Metabolic Subtyping of Adrenal Tumors: Prospective Multi-Center Cohort Study in Korea
| Title: | Metabolic Subtyping of Adrenal Tumors: Prospective Multi-Center Cohort Study in Korea |
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| 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 |
| Source: | Endocrinology and Metabolism, Vol 36, Iss 5, Pp 1131-1141 (2021) |
| Publisher Information: | Korean Endocrine Society, 2021. |
| Publication Year: | 2021 |
| Collection: | LCC:Diseases of the endocrine glands. Clinical endocrinology |
| Subject Terms: | steroid metabolism, supervised machine learning, adrenal neoplasms, cushing syndrome, primary hyperaldosteronism, Diseases of the endocrine glands. Clinical endocrinology, RC648-665 |
| More Details: | 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. |
| Document Type: | article |
| File Description: | electronic resource |
| Language: | English Korean |
| ISSN: | 2093-596X 2093-5978 |
| Relation: | http://www.e-enm.org/upload/pdf/enm-2021-1149.pdf; https://doaj.org/toc/2093-596X; https://doaj.org/toc/2093-5978 |
| DOI: | 10.3803/EnM.2021.1149 |
| Access URL: | https://doaj.org/article/e60005c6253447c284f8e197d933172b |
| Accession Number: | edsdoj.60005c6253447c284f8e197d933172b |
| Database: | Directory of Open Access Journals |
| ISSN: | 2093596X 20935978 |
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| DOI: | 10.3803/EnM.2021.1149 |
| Published in: | Endocrinology and Metabolism |
| Language: | English Korean |