Advancing predictive oncology: Integrating clinical and radiomic models to optimize transarterial chemoembolization outcomes in hepatocellular carcinoma.

Abstract

This article discusses the innovative use of computed tomography radiomics combined with clinical factors to predict treatment response to first-line transarterial chemoembolization in hepatocellular carcinoma. Zhao et al developed a robust predictive model demonstrating high accuracy (area under the curve 0.92 in the training cohort) by integrating venous phase radiomic features with alpha-fetoprotein levels. This noninvasive approach enables early identification of patients unlikely to benefit from transarterial chemoembolization, allowing a timely transition to alternative therapies such as targeted agents or immunotherapy. Such precision strategies may improve clinical outcomes, optimize resource utilization, and increase survival in advanced hepatocellular carcinoma management. Future studies should emphasize external validation and broader clinical adoption.