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Review
. 2025 Aug 2:17:17562872251352553.
doi: 10.1177/17562872251352553. eCollection 2025 Jan-Dec.

Artificial intelligence and radiomics applications in adrenal lesions: a systematic review

Matteo Ferro  1 , Octavian Sabin Tataru  2 , Giuseppe Carrieri  3 , Gian Maria Busetto  3 , Ugo Giovanni Falagario  3 , Martina Maggi  4 , Felice Crocetto  5 , Biagio Barone  6 , Francesco Del Giudice  4 , Michele Marchioni  7 , Daniela Terracciano  8 , Giuseppe Lucarelli  9 , Pasquale Ditonno  9 , Raul Gherasim  10 , Ciprian Todea-Moga  10 , Giuseppe Fallara  11 , Marco Tozzi  1 , Antonio Cioffi  12 , Roberto Bianchi  11 , Alessio Digiacomo  13 , Alessandro Veccia  13 , Alessandro Antonelli  13 , Maria Chiara Sighinolfi  14 , Luigi Schips  7 , Bernardo Rocco  14
Affiliations
Review

Artificial intelligence and radiomics applications in adrenal lesions: a systematic review

Matteo Ferro et al. Ther Adv Urol. .

Abstract

Background: Adrenal lesions, often incidentally detected, present diagnostic challenges in distinguishing benign from malignant or hormonally active lesions. Conventional imaging (computed tomography/magnetic resonance imaging (CT/MRI)) has limitations, driving interest in artificial intelligence (AI) and radiomics for enhanced accuracy.

Objectives: To systematically evaluate AI and radiomics applications in adrenal lesion characterization, focusing on diagnostic performance, methodologies, and clinical utility.

Design: PRISMA-guided systematic review of studies published up to June 2024.

Data sources and methods: PubMed, Scopus, Web of Science, and Google Scholar were searched using the keywords: adrenal lesions, AI, radiomics, and machine learning. Inclusion followed PICO criteria: patients with indeterminate lesions, AI/radiomics interventions, comparisons to standard diagnostics, and diagnostic accuracy. Two reviewers screened studies, resolving discrepancies via consensus. Eleven retrospective studies (996 patients) met eligibility.

Results: CT-based radiomics (eight studies) achieved a mean AUC of 0.88 (range: 0.84-0.94) in differentiating benign/malignant or functional/non-functional lesions. Top-performing models identified aldosterone-producing adenomas (AUC: 0.99). MRI-based radiomics (three studies) yielded mean AUC: 0.82 (0.72-0.92), with test-set performance declines (e.g., AUC: 0.72) suggesting overfitting. Nuclear medicine (four studies) demonstrated that hybrid 18F-FDG PET/CT models (SUVmax + texture features) achieved an AUC of 0.97 for metastatic versus benign lesions. AI applications extended to intraoperative navigation (AUC: 0.93) and prognostic prediction.

Conclusion: CT-based radiomics outperformed MRI, aligning with guidelines favoring CT for adrenal assessment. AI-enhanced models show promise in refining diagnostics and reducing invasive procedures. However, retrospective designs, small cohorts, and protocol variability limit generalizability. Future work requires multicenter collaboration, standardized protocols, and prospective validation to translate AI/radiomics into clinical practice.

Keywords: adrenal tumors; artificial intelligence; computed tomography; magnetic resonance imaging; radiomics.

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Conflict of interest statement

The authors declare that there is no conflict of interest.

Figures

“Literature search and study selection flowchart.”
Figure 1.
Literature search and study selection flowchart.
This diagram shows a graphical representation of artificial intelligence, machine learning, and deep learning.
Figure 2.
Graphical representation of AI, ML, and DL. AI, artificial intelligence; DL, deep learning; ML, machine learning.
sample alt text description
Figure 3.
Radiomics workflow for adrenal tumor identification.
The diagram compares the processes of Machine Learning (ML) and Deep Learning (DL) using a flowchart. It starts with input, followed by feature extraction in ML, and classification in ML. The process is similar in DL with the addition of combining feature extraction with classification.
Figure 4.
Differences between ML and DL. DL, deep learning; ML, machine learning.
See this image and copyright information in PMC

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References

    1. Boland GWL, Blake MA, Hahn PF, et al. Incidental adrenal lesions: principles, techniques, and algorithms for imaging characterization. Radiology 2008; 249: 756–775. - PubMed
    1. Song JH, Chaudhry FS, Mayo-Smith WW. The incidental indeterminate adrenal mass on CT (>10 H) in patients without cancer: is further imaging necessary? Follow-up of 321 consecutive indeterminate adrenal masses. Am J Roentgenol 2007; 189: 1119–1123. - PubMed
    1. Altay C, Bașara Akın I, Özgül AH, et al. Machine learning analysis of adrenal lesions: preliminary study evaluating texture analysis in the differentiation of adrenal lesions. Diagn Interv Radiol 2023; 29: 234–243. - PMC - PubMed
    1. Sahdev A, Willatt J, Francis IR, et al. The indeterminate adrenal lesion. Cancer Imaging 2010; 10: 102–113. - PMC - PubMed
    1. Zhang H, Lei H, Pang J. Diagnostic performance of radiomics in adrenal masses: a systematic review and meta-analysis. Front Oncol 2022; 12: 975183. - PMC - PubMed

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