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.

Keywords: Hepatocellular carcinoma; Radiomics; Transarterial chemoembolization; Alpha-fetoprotein; Predictive modeling; Machine learning; Computed tomography; Texture analysis; Treatment response; Personalized oncology

Core Tip: Radiomic analysis of computed tomography images-particularly texture and shape features-combined with clinical biomarkers such as alpha-fetoprotein, enables accurate prediction of response to transarterial chemoembolization in hepatocellular carcinoma, with area under the curve values exceeding 0.90. These noninvasive models allow early identification of non-responders, support personalized treatment selection, and may improve outcomes through timely initiation of alternative therapies in liver cancer management.