Letter to the Editor: Magnetic resonance imaging-based deep learning radiomics for preoperative risk stratification in pediatric hepatoblastoma

Figure 1 Based on the study results, the figure illustrates a diagnostic paradigm shift where a magnetic resonance imaging derived deep-learning radiomics score, calculated from four key T1/T2 characteristics via ResNet34, provides a quantitative biological signature of hepatoblastoma heterogeneity. When integrated with traditional markers like AFP and PRETEXT stage into a prognostic nomogram, the model identifies high-risk patients requiring aggressive neoadjuvant therapy and intensified surveillance, while conversely identifying low-risk candidates for surgery alone to avoid adjuvant chemotherapy causing cardio or ototoxicities. Created in https://BioRender.com/di4ajz9.