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
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.
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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.