Aim: When diagnosing chronic hepatitis, usual ultrasonography is not safe enough when determining
the difference between certain fibrosis stages. We tried to assess the usefulness
of the computerized texture image analysis in noninvasive assessment of fibrosis stage
in chronic hepatitis C patients.
Methods: We've selected 58 chronic hepatitis C patients, which have pure fibrosis (F0-F4)
without any steatosis. We used a Logiq 7 scanner with the same working parameters
for all patients. On each evaluated image, we established a Region of Interest and
we extracted 166 features using 4 algorithms from it. The values resulted after applying
the algorithms were analyzed and sorted according to their statistical relevance for
each fibrosis stage (we used t-Student's test for a p<0.05)
Results: From the 4 tested algorithms the GLCM statistics gave us the most relevant features
in order to differentiate fibrosis grades. Using all the generated features we observed
that in differentiating F0 from F1 9 features (18.36%) were statistically relevant,
13 features (26.53%) – in differentiating F1-F2, 47 features (95.9%) – in differentiating
F2-F3 and 32 features (65.3%) – in differentiating F3-F4.
Conclusions: Although there is no a single feature that has a relevance over 95% in all comparison
cases, our findings suggest that a combination of features must be used in order to
successfully diagnose the fibrosis stage. The direct medical benefit will resume in
a possible distinction between patients without significant fibrosis and those with
severe fibrosis or cirrhosis, distinction which is a very important prognosis factor.
