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Differentiation of Fat-poor and Atypical Adrenal Adenomas from Metastases: MRI-based Radiomic, Radiologic, and

Ceyda Turan Bektaş1, Hasan Bulut2, Ece Ates Kus3

  • 1Department of Radiology, Istanbul Training and Research Hospital, Istanbul, Turkey.

Current Medical Imaging
|April 13, 2026
PubMed
Summary

A machine learning model combining radiomic and radiologic MRI features accurately differentiates adrenal tumors. This approach shows promise for non-invasively distinguishing atypical adrenal adenomas from metastases.

Keywords:
AdenomaArtificial intelligenceMachine learningMetastasisRadiomics.Texture analysis

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Area of Science:

  • Radiology
  • Machine Learning
  • Oncology

Background:

  • Differentiating fat-poor/atypical adrenal adenomas from metastases is challenging.
  • MRI-based radiomic, radiologic, and combined machine learning (ML) models were evaluated.

Purpose of the Study:

  • To assess the predictive value of MRI-based radiomic, radiologic, and combined ML models.
  • To improve the non-invasive differentiation of adrenal masses.

Main Methods:

  • Retrospective study of 44 adrenal masses (19 adenomas, 25 metastases).
  • Radiomic features extracted from T2W, in-phase, out-of-phase, and ADC MRI sequences.
  • Radiologic features included size, T2W signal intensity, heterogeneity, and signal drop.
  • Support vector machine classification with data augmentation and feature selection.

Main Results:

  • Combined radiomic-radiologic model achieved the highest performance (AUC 0.939, accuracy 85.7%).
  • Outperformed radiomic-only (AUC 0.898) and radiologic-only (AUC 0.857) models.
  • Radiomic feature reproducibility was limited (12% excellent).

Conclusions:

  • Integrating radiomic and radiologic features enhances classification performance.
  • Combined ML model shows potential as a non-invasive tool for adrenal mass differentiation.
  • Complementary value of radiomic features improves model robustness despite limited reproducibility.