093-5978
Source
https://www.e-enm.org/journal/view.php?number=2224
DOI
10.3803/EnM.2021.1149
URI
https://kumel.medlib.dsmc.or.kr/handle/2015.oak/43852
Appears in Collections:
1. School of Medicine (의과대학) > Dept. of Internal Medicine (내과학)
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