The assay of multiple steroids by mass spectrometry coupled with chromatography, combined with data analysis using an artificial intelligence approach, has become more widely accessible in recent years. Multiple applications for this technology exist for the study of adrenocortical tumors. Taking advantage of the capacity of malignant cortical tumor secretion of non-bioactive precursors, it provides an additional diagnostic approach that can point to the nature of a tumor. These encouraging perspectives have been based to date only on pilot retrospective studies. However, this has changed in 2020 with the publication of data from the EURINE-ACT study. This very large prospective European study provided more nuanced evidence for the benefit of combining the measurement of a panel of steroids with essential imaging tools. This study also facilitated our understanding and provided more precise characterisation of autonomous steroid secretion, particularly in the case of sublinical cortisol-secreting adrenocortical adenomas. This article will focus on our current knowledge on the potential utility of mass spectrometry for diagnosis of both the nature of an adrenal tumors and their secretion.
Keywords: Adrenal steroids; Adrenal tumors; Adrenocortical carcinoma; Autonomous cortisol secreting adenoma; Machine learning; Mass spectrometry.
Copyright © 2020. Published by Elsevier Masson SAS.