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

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)1131-1141
Number of pages11
JournalEndocrinology and Metabolism
Volume36
Issue number5
DOIs
Publication statusPublished - 2021 Oct

Bibliographical note

Publisher Copyright:
© 2021 Korean Endocrine Society. All rights reserved.

All Science Journal Classification (ASJC) codes

  • Endocrinology, Diabetes and Metabolism
  • Endocrinology

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