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Ku EJ, Lee C, Shim J, et al. Metabolic Subtyping of Adrenal Tumors: Prospective Multi-Center Cohort Study in Korea. Endocrinol Metab (Seoul). 2021;36(5):1131-1141doi: 10.3803/EnM.2021.1149.
Ku, E. J., Lee, C., Shim, J., Lee, S., Kim, K. A., Kim, S. W., Rhee, Y., Kim, H. J., Lim, J. S., Chung, C. H., Chun, S. W., Yoo, S. J., Ryu, O. H., Cho, H. C., Hong, A. R., Ahn, C. H., Kim, J. H., & Choi, M. H. (2021). Metabolic Subtyping of Adrenal Tumors: Prospective Multi-Center Cohort Study in Korea. Endocrinology and metabolism (Seoul, Korea), 36(5), 1131-1141. https://doi.org/10.3803/EnM.2021.1149
Ku, Eu Jeong, et al. "Metabolic Subtyping of Adrenal Tumors: Prospective Multi-Center Cohort Study in Korea." Endocrinology and metabolism (Seoul, Korea) vol. 36,5 (2021): 1131-1141. doi: https://doi.org/10.3803/EnM.2021.1149
Ku EJ, Lee C, Shim J, Lee S, Kim KA, Kim SW, Rhee Y, Kim HJ, Lim JS, Chung CH, Chun SW, Yoo SJ, Ryu OH, Cho HC, Hong AR, Ahn CH, Kim JH, Choi MH. Metabolic Subtyping of Adrenal Tumors: Prospective Multi-Center Cohort Study in Korea. Endocrinol Metab (Seoul). 2021 Oct;36(5):1131-1141. doi: 10.3803/EnM.2021.1149. Epub 2021 Oct 21. PMID: 34674508; PMCID: PMC8566125.
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Endocrinol Metab (Seoul). 2021 Oct;36(5):1131-1141. doi: 10.3803/EnM.2021.1149. Epub 2021 Oct 21.

Metabolic Subtyping of Adrenal Tumors: Prospective Multi-Center Cohort Study in Korea.

Endocrinology and metabolism (Seoul, 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

Affiliations

  1. Department of Internal Medicine, Chungbuk National University Hospital, Chungbuk National University College of Medicine, Cheongju, Korea.
  2. Molecular Recognition Research Center, Korea Institute of Science and Technology, Seoul, Korea.
  3. Department of Internal Medicine, Gachon University College of Medicine, Incheon, Korea.
  4. Department of Internal Medicine, Dongguk University Ilsan Hospital, Dongguk University College of Medicine, Goyang, Korea.
  5. Department of Internal Medicine, Seoul Metropolitan Government Seoul National University Boramae Medical Center, Seoul National University College of Medicine, Seoul, Korea.
  6. Department of Internal Medicine, Yonsei University College of Medicine, Seoul, Korea.
  7. Department of Internal Medicine, Nowon Eulji Medical Center, Eulji University, Seoul, Korea.
  8. Department of Internal Medicine, Yonsei University Wonju College of Medicine, Wonju, Korea.
  9. Department of Internal Medicine, Soonchunhyang University Cheonan Hospital, Soonchunhyang University College of Medicine, Cheonan, Korea.
  10. Division of Endocrinology and Metabolism, Department of Internal Medicine, Bucheon St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Bucheon, Korea.
  11. Department of Internal Medicine, Hallym University Chuncheon Sacred Heart Hospital, Hallym University College of Medicine, Chuncheon, Korea.
  12. Department of Internal Medicine, Keimyung University School of Medicine, Daegu, Korea.
  13. Department of Internal Medicine, Chonnam National University Medical School, Gwangju, Korea.
  14. Department of Internal Medicine, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Korea.
  15. Department of Internal Medicine, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Korea.

PMID: 34674508 PMCID: PMC8566125 DOI: 10.3803/EnM.2021.1149

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.

Keywords: Adrenal neoplasms; Cushing syndrome; Primary hyperaldosteronism; Steroid metabolism; Supervised machine learning

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