Reproducibility of CT and MRI texture features of hepatocellular carcinoma

Abstract

Purpose: To compare the reproducibility of texture features during segmentation of CT and MR images of hepatocellular cancer (HCC) by two radiologists. Evaluate the possibility of images pre-processing to improve the reproducibility of texture features. Material and methods. We included 66 patients with preoperative CT and MR images, followed by surgical liver resection for HCC, indicating the tumor histologic grade. Two radiologists with 3 and 6 years of abdominal imaging experience independently segmented the entire tumor volume on CT and MR images. The calculation of texture features was performed without the use of images pre-processing and using a spatial resampling with fixed voxel size of 1 mm3 (RES), a Laplace Gaussian filter, and for CT images, also with a density threshold from 0 to 300 HU (0–300 HU). The agreement between measurements of radiologists in relation to texture features was carried out using the type 2 intra-class correlation coefficient (ICC). Results. The highest percentage of reproducible texture features with the use of image preprocessing was observed with CT in the arterial phase (0–300 HU + RES) (89.1%), and with MRI in the hepatobiliary phase (RES) (86.6%). At the same time, spatial resampling with fixed voxel size of 1 mm3 increased the percentage of reproducible texture features in the native, arterial, and delayed phases in CT, as well as in T2-WI, DWI, and in the hepatobiliary phase for MRI. Conclusion. Thus, both techniques, CT and MRI, make it possible to extract reproducible texture features, especially with the use of spatial resampling with fixed voxel size. In this case, preference should be given to a postcontrast images, including the hepatobiliary phase. In CT, it is also advisable to use a density threshold from 0 to 300 HU for the selected area of interest, which will avoid affecting the texture features of adjacent organs and adipose tissue and increase the reproducibility of textural features.