Abstract Chronic kidney disease (CKD) in children is a rare but serious condition characterized by progressive renal dysfunction and a range of systemic complications. Renal fibrosis plays a central role in disease progression, representing irreversible damage associated with poor outcomes. Currently, diagnosis and assessment of fibrosis rely primarily on invasive renal biopsy, which is limited by procedural risks and sampling variability. Quantitative magnetic resonance imaging (MRI) techniques, including relaxometry methods such as T1, T2, and T2* mapping, diffusion-weighted imaging, magnetization transfer imaging (including amide proton transfer), and magnetic resonance elastography, have emerged as promising non-invasive alternatives for evaluating renal structure and pathology, including the presence of fibrosis. Furthermore, artificial intelligence and machine learning methods applied to MRI data hold potential to enhance fibrosis detection and monitoring, though their application in pediatric CKD remains nascent. This review discusses the evolving role of quantitative MRI biomarkers in pediatric CKD and renal fibrosis, integrating findings from pediatric and adult studies to identify promising imaging biomarkers that may improve diagnosis, prognosis, and treatment monitoring. Emphasis is placed on the need for rigorous validation and longitudinal studies to enable clinical translation and personalized care approaches for children affected by CKD.
Dillman et al. (Sat,) studied this question.