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

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.

SUBMITTER: Ku EJ 

PROVIDER: S-EPMC8566125 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature