Contributing to the prediction of prognosis for treated hepatocellular carcinoma: Imaging aspects that sculpt the future

Abstract

A novel nomogram model to predict the prognosis of hepatocellular carcinoma (HCC) treated with radiofrequency ablation and transarterial chemoembolization was recently published in the World Journal of Gastrointestinal Surgery. This model includes clinical and laboratory factors, but emerging imaging aspects, particularly from magnetic resonance imaging (MRI) and radiomics, could enhance the predictive accuracy thereof. Multiparametric MRI and deep learning radiomics models significantly improve prognostic predictions for the treatment of HCC. Incorporating advanced imaging features, such as peritumoral hypointensity and radiomics scores, alongside clinical factors, can refine prognostic models, aiding in personalized treatment and better predicting outcomes. This letter underscores the importance of integrating novel imaging techniques into prognostic tools to better manage and treat HCC.

Keywords: Hepatocellular carcinoma; Radiofrequency ablation; Transcatheter arterial chemoembolization; Prognosis; Magnetic resonance imaging; Radiomics; Artificial intelligence

Core Tip: Emerging imaging techniques, particularly multiparametric magnetic resonance imaging and radiomics, enhance prognostic models for hepatocellular carcinoma (HCC) treated with radiofrequency ablation and transarterial chemoembolization. Incorporating advanced imaging features alongside clinical factors refines prediction accuracy, aiding personalized treatment and improving patient outcomes. This integration is crucial for advancing HCC prognosis and therapy.