1.Metabolic Subtyping of Adrenal Tumors: Prospective Multi-Center Cohort Study in Korea
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
Endocrinology and Metabolism 2021;36(5):1131-1141
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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