Endocrinol Metab.  2021 Oct;36(5):1131-1141. 10.3803/EnM.2021.1149.

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

• 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

• KMID: 2521960
• DOI: http://doi.org/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.

Keyword

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

Figure

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  Fig. 1 Comparative serum levels of adrenal steroids between patients with nonfunctioning adenoma (NFA), Cushing’s syndrome (CS), and primary aldosteronism (PA) after adjustment for age and gender. 17α-OHP, 17α-hydroxypregnenolone; 11-deoxyF, 11-deoxycortisol; THE, tetrahydrocortisone; 20α-DHF, 20α-dihydrocortisol; 18-OHF, 18-hydroxycortisol; THF, tetrahydrocortisol; 6β-OHF, 6β-hydroxycortisol; DHEA, dehydroepiandrosterone; DHEA-S, DHEA-sulfate; Preg-S, pregnenolone sulfate.

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  Fig. 2 The decision tree analysis for classification of nonfunctioning adenoma (NFA), Cushing’s syndrome (CS), and primary aldosteronism (PA) groups in subjects with adrenal tumors using the multiple steroid panels. (A) The significant features of steroid panels, (B) the confusion matrix for decision tree analysis, (C) the diagnostic performance of decision tree analysis in each group. Accuracy, 0.78 (95% confidence interval, 0.71 to 0.85); P<1.6×1011. 18-OHF, 18-hydroxycortisol; DHEA, dehydroepiandrosterone; THE, tetrahydrocortisone; 11-deoxyF, 11-deoxycortisol; PPV, positive predictive value; NPV, negative predictive value.

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  Fig. 3 The random forest analysis for discriminating nonfunctioning adenoma (NFA), Cushing’s syndrome (CS), and primary aldosteronism (PA) groups in subjects with adrenal tumors using the multiple steroid panels. (A) The random forest analysis for multiple steroids with the importance of each steroid displayed on the right y-axis, (B) the confusion matrix for random forest model, (C) the diagnostic performance of random forest model in each group. Accuracy, 0.96 (95% confidence interval, 0.91 to 0.98); P<2×10−16. THE, tetrahydrocortisone; 18-OHF, 18-hydroxycortisol; DHEA, dehydroepiandrosterone; DHEA-S, DHEA-sulfate; 20α-DHF, 20α-dihydrocortisol; 6β-OHF, 6β-hydroxycortisol; THF, tetrahydrocortisol; 11-deoxyF, 11-deoxycortisol; Preg-S, pregnenolone sulfate; 17α-OHP, 17α-hydroxypregnenolone; PPV, positive predictive value; NPV, negative predictive value.

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  Fig. 4 The extreme gradient boost (XGBoost) algorithm for discriminating nonfunctioning adenoma (NFA), Cushing’s syndrome (CS), and primary aldosteronism (PA) groups in subjects with adrenal tumors using the multiple steroid panels. (A) The distributed gradient boosting framework for multiple steroids with the importance of each steroid displayed on the right y-axis, (B) the confusion matrix for XGBoost algorithm, (C) the diagnostic performance of XGBoost algorithm in each group. Accuracy, 0.97 (95% confidence interval, 0.92 to 0.99); P<2×10−16. THE, tetrahydrocortisone; 18-OHF, 18-hydroxycortisol; THF, tetrahydrocortisol; 20α-DHF, 20α-dihydrocortisol; 11-deoxyF, 11-deoxycortisol; DHEA, dehydroepiandrosterone; Preg-S, pregnenolone sulfate; DHEA-S, DHEA-sulfate; 6β-OHF, 6β-hydroxycortisol; 17α-OHP, 17α-hydroxypregnenolone; PPV, positive predictive value; NPV, negative predictive value.

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  Fig. 5 The receiver operating characteristic (ROC) curves for validating the discrimination power of the decision tree, random forest, and the extreme gradient boost (XGBoost) algorithm in (A) Cushing’s syndrome and (B) primary aldosteronism. AUC, area under the curve; CI, confidence interval.

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