Quantification of Degree of Liver Fibrosis Using Fibrosis Area Fraction Based on Statistical Chi-Square Analysis of Heterogeneity of Liver Tissue Texture on Routine Ultrasound Images

We present a novel method to quantify the degree of liver fibrosis using fibrosis area fraction based on statistical chi-square analysis of heterogeneity of echo texture within liver on routine ultrasound images. We demonstrate, in a clinical study, that fibrosis area fraction derived this way has the potential to become a noninvasive, quantitative radiometric discriminator of normal or low-grade liver fibrosis (Ishak fibrosis score range = F0–3) and advanced liver fibrosis or cirrhosis (Ishak fibrosis score range = F4–6) on routine ultrasound images. This retrospective patient study was institutional review board approved. Ultrasound images of 100 patients (61 males, 39 females; 18–81 years) who underwent nontargeted ultrasound-guided biopsy were randomly divided into two groups: a training group consisted of 31 cases, and a validation group that contained the rest cases. An investigator manually selected a primary region of interest (ROI; approximately 4–6 cm2) in the liver tissue while avoiding nonhepatic parenchyma. The primary ROI contained a large number of secondary ROIs (25 × 25 pixels) to maintain the precision of statistical analysis. Sample variance σ2 of image gradient (a texture feature related to the amount of edge structures) was calculated in secondary ROIs in a roster scan fashion. A theoretical derivation was presented to estimate population variance σ02~ of image gradient across the primary ROI from mean gradient µ of secondary ROIs. The χ2 (χ2 = σ2/σ02~) was computed at each secondary ROI, forming a χ2 map of liver tissue heterogeneity. A cut-off value was optimized from datasets in the training group by comparing to the fibrosis grades determined by biopsy. This cut-off value was then applied to the datasets in the validation group to convert the χ2 maps into binary images, from which fibrosis area fractions (fraction of fibrosis area to the total area of the primary ROI) were calculated and entered in a statistical analysis. In the training group, the optimal setting was found to be Tχ2= 6.0, which resulted a maximum discrimination of F0–3 vs F4–6: p < 0.0001, area under curve = 0.985, sensitivity = 93.7%, specificity = 93.3%. When this setting was applied to the datasets in the validation group, a distinct separation was seen between the two classes (p < 0.0001). F0–3 class had an average fibrosis area fraction of 4.7% (1.7%−11.4%), whereas the F4–6 class had an average fibrosis area fraction of 17.3% (9.8%−29.6%). A strong correlati