Characterization of adrenal lesions on unenhanced MRI using texture analysis: a machine-learning approach
Abstract
Background Adrenal adenomas (AA) are the most common benign adrenal lesions, often characterized based on intralesional fat content as either lipid‐rich (LRA) or lipid‐poor (LPA). The differentiation of AA, particularly LPA, from nonadenoma adrenal lesions (NAL) may be challenging. Texture analysis (TA) can extract quantitative parameters from MR images. Machine learning is a technique for recognizing patterns that can be applied to medical images by identifying the best combination of TA features to create a predictive model for the diagnosis of interest. Purpose/Hypothesis To assess the diagnostic efficacy of TA‐derived parameters extracted from MR images in characterizing LRA, LPA, and NAL using a machine‐learning approach. Study Type Retrospective, observational study. Population/Subjects/Phantom/Specimen/Animal Model Sixty MR examinations, including 20 LRA, 20 LPA, and 20 NAL. Field Strength/Sequence Unenhanced T1‐weighted in‐phase (IP) and out‐of‐phase (OP) as well as T2‐weighted (T2‐w) MR images acquired at 3T. Assessment Adrenal lesions were manually segmented, placing a spherical volume of interest on IP, OP, and T2‐w images. Different selection methods were trained and tested using the J48 machine‐learning classifiers. Statistical Tests The feature selection method that obtained the highest diagnostic performance using the J48 classifier was identified; the diagnostic performance was also compared with that of a senior radiologist by means of McNemar's test. Results A total of 138 TA‐derived features were extracted; among these, four features were selected, extracted from the IP (Short_Run_High_Gray_Level_Emphasis), OP (Mean_Intensity and Maximum_3D_Diameter), and T2‐w (Standard_Deviation) images; the J48 classifier obtained a diagnostic accuracy of 80%. The expert radiologist obtained a diagnostic accuracy of 73%. McNemar's test did not show significant differences in terms of diagnostic performance between the J48 classifier and the expert radiologist. Data Conclusion Machine learning conducted on MR TA‐derived features is a potential tool to characterize adrenal lesions. Level of Evidence: 4 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018.
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At present, there is insufficient evidence to support clinical translation of textural analysis and machine learning techniques for adrenal lesions.
“At present, there is insufficient evidence to support clinical translation, but these techniques have the potential to improve diagnostic accuracy and more informed decision making in the future.”
From Adrenal incidentaloma (2020) · cites this paper as [383]
Early data evaluating textural analysis-derived parameters extracted from MRI of adrenal lesions in 60 patients using machine learning showed a diagnostic accuracy of 80% compared to expert radiologist accuracy of 73%.
“Early data evaluating the efficacy of textural analysis-derived parameters extracted from MRI of adrenal lesions in 60 patients using a machine learning approach showed a diagnostic accuracy of 80% compared to expert radiologist accuracy of 73% (383).”
From Adrenal incidentaloma (2020) · cites this paper as [383]
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Kamińska, M., Trofimiuk–Müldner, M., Sokołowski, G., & Hubalewska–Dydejczyk, A. (2025) · Endocrine · doi:10.1007/s12020-025-04378-6 · Cited by 3 · see citing works
“TA of unenhanced MRI scans combined with ML methods was used by Romeo et al, who focused on differentiating between LRAs, LPAs and non-adenoma lesions [38].”
Binas, D. A., Betsi, G., Economopoulos, T. L., Mytareli, C., Bourgioti, C. I., Xekouki, P., Angelousi, A. G., & Matsopoulos, G. K. (2025) · HORMONES · doi:10.1007/s42000-025-00682-y · Cited by 3 · see citing works
“Until now, there have been few studies employing machine learning techniques for diagnosis and treatment of adrenal tumors [9–11].”
A radiomic signature based on magnetic resonance imaging to determine adrenal Cushing’s syndrome
Pişkin, F. C., Akkuş, G., Yücel, S. P., Akbas, B., & Odabası, F. (2023) · Polish Journal of Radiology · doi:10.5114/pjr.2023.124435 · Cited by 3 · see citing works
“They obtained a diagnostic accuracy of 80% [14].”
BackgroundAdrenal lesion classification with abdomen caps and the effect of ROI size
Solak, A., Ceylan, R., Bozkurt, M. A., Cebeci, H., & Koplay, M. (2023) · Physical and Engineering Sciences in Medicine · doi:10.1007/s13246-023-01259-y · Cited by 3 · see citing works
“Studies on MR images are as follows: Romeo et al. [13] assessed a classification study using a dataset comprising of 20 lipid-rich adenomas (LRAs), 20 lipid-poor adenomas (LPAs), and 20 non-adenomas adrenal lesions (NALs).”
