ArticleCurrent medical imaging2026
Differentiation of Fat-poor and Atypical Adrenal Adenomas from Metastases: MRI-based Radiomic, Radiologic, and Radiomic-radiologic Machine Learning Models.
Article in Current medical imaging, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- AI-enabled precision evaluation of adrenal masses: radiomics, deep learning, and explainable imaging biomarkers.Frontiers in endocrinology · 2026Review
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionThe accurate differentiation of fat-poor and atypical adrenal adenomas from metastases remains a diagnostic challenge. This study aimed to evaluate the predictive value of MRI-based radiomic, radiologic, and combined radiomic-radiologic machine learning (ML) models.
methodsThis single-center retrospective study included 37 patients with 44 adrenal masses (19 adenomas; 25 metastases). Data were split into training and testing sets (2:1). To expand the training set, data augmentation was performed by multiple sampling (56 labeled slices from 30 masses). Radiomic features were extracted from T2-weighted (T2W), in-phase, out-of-phase, and apparent diffusion coefficient (ADC) sequences, while mass size, T2W signal intensity, heterogeneity, and signal drop were assessed as radiologic features. Dimension reduction was performed by reliability analysis and wrapper-based feature selection with five algorithms. A support vector machine (SVM) was used for classification, and performance was assessed using 10-fold cross-validation and unseen testing. Friedman test and post-hoc analyses compared bootstrapped unseen test area under the curve (AUCs).
resultsOnly 12% of radiomic features demonstrated excellent reproducibility. A significant difference was observed among the three models, χ2(2)=779.5, p<0.001. The combined radiomic-radiologic model achieved the best performance (AUC 0.939; accuracy 85.7%), outperforming radiomic-only (AUC 0.898; accuracy 85.7%) and radiologic-only (AUC 0.857; accuracy 78.5%) models (adjusted p<0.001). DISCUSSION: Integrating radiomic and radiologic features improved classification performance compared to using either feature set alone. Although the reproducibility of radiomic features was limited, their complementary value enhanced model robustness.
conclusionA combined radiomic-radiologic ML model based on multi-sequence MRI may serve as a promising non-invasive tool for differentiating atypical adrenal adenomas from metastases.
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