Quantifying liver fibrosis through the application of texture analysis to diffusion weighted imaging

Area of Science:

• Biomedical Imaging
• Medical Physics
• Gastroenterology

Background:

• Hepatic fibrosis, a significant contributor to liver disease morbidity and mortality, requires accurate quantification for effective management.
• Current methods for assessing liver fibrosis often involve invasive procedures or lack quantitative precision.

Purpose of the Study:

• To evaluate the utility of texture analysis applied to parametric apparent diffusion coefficient (ADC) maps for quantifying hepatic fibrosis.
• To explore the correlation between texture-based features and established methods of fibrosis assessment.

Main Methods:

• Ex vivo murine liver tissues from a dietary model of hepatic fibrosis were analyzed.
• Texture analysis techniques, including histogram-based, gray-level co-occurrence matrix (GLCM), and gray-level run-length matrix (GLRLM) features, were employed.
• Correlations between texture features and subjective/digital image analysis assessments of fibrosis were investigated.

Main Results:

• Several texture-based features derived from ADC maps demonstrated moderate to very strong correlations with hepatic fibrosis.
• These correlations were consistent with both subjective and digital image analysis-based assessments of fibrosis severity.

Conclusions:

• Texture analysis of parametric ADC maps is a potentially valuable tool for the noninvasive, quantitative assessment of hepatic fibrosis.
• This approach validates the utility of texture-based features in a preclinical model, paving the way for clinical translation.