Advancing precision in hepatocellular carcinoma prognostication: The promise of biparametric magnetic resonance imaging-based multimodal models

Zuo and Liu investigated the value of a novel noninvasive approach integrating biparametric magnetic resonance imaging, radiomics, deep transfer learning, and clinical factors in predicting Ki-67 risk stratification and recurrence-free survival (RFS) in hepatocellular carcinoma (HCC). The study included 198 HCC patients and utilized histopathological Ki-67 expression as the reference standard for risk stratification. The integrated multimodal model combining radiomic features, deep transfer learning signatures, and clinical factors (nonsmooth tumor margin and absence of an enhanced capsule), achieved an area under the curve of 0.92 in the training and validation cohorts for predicting high Ki-67 risk, with a sensitivity and specificity of 0.88 and 0.85, respectively. Furthermore, the model effectively stratified RFS, with median RFS of 33.53 months in the high-risk group <i>vs</i> 66.74 months in the low-risk group, consistent with histopathological findings that directly refer to Ki-67 stratification. The findings highlight the potential of biparametric magnetic resonance imaging-based multimodal models in noninvasive HCC prognostication, though external validation in larger cohorts is warranted. The demand for precise, noninvasive preoperative assessment tools in HCC management remains high in clinical practice.