Enhanced CT Textures Derived From Computer Mathematic Distribution Analysis Enables Arterial Enhancement Fraction Being an Imaging Biomarker Option of Hepatocellular Carcinoma

Purpose: This study aims to explore the imaging–clinic relationship and an optional imaging biomarker of hepatocellular carcinoma (HCC) by using texture analysis on arterial enhancement fraction (AEF). Materials and Methods: The HCC patients treated in No. 2 Interventional Ward, ShengJing Hospital of China Medical University from June 2018 to June 2019 were enrolled, for whom tri-phasic enhanced CT scans were acquired. Perfusion analysis and texture analysis were then performed on the tri-phasic enhanced CT images. After the region of interest (ROI) of viable HCC was drawn, 13 AEF textures describing the values distribution were conducted. A between-groups comparison of AEF textures was made where the cases had grouping properties, a correlation analysis was made between AEF textures and alpha-fetoprotein (AFP) as well as other clinical data which were digital, and regression analysis was made when a significant correlation was found. SPSS 19.0 (IBM) was utilized for statistical analysis; a significant difference was considered when P < 0.05. Results: Twenty-five HCC patients were enrolled. Several AEF textures were found to have a correlation with clinical features, including previous surgery history, age, glutamic oxaloacetylase, indirect bilirubin, creatinine, and AFP. The majority of AEF textures (up to 9/13) were found to have a correlation with AFP (SD, variance, uniformity, energy, entropy, inertia, correlation, inverse difference moment, and cluster prominence), while six or seven textures have a linear or cubic relationship with AFP (SD, variance, uniformity, inertia, correlation, cluster prominence, plus inverse difference moment). Conclusion: The AEF textures of HCC are strongly correlated with and are impacted by AFP, which may enable AEF to act as an optional imaging biomarker of HCC.