Cardiotoxicity in pediatric oncology: a systematic review and meta-analysis.
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BACKGROUND: Anthracycline-induced cardiotoxicity is a major concern in childhood cancer survivors. Detecting subclinical cardiac dysfunction early is critical, but the accuracy of current diagnostic tools is uncertain. METHODS: We performed a systematic review and meta-analysis of studies evaluating the prognostic accuracy of echocardiography, serum biomarkers, microRNAs (miRNAs), and artificial intelligence (AI) models in predicting chemotherapy-induced cardiotoxicity in pediatric patients. Seventy-three studies were included in the qualitative synthesis, and ten in the meta-analysis. Quality was assessed using the QUAPAS tool (CRD42023485629). RESULTS: AI-based models showed the highest pooled predictive performance area under the curve (AUC)~0.80, despite significant heterogeneity (I² = 93%). Global longitudinal strain (GLS) had moderate accuracy (AUC~0.72). Cardiac biomarkers like troponin and N-terminal pro-B-type natriuretic peptide (NT-proBNP) showed variable sensitivity and specificity, largely influenced by timing and thresholds. Preliminary evidence on miRNAs was promising but lacked standardization. Additional methods (e.g., cardiac MRI, cardiopulmonary exercise testing (CPET)) were excluded from meta-analysis due to methodological variability. CONCLUSION: No single modality reliably detects early cardiotoxicity. Promising tools like AI models and miRNAs need further validation. A multimodal diagnostic strategy combining imaging, biomarkers, and clinical data may be the most effective approach. Standardized definitions and protocols are urgently needed. IMPACT: Early detection of chemotherapy-induced cardiotoxicity in children remains unresolved. Multimodal assessment is most effective because no single test is sufficiently reliable. There are critical methodological gaps and heterogeneity that impede standardization in pediatric cardio-oncology. AI and microRNAs are promising but still unvalidated tools. The findings guide future clinical monitoring strategies and support standardized multimodal diagnostic algorithms.