Abstract：
The measurement of steroid hormones in blood and urine, which reflects steroid biosynthesis and metabolism, has been recognized as a valuable tool for identifying and distinguishing steroidogenic disorders. The application of mass spectrometry enables the reliable and simultaneous analysis of large panels of steroids, ushering in a new era for diagnosing adrenal diseases. However, the interpretation of complex hormone results necessitates the expertise and experience of skilled clinicians. In this scenario, machine learning techniques are gaining worldwide attention within healthcare fields. The clinical values of combining mass spectrometry-based steroid profiles analysis with machine learning models, also known as steroid metabolomics, have been investigated for identifying and discriminating adrenal disorders such as adrenocortical carcinomas, adrenocortical adenomas, and congenital adrenal hyperplasia. This promising approach is expected to lead to enhanced clinical decision-making in the field of adrenal diseases. This review will focus on the clinical performances of steroid profiling, which is measured using mass spectrometry and analyzed by machine learning techniques, in the realm of decision-making for adrenal diseases.
摘要：
血液和尿液中类固醇激素的测量，这反映了类固醇的生物合成和代谢，已被认为是识别和区分类固醇疾病的有价值的工具。质谱的应用能够可靠地同时分析大量类固醇，开创肾上腺疾病诊断的新时代。然而,复杂激素结果的解释需要熟练临床医生的专业知识和经验.在这种情况下，机器学习技术在医疗保健领域得到了全世界的关注。基于质谱的类固醇谱分析与机器学习模型相结合的临床价值，也被称为类固醇代谢组学，已被调查以识别和区分肾上腺皮质癌等肾上腺疾病，肾上腺皮质腺瘤，先天性肾上腺增生.这种有希望的方法有望导致肾上腺疾病领域的临床决策增强。这篇综述将集中在类固醇的临床表现，它使用质谱测量，并通过机器学习技术进行分析，在肾上腺疾病的决策领域。
