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

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

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 (A...

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Main Authors: Matteo Ferro, Octavian Sabin Tataru, Giuseppe Carrieri, Gian Maria Busetto, Ugo Giovanni Falagario, Martina Maggi, Felice Crocetto, Biagio Barone, Francesco Del Giudice, Michele Marchioni, Daniela Terracciano, Giuseppe Lucarelli, Pasquale Ditonno, Raul Gherasim, Ciprian Todea-Moga, Giuseppe Fallara, Marco Tozzi, Antonio Cioffi, Roberto Bianchi, Alessio Digiacomo, Alessandro Veccia, Alessandro Antonelli, Maria Chiara Sighinolfi, Luigi Schips, Bernardo Rocco
Format: Article
Language:English
Published: SAGE Publishing 2025-07-01
Series:Therapeutic Advances in Urology
Online Access:https://doi.org/10.1177/17562872251352553
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Summary: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.
ISSN:1756-2880

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