Texture analysis of multi-phase MRI images to detect expression of Ki67 in hepatocellular carcinoma

Abstract

Aim: To determine whether texture analysis of preoperative magnetic resonance imaging (MRI) images could be used to detect Ki67 expression, a widely used cell proliferation marker in hepatocellular carcinoma (HCC).

Materials and methods: In total, 83 patients were included, 25 with low Ki67 (Ki67 ≤10%) HCC expression and 58 with high Ki67 (Ki67 ≥10%) HCC expression as demonstrated by retrospective surgical evaluation. All patients were examined using a 3 T MRI unit with one standard protocol. The region of interest was drawn manually by one radiologist. Texture analysis included histogram, co-occurrence matrix, run-length matrix, gradient, auto-regressive model, and wavelet transform features as calculated by MaZda (version 4.6; quantitative texture analysis software). The features reduced by the Fisher, probability of classification error, and average correlation coefficient (POE+ACC), mutual information were used to select the features that predicted Ki67 proliferation status with highest accuracy and then using the B11 program for data analysis and classification.

Results: The misclassification rate of the principal component analysis (PCA) in the hepatobiliary phase (HBP), T2-weighted imaging (T2WI), arterial phase (AP), and portal vein phase (PVP) was 36/83 (43.37%), 35/82 (42.68%), 40/83 (48.19%), and 34/83 (40.96%), respectively. The misclassification of the linear discriminant analysis in HBP, T2WI, AP, and PVP phase was 13/83 (15.66%), 21/82 (25.61%), 9/83 (10.84%), and 8/83 (9.64%), respectively. The misclassification of the nonlinear discriminant analysis in HBP, T2WI, AP, and PVP phase was 7/83 (8.43%), 6/82 (7.32%), 5/83 (6.02%), and 7/83 (8.43%), respectively.

Conclusions: Texture analysis of HBP, AP, and PVP were helpful for predicting Ki67 expression and may provide a less-invasive method to investigate critical histopathology markers for HCC.