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- Title
Metabolic Subtyping of Adrenal Tumors: Prospective Multi-Center Cohort Study in Korea.
- Authors
Eu Jeong Ku; Chaelin Lee; Jaeyoon Shim; Sihoon Lee; Kyoung-Ah Kim; Sang Wan Kim; Yumie Rhee; Hyo-Jeong Kim; Jung Soo Lim; Choon Hee Chung; Sung Wan Chun; Soon-Jib Yoo; Ohk-Hyun Ryu; Ho Chan Cho; A Ram Hong; Chang Ho Ahn; Jung Hee Kim; Man Ho Choi
- 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.
- Subjects
SOUTH Korea; ADRENAL tumors; CUSHING'S syndrome; LIQUID chromatography-mass spectrometry; ADRENAL diseases; RECEIVER operating characteristic curves; COHORT analysis
- Publication
Endocrinology & Metabolism, 2021, Vol 36, Issue 5, p1131
- ISSN
2093-596X
- Publication type
Academic Journal
- DOI
10.3803/EnM.2021.1149