MethodElsherif, S. B., Javadi, S., Blair, K. J., Morshid, A. I., Hui, L., Khalaf, A., Elbanan, M., Zhang, M., Habra, M. A., & Elsayes, K. M. (2020) · Journal of Computer Assisted Tomography · doi:10.1097/rct.0000000000001018 · Cited by 3 · see citing works
Pande, A., Kumar, A., & Anjankar, A. P. (2025) · Multidisciplinary Reviews · doi:10.31893/multirev.2025218 · Cited by 2 · see citing works
Adrenal Tumor Classification on T1 and T2-weighted Abdominal MR Images
Barstuğan, M., Ceylan, R., Asoğlu, S., Cebeci, H., & Koplay, M. (2019) · International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT) · doi:10.1109/ismsit.2019.8932938 · Cited by 2 · see citing works
“This study used more images than [8] and has a higher classification performance.”
BackgroundMethodInfluentialGonçalves, B., Saldanha, G., Ramalho, M., Vieira, L., & Vieira, P. M. (2025) · Applied Sciences · doi:10.3390/app15084100 · Cited by 1 · see citing works
Bioinformatics–computer programming
Iftikhar, M. S., Talha, G. M., Aleem, M., & Shamim, A. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-818154-6.00009-3
Adrenal Lesion Classification on T1-Weighted Abdomen Images with Convolutional Neural Networks
Solak, A., Ceylan, R., Bozkurt, M. A., Cebeci, H., & Koplay, M. (2022) · Uluslararası mühendislik araştırma ve geliştirme dergisi · doi:10.29137/umagd.1215882
Belyanova, M., & Krupev, M. (2023) · Proceedings of the Bulgarian Academy of Sciences · doi:10.7546/crabs.2023.10.16
“Several authors have used different order texture features which were not discriminative by themselves but when used in conjunction as input for different algorithms achieved satisfactory sensitivity and specificity when differentiating LPA from metastases [ 4,7,15–18 ].”
BackgroundEnhance a System for Predicting Skin Lesion Using Hybrid Feature Selection Technique
Singh, N. R., Kumar, S., & Vasudevan, S. K. (2024) · Lecture notes in networks and systems · doi:10.1007/978-981-97-4149-6_36
Beyond the norm: Exploring the diverse facets of adrenal lesions
Afif, S., Mahmood, Z., Zaheer, A., & Azadi, J. R. (2025) · Current Problems in Diagnostic Radiology · doi:10.1067/j.cpradiol.2025.08.015
Effectiveness of the Adaptive Heuristic Evaluation Method for Augmented Reality Interfaces
Lim, D., Suh, G., Shin, S., & Hwang, W. (2025) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2025.2561767
“For this type of data structure, McNemar test—widely used in the machine learning field to compare the significance of performance metrics—is more appropriate than participant-level tests such as the Wilcoxon test or the Mann–Whitney test (Mani et al., 2014; Romeo et al., 2018; S¸ener et al., 2024).”
Méthodes numériques pour la résolution de problèmes inverses en électrocardiographie
Karoui, A. (2021) · theses.fr (ABES) · doi:10.70675/cd6e5212zb67cz46eaza6e4z6422e1f8e0ee
Chasseloup, F. (2022) · theses.fr (ABES) · doi:10.70675/ddec5b6dzf376z4aadz9970zf0bf052c6f02
A CT-based deep learning model to differentiate between benign and malignant adrenal lesions
Huang, Z., Dohan, A. J. J., Assié, G., Gaillard, M., Violon, F., Jouinot, A., Soyer, P. A., Bertherat, J. Y., Marini, R., Chassagnon, G., et al. (2026) · European Journal of Radiology · doi:10.1016/j.ejrad.2026.112907
- Authors
- Romeo, V., Maurea, S., Cuocolo, R., Petretta, M., Mainenti, P. P., Verde, F., Coppola, M., Dell'Aversana, S., & Brunetti, A.
- Affiliations
- University of Naples Federico II (IT)
- Institute of Biostructure and Bioimaging (IT)
- Department of Advanced Biomedical Sciences University of Naples “Federico II,” Naples Italy
- Corresponding author
- Cuocolo, Renato
- ORCID
- Venue
- Journal of Magnetic Resonance Imaging
- Publisher
- Wiley
- Published
- 2018-01-17
- Volume
- 48
- Issue
- 1
- Pages
- 198-204
- ISSN
- 1053-1807 (ISSN-L)
- 1522-2586
- OpenAlex
- W2793377787
- Cited by
- 69
- References
- 24
- Citations by year
- 2026: 1 · 2025: 8 · 2024: 1 · 2023: 6 · 2022: 12 · 2021: 8 · 2020: 14 · 2019: 18 · 2018: 1
- FWCI
- 7.07
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Magnetic Resonance Imaging (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
- Health Sciences › Medicine › Surgery › Adrenal and Paraganglionic Tumors
- Keywords
- adrenal adenoma, adrenal mass, texture analysis, non-contrast MRI, in-phase and out-of-phase imaging, T2-weighted imaging, J48 classifier, feature selection, computer-aided diagnosis
- MeSH terms
- Machine Learning, Adenoma, Adolescent, Adrenal Gland Neoplasms, Adrenal Glands, Adult, Aged, Algorithms, Contrast Media, Female, Humans, Image Interpretation, Computer-Assisted, Image Processing, Computer-Assisted, Lipids, Magnetic Resonance Imaging, Male, Middle Aged, Pattern Recognition, Automated, Retrospective Studies, Reproducibility of Results, Young Adult
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