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2 "Adrenal neoplasm"
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Original Article
Adrenal Gland
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
Endocrinol Metab. 2021;36(5):1131-1141.   Published online October 21, 2021
DOI: https://doi.org/10.3803/EnM.2021.1149
  • 11,773 View
  • 258 Download
  • 20 Web of Science
  • 19 Crossref
AbstractAbstract PDFSupplementary MaterialPubReader   ePub   
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.

Citations

Citations to this article as recorded by  
  • Clinical Utility of Salivary Steroid Profiling for the Differential Diagnosis of Adrenal Diseases
    Kyoung Yeun Jung, Soyeon Park, Chaelin Lee, Eu Jeong Ku, Sihoon Lee, Kyoung-Ah Kim, Sang Wan Kim, Yumie Rhee, Jung Soo Lim, Choon Hee Chung, Sung Wan Chun, Soon-Jib Yoo, Ohk-Hyun Ryu, Hochan Cho, A Ram Hong, Hyo-Jeong Kim, Man Ho Choi, Jung Hee Kim
    The Journal of Clinical Endocrinology & Metabolism.2026; 111(3): e827.     CrossRef
  • The role of arachidonic acid metabolites in the subtype classification and pathogenesis of primary aldosteronism
    Mei You, Li Li, Xintao Tan, Xiaoli Liu, Lijuan Wang, Tingbing Cao, Bowen Wang, Min Liu, Yue Gao, Qing Zhou, Ruomei Yang, Weihua Lan, Peng Gao, Zhiming Zhu, Zhencheng Yan
    iScience.2026; 29(2): 114598.     CrossRef
  • Usefulness of steroid profiling in the diagnosis of primary Aldosteronism a systematic review
    Paola Parra Ramírez, Alberto Artiles Medina, Patricia Martín Rojas-Marcos, Gregori Casals, Manuel Morales, Imanol Madrid, Felicia A. Hanzu, Marta Araujo-Castro
    Endocrine.2026;[Epub]     CrossRef
  • An Analysis of Post-Adrenalectomy Dynamics in MACS (Mild Autonomous Cortisol Secretion)-Positive Adrenal Tumours: The Biomarkers and Clinical Impact
    Alexandra-Ioana Trandafir, Mara Carsote, Alexandru-Florin Florescu
    Journal of Clinical Medicine.2025; 14(15): 5217.     CrossRef
  • Machine learning in endocrinology: current applications and future perspectives
    Magdalena Kamińska, Małgorzata Trofimiuk-Müldner, Grzegorz Sokołowski, Alicja Hubalewska-Dydejczyk
    Endocrine.2025; 90(2): 357.     CrossRef
  • Dexamethasone Suppression Testing in Patients with Adrenal Incidentalomas with/Without Mild Autonomous Cortisol Secretion: Spectrum of Cortisol Cutoffs and Additional Assays (An Updated Analysis)
    Alexandra-Ioana Trandafir, Mara Carsote
    Biomedicines.2025; 13(9): 2169.     CrossRef
  • Is evaluation of cortisol after dexamethasone suppression test enough? Analysis of steroid profile after dexamethasone suppression test using tandem mass spectrometry
    Tomas Brutvan, Marcela Kotasova, Adela Krausova, Jarmila Krizova, Otakar Psenicka, Jan Sevcik, Martin Sevcik, Hana Vitkova, Jana Jezkova
    Endocrine Connections.2025;[Epub]     CrossRef
  • Treating Primary Aldosteronism-Induced Hypertension: Novel Approaches and Future Outlooks
    Nathan Mullen, James Curneen, Padraig T Donlon, Punit Prakash, Irina Bancos, Mark Gurnell, Michael C Dennedy
    Endocrine Reviews.2024; 45(1): 125.     CrossRef
  • Steroid profiling in adrenal disease
    Danni Mu, Dandan Sun, Xia Qian, Xiaoli Ma, Ling Qiu, Xinqi Cheng, Songlin Yu
    Clinica Chimica Acta.2024; 553: 117749.     CrossRef
  • Plasma Steroid Profiling Combined With Machine Learning for the Differential Diagnosis in Mild Autonomous Cortisol Secretion From Nonfunctioning Adenoma in Patients With Adrenal Incidentalomas
    Danni Mu, Xia Qian, Yichen Ma, Xi Wang, Yumeng Gao, Xiaoli Ma, Shaowei Xie, Lian Hou, Qi Zhang, Fang Zhao, Liangyu Xia, Liling Lin, Ling Qiu, Jie Wu, Songlin Yu, Xinqi Cheng
    Endocrine Practice.2024; 30(7): 647.     CrossRef
  • Mild autonomous cortisol secretion: pathophysiology, comorbidities and management approaches
    Alessandro Prete, Irina Bancos
    Nature Reviews Endocrinology.2024; 20(8): 460.     CrossRef
  • Plasma Steroid Profiling Between Patients With and Without Diabetes Mellitus in Nonfunctioning Adrenal Incidentalomas
    Yui Nakano, Maki Yokomoto-Umakoshi, Kohta Nakatani, Hironobu Umakoshi, Hiroshi Nakao, Masamichi Fujita, Hiroki Kaneko, Norifusa Iwahashi, Tatsuki Ogasawara, Tazuru Fukumoto, Yayoi Matsuda, Ryuichi Sakamoto, Yoshihiro Izumi, Takeshi Bamba, Yoshihiro Ogawa
