Metabolic Subtyping of Adrenal Tumors: Prospective Multi-Center Cohort Study in Korea
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 spectrometr...
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| Format: | Article |
| Language: | English |
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Korean Endocrine Society
2021-10-01
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| Series: | Endocrinology and Metabolism |
| Subjects: | |
| Online Access: | http://www.e-enm.org/upload/pdf/enm-2021-1149.pdf |
| _version_ | 1818343350637428736 |
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| author | 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 |
| author_facet | 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 |
| author_sort | Eu Jeong Ku |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-13T16:29:11Z |
| format | Article |
| id | doaj.art-e60005c6253447c284f8e197d933172b |
| institution | Directory Open Access Journal |
| issn | 2093-596X 2093-5978 |
| language | English |
| last_indexed | 2024-12-13T16:29:11Z |
| publishDate | 2021-10-01 |
| publisher | Korean Endocrine Society |
| record_format | Article |
| series | Endocrinology and Metabolism |
| spelling | doaj.art-e60005c6253447c284f8e197d933172b2022-12-21T23:38:32ZengKorean Endocrine SocietyEndocrinology and Metabolism2093-596X2093-59782021-10-013651131114110.3803/EnM.2021.11492224Metabolic Subtyping of Adrenal Tumors: Prospective Multi-Center Cohort Study in KoreaEu Jeong Ku0Chaelin Lee1Jaeyoon Shim2Sihoon Lee3Kyoung-Ah Kim4Sang Wan Kim5Yumie Rhee6Hyo-Jeong Kim7Jung Soo Lim8Choon Hee Chung9Sung Wan Chun10Soon-Jib Yoo11Ohk-Hyun Ryu12Ho Chan Cho13A Ram Hong14Chang Ho Ahn15Jung Hee Kim16Man Ho Choi17 Department of Internal Medicine, Chungbuk National University Hospital, Chungbuk National University College of Medicine, Cheongju, Korea Molecular Recognition Research Center, Korea Institute of Science and Technology, Seoul, Korea Molecular Recognition Research Center, Korea Institute of Science and Technology, Seoul, Korea Department of Internal Medicine, Gachon University College of Medicine, Incheon, Korea Department of Internal Medicine, Dongguk University Ilsan Hospital, Dongguk University College of Medicine, Goyang, Korea Department of Internal Medicine, Seoul Metropolitan Government Seoul National University Boramae Medical Center, Seoul National University College of Medicine, Seoul, Korea Department of Internal Medicine, Yonsei University College of Medicine, Seoul, Korea Department of Internal Medicine, Nowon Eulji Medical Center, Eulji University, Seoul, Korea Department of Internal Medicine, Yonsei University Wonju College of Medicine, Wonju, Korea Department of Internal Medicine, Yonsei University Wonju College of Medicine, Wonju, Korea Department of Internal Medicine, Soonchunhyang University Cheonan Hospital, Soonchunhyang University College of Medicine, Cheonan, Korea Division of Endocrinology and Metabolism, Department of Internal Medicine, Bucheon St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Bucheon, Korea Department of Internal Medicine, Hallym University Chuncheon Sacred Heart Hospital, Hallym University College of Medicine, Chuncheon, Korea Department of Internal Medicine, Keimyung University School of Medicine, Daegu, Korea Department of Internal Medicine, Chonnam National University Medical School, Gwangju, Korea Department of Internal Medicine, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Korea Department of Internal Medicine, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Korea Molecular Recognition Research Center, Korea Institute of Science and Technology, Seoul, KoreaBackground 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.http://www.e-enm.org/upload/pdf/enm-2021-1149.pdfsteroid metabolismsupervised machine learningadrenal neoplasmscushing syndromeprimary hyperaldosteronism |
| spellingShingle | 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 Metabolic Subtyping of Adrenal Tumors: Prospective Multi-Center Cohort Study in Korea Endocrinology and Metabolism steroid metabolism supervised machine learning adrenal neoplasms cushing syndrome primary hyperaldosteronism |
| title | Metabolic Subtyping of Adrenal Tumors: Prospective Multi-Center Cohort Study in Korea |
| title_full | Metabolic Subtyping of Adrenal Tumors: Prospective Multi-Center Cohort Study in Korea |
| title_fullStr | Metabolic Subtyping of Adrenal Tumors: Prospective Multi-Center Cohort Study in Korea |
| title_full_unstemmed | Metabolic Subtyping of Adrenal Tumors: Prospective Multi-Center Cohort Study in Korea |
| title_short | Metabolic Subtyping of Adrenal Tumors: Prospective Multi-Center Cohort Study in Korea |
| title_sort | metabolic subtyping of adrenal tumors prospective multi center cohort study in korea |
| topic | steroid metabolism supervised machine learning adrenal neoplasms cushing syndrome primary hyperaldosteronism |
| url | http://www.e-enm.org/upload/pdf/enm-2021-1149.pdf |
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