Metabolic Subtyping of Adrenal Tumors: Prospective Multi-Center Cohort Study in Korea
- Authors
- 구유정; 이채린; 심재윤; 이시훈; 김경아; 김상완; 이유미; 김효정; 임정수; 정춘희; 전성완; 유순집; 류옥현; 조호찬; 홍아람; 안창호; 김정희; 최만호
- Issue Date
- Oct-2021
- Publisher
- 대한내분비학회
- Keywords
- Steroid metabolism; Supervised machine learning; Adrenal neoplasms; Cushing syndrome; Primary hyperaldosteronism
- Citation
- Endocrinology and Metabolism, v.36, no.5, pp.1131 - 1141
- Journal Title
- Endocrinology and Metabolism
- Volume
- 36
- Number
- 5
- Start Page
- 1131
- End Page
- 1141
- URI
- https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/82931
- DOI
- 10.3803/EnM.2021.1149
- ISSN
- 2093-596X
- Abstract
- Background: Conventional diagnostic approaches for adrenal tumors require multi-step processes, including imaging studies anddynamic 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 adrenaldisease. 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 levelsof 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, andXGBoost for classifying each type were 78%, 96%, and 97%, respectively. In receiver operating characteristics (ROC) analysis forCS, 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-stepdiagnostic approach for the classification of adrenal tumor subtypes.
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