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KMID : 1141520210360051131
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Endocrinology and Metabolism
2021 Volume.36 No. 5 p.1131 ~ p.1141
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Metabolic Subtyping of Adrenal Tumors: Prospective Multi-Center Cohort Study in Korea
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Ku Eu-Jeong
Lee Chae-Lin
Shim Jae-Yoon
Lee Si-Hoon
Kim Kyoung-Ah
Kim Sang-Wan
Rhee Yu-Mie
Kim Hyo-Jeong
Lim Jung-Soo
Chung Choon-Hee
Chun Sung-Wan
Yoo Soon-Jib
Ryu Ohk-Hyun
Cho Ho-Chan
Hong A-Ram
Ahn Chang-Ho
Kim Jung-Hee
Choi Man-Ho
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Abstract
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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.
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KEYWORD
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Steroid metabolism, Supervised machine learning, Adrenal neoplasms, Cushing syndrome, Primary hyperaldosteronism
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