    Journal of the Endocrine Society.2024;[Epub]     CrossRef
  • Peak-Based Machine Learning for Plastic Type Classification in Time-of-Flight Secondary Ion Mass Spectrometry
    Jin Gyeong Son, Hyun Kyong Shon, Ji-Eun Kim, In−Ho Lee, Tae Geol Lee
    Journal of the American Society for Mass Spectrometry.2024; 35(12): 3107.     CrossRef
  • Serum and hair steroid profiles in patients with nonfunctioning pituitary adenoma undergoing surgery: A prospective observational study
    Seung Shin Park, Yong Hwy Kim, Ho Kang, Chang Ho Ahn, Dong Jun Byun, Man Ho Choi, Jung Hee Kim
    The Journal of Steroid Biochemistry and Molecular Biology.2023; 230: 106276.     CrossRef
  • Recent Updates on the Management of Adrenal Incidentalomas
    Seung Shin Park, Jung Hee Kim
    Endocrinology and Metabolism.2023; 38(4): 373.     CrossRef
  • LC-MS based simultaneous profiling of adrenal hormones of steroids, catecholamines, and metanephrines
    Jongsung Noh, Chaelin Lee, Jung Hee Kim, Seung Woon Myung, Man Ho Choi
    Journal of Lipid Research.2023; 64(11): 100453.     CrossRef
  • 2023 Korean Endocrine Society Consensus Guidelines for the Diagnosis and Management of Primary Aldosteronism
    Jeonghoon Ha, Jung Hwan Park, Kyoung Jin Kim, Jung Hee Kim, Kyong Yeun Jung, Jeongmin Lee, Jong Han Choi, Seung Hun Lee, Namki Hong, Jung Soo Lim, Byung Kwan Park, Jung-Han Kim, Kyeong Cheon Jung, Jooyoung Cho, Mi-kyung Kim, Choon Hee Chung
    Endocrinology and Metabolism.2023; 38(6): 597.     CrossRef
  • Toward Systems-Level Metabolic Analysis in Endocrine Disorders and Cancer
    Aliya Lakhani, Da Hyun Kang, Yea Eun Kang, Junyoung O. Park
    Endocrinology and Metabolism.2023; 38(6): 619.     CrossRef
  • Prevalence and Characteristics of Adrenal Tumors in an Unselected Screening Population
    Ying Jing, Jinbo Hu, Rong Luo, Yun Mao, Zhixiao Luo, Mingjun Zhang, Jun Yang, Ying Song, Zhengping Feng, Zhihong Wang, Qingfeng Cheng, Linqiang Ma, Yi Yang, Li Zhong, Zhipeng Du, Yue Wang, Ting Luo, Wenwen He, Yue Sun, Fajin Lv, Qifu Li, Shumin Yang
    Annals of Internal Medicine.2022; 175(10): 1383.     CrossRef
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Case Report
A Case of Adrenocortical Oncocytoma.
Seong Jin Lee, Ho Gwon Lee, Cheol young Park, In Kyung Jeong, Eun Gyung Hong, Gi Weon Oh, Hyeon Kyu Kim, Doo Man Kim, Jae Myung Yoo, Sung Hee Ihm, Moon Gi Choi, Hyung Joon Yoo, Sung Woo Park
J Korean Endocr Soc. 2004;19(1):82-89.   Published online February 1, 2004
  • 1,797 View
  • 18 Download
AbstractAbstract PDF
Oncocytomas are neoplasms, histologically are composed of epithelial cells, with abundant, acidophilic and granular cytoplasm. Electron microscopic studies of oncocytomas have shown that the cytoplasm of oncocytes is packed with mitochondria. The adrenal gland is a very rare anatomical site for oncocytomas, and to the best of our knowledge, only thirty-six cases of adrenal oncocytomas have been described. Herein, a case of a large adrenal mass in a forty-year-old man, which was incidentally detected by abdominal ultrasonography, is presented. This patient demonstrated no clinical manifestation associated with adrenal hyperfunction. Hormonal studies showed no abnormal findings, except for a mild elevation of the 24-hour urinary VMA level. Abdominal computed tomography with enhancement revealed a large, well-defined left adrenal mass, measuring 5.0x.2 x.0cm. The patient underwent a left adrenalectomy, and a light microscopic examination confirmed an adrenocortical oncocytoma, with characteristic oncocytes and polygonal, abundant, eosinophilic and granular cytoplasm. The tumor cells were positive for cytokeratin and vimentin as well as S-100, but negative for chromogranin on immunohistochemical staining. An electron microscopic examination demonstrated closely packed mitochondria, containing intramitochondrial inclusions. After surgery, there was no evidence of a recurrent or distant metastatic disease at the 5 month follow-up. In summary, an extremely rare case of a man with an adrenocortical oncocytoma is reported, which was confirmed by histological examinations, including electron microscopy.
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