Kinetic Textural Biomarker for Predicting Survival of Patients with Advanced Hepatocellular Carcinoma After Antiangiogenic Therapy by Use of Baseline First-Pass Perfusion CT

Previous texture analysis studies of liver CT images have shown the potential to achieve hepatic malignancy or predict overall survival (OS). However, to date, most studies have mainly focused on assessing texture features of the noncontrast CT or portal-phase image in the dynamic contrast-enhanced CT sequence. The aim of this study was to quantify texture features of physiologically-based kinetic parametric images, and to develop prognostic kinetic textural biomarkers for 1-year survival (1YS) and OS in patients with advanced hepatocellular carcinoma (HCC) following antiangiogenic therapy in comparison among five different tracer kinetic models. Mean, standard deviation, coefficient of variation, skewness, and kurtosis of the pixel distribution histogram within HCC were derived from baseline first-pass perfusion CT parameters. Results suggest that texture analysis of kinetic parametric images can provide better chances of finding effective prognostic biomarkers for the prediction of survival than a mean value analysis alone.