ArticleAnnals of medicine2025
Automatic recognition of adrenal incidentalomas using a two-stage cascade network: a multicenter study.
Article in Annals of medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Radiomic Characterization of Adrenal Incidentalomas on NECT: Retrospective Exploratory Study and Systematic Review.Journal of imaging · 2026Article
- AI-enabled precision evaluation of adrenal masses: radiomics, deep learning, and explainable imaging biomarkers.Frontiers in endocrinology · 2026Review
- An interpretable machine learning framework using multi-phase computed tomography for differentiation of adrenal lipid-poor adenomas and pheochromocytomas.Frontiers in artificial intelligence · 2026Article
- Differentiation of Fat-poor and Atypical Adrenal Adenomas from Metastases: MRI-based Radiomic, Radiologic, and Radiomic-radiologic Machine Learning Models.Current medical imaging · 2026Article
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Authors and funding
17 authors.
Funding
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Abstract
backgroundThe incidence of adrenal incidentalomas (AIs) is increasing yearly. The early discovery of AIs is helpful to better manage adrenal diseases, especially subclinical primary aldosteronism, Cushing's syndrome and pheochromocytoma.
methodsIn this multicenter retrospective study, a total of 778 patients from three different medical centers were assessed. The two-stage cascade network consisted of a 3D Res-Unet network for adrenal gland segmentation and a classifier for determining the presence of AIs. The segmentation network was mainly evaluated by the Dice similarity coefficient (DSC), and the classifier was evaluated by the area under the receiver operator characteristic curve (AUC), accuracy, sensitivity, and specificity. The Delong test was used to compare the classification performance between the cascade network and manual segmentation.
resultsA total of 443 patients were randomly assigned in a 7:3 ratio, stratified sampling, to train and valid sets of the model development cohort, and 335 patients from the three centers were included in the test cohort. In the validation set, the AUC of the model for identifying left AI was 88.15%, and the AUC of the model for identifying right AI was 87.90%. There was no significant difference between model performance and manual segmentation of AIs (
conclusionsThe two-stage cascade network based on a deep learning algorithm can be used for automatic recognition of AIs in nonenhanced CT from different centers.
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