Artificial Intelligence computed tomography models for the discrimination of Wilms versus non-Wilms tumors: systematic review and meta-analysis.

OBJECTIVE: To conduct a systematic review and meta-analysis to evaluate the effectiveness of artificial intelligence (AI) models aimed at identify Wilms tumor on computed tomography (CT) scans. METHODS: A search was carried out across MEDLINE, Embase, Web of Science, and Cochrane databases in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines. Diagnostic studies using AI-based CT to diagnose Wilms tumor were included if they reported sensitivity, specificity, and AUC. Studies with incomplete data or lacking full-text availability were excluded. Statistical analysis was conducted in R (v4.3.3) using a random-effects model, with logit transformation for univariate analysis and SROC curve construction for bivariate analysis. Heterogeneity (I2 ≥ 40%) was assessed and explored via sensitivity analysis. RESULTS: The analysis included four studies (three studies from China and one from Turkey) with 177 patients with Wilms tumors and 62 without Wilms tumors. The combined analysis of all models demonstrated a sensitivity of 63.9% (95% CI: 0.533-0.734), a specificity of 82.8% (95% CI: 0.716-0.902), and an area under the curve (AUC) of 0.831 (95% CI: 0.607-0.883). CONCLUSION: This study demonstrated that AI models exhibit moderate sensitivity and high specificity to identify Wilms tumor on CT scans, with an overall AUC of 0.831. These results underscore the promise of AI as a supportive tool in diagnostic imaging, although the limited number of studies and notable methodological heterogeneity warrant cautious interpretation and reinforce the need for validation in larger, more representative populations.