ReviewTherapeutic advances in urology
Artificial intelligence and radiomics applications in adrenal lesions: a systematic review.
Review in Therapeutic advances in urology. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed.
- Radiomic Characterization of Adrenal Incidentalomas on NECT: Retrospective Exploratory Study and Systematic Review.Journal of imaging · 2026Article
- Clinical Artificial Intelligence Agents in Nephrology: From Prediction to Action Through Workflow-Native Intelligence-A Roadmap for Workflow-Integrated Care.Journal of clinical medicine · 2026Review
- [Modern trends in the radiological diagnostics of adrenal tumors].Problemy endokrinologii · 2026Article
- Can radiomics-based innovations improve the diagnosis of kidney fibrosis in diabetic nephropathy?Clinical kidney journal · 2026Review
- Differentiation of Fat-poor and Atypical Adrenal Adenomas from Metastases: MRI-based Radiomic, Radiologic, and Radiomic-radiologic Machine Learning Models.Current medical imaging · 2026Article
- AI-enabled precision evaluation of adrenal masses: radiomics, deep learning, and explainable imaging biomarkers.Frontiers in endocrinology · 2026Review
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
25 authors.
Funding
No grant is acknowledged in the PubMed record.
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: 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.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.