Predictive Value of MRI Radiomics for the Efficacy of High-Intensity Focused Ultrasound (HIFU) Ablation in Uterine...

High-Intensity Focused Ultrasound (HIFU) ablation has emerged as a non-invasive treatment option for uterine fibroids that preserves fertility and offers faster recovery. Pre-intervention prediction of HIFU efficacy can augment clinical decision-making and patient management. This systematic review and meta-analysis aims to evaluate the performance of MRI-based radiomics machine learning (ML) models in predicting the efficacy of HIFU ablation in uterine fibroids. Studies were retrieved by conducting a thorough literature search across databases including PubMed, Scopus, Embase, and Web of Science, up to June 2025. The quality of the included studies was assessed using the QUADAS-2 and METRICS tools. A meta-analysis of the radiomics models was conducted to pool sensitivity, specificity, and AUC using a bivariate random-effects model. A total of 13 studies were incorporated in the systematic review and meta-analysis. Meta-analysis of 608 patients from 7 internal and 6 external validation cohorts showed pooled AUC, sensitivity, and specificity of 0.84, 77%, and 78%, respectively. QUADAS-2 was notable for significant methodological biases in the index test and flow and timing domains. Across all studies, the mean METRICS score was 76.93%-with a range of 54.9%-90.3%-denoting good overall quality and performance in most domains but with notable gaps in the open science domain. MRI-based radiomics models show promise in predicting the effectiveness of HIFU ablation for uterine fibroids. However, limitations such as limited geographic diversity, inconsistent reporting standards, and poor open science practices hinder broader application. Therefore, future research should focus on standardizing imaging protocols, using multi-center designs with external validation, and integrating diverse data sources.