A semi-automated toolkit for analysis of liver cancer treatment response using Perfusion CT

Abstract

Delineation of hepatic tumours is challenging in CT due to limited inherent tissue contrast, leading to significant intra-/interobserver variability. Perfusion CT (pCT) allows quantitative assessment of enhancement patterns in normal and abnormal liver. This study aims to develop a semi-automated perfusion analysis toolkit that classifies hepatic tissue based on perfusion-derived parameters. pCT data from patients with hepatic metastases were used in this study. Tumour motion was minimized through image registration; perfusion parameters were derived and then employed in the training of a machine learning algorithm used to classify hepatic tissue. This method was found to deliver promising results for 10 data sets, with recorded sensitivity and specificity of the tissue classification in the ranges of 0.92–0.99 and 0.98– 0.99 respectively. This semi-automated method could be used to analyze response over the treatment course, as it is not based on intensity values.