BACKGROUND/AIMS: Liver fibrosis is one of the most important and characteristic histologic alterations in progressive and chronic liver diseases. Thus, in both clinical and experimental practice, it is fundamental to have a reliable and objective method for its precise quantification. Several semi-quantitative scoring systems have been described. All are time-consuming and produce partially subjective fibrosis evaluations that are not very precise. This paper describes the design and validation of an original image analysis-based application, FibroQuant, for automatically and rapidly quantifying perisinusoidal, perivenular and portal-periportal and septal fibrosis and portal-periportal and septal morphology in liver histologic specimens. METHODS: The implemented image-processing algorithms automatically segment interstitial fibrosis areas, while extraction of portal-periportal and septal region is carried out with an automatic algorithm and a simple interactive step. For validation, all automatically extracted areas were also manually segmented and quantified. RESULTS: Statistical analysis showed significant intra- and interoperator variability in manual segmentation of all areas. Automatic quantifications did not significantly differ from mean manual evaluations of the same areas. Comparison of our image analysis quantifications with staging histologic evaluations of liver fibrosis showed significant correlations (Spearman's, 0.72<r<0.83; p<0.0001) and that the latter are based more on the distribution patterns than on the quantity of fibrosis. CONCLUSIONS: FibroQuant is a sensitive, precise, objective and reproducible method of fibrosis quantification, which complements semi-quantitative histologic evaluation systems. This novel tool could be of special value in clinical trials and for improving the prognosis and follow-up among patients with fibrosis-inducing hepatic diseases.
BACKGROUND/AIMS: Liver fibrosis is one of the most important and characteristic histologic alterations in progressive and chronic liver diseases. Thus, in both clinical and experimental practice, it is fundamental to have a reliable and objective method for its precise quantification. Several semi-quantitative scoring systems have been described. All are time-consuming and produce partially subjective fibrosis evaluations that are not very precise. This paper describes the design and validation of an original image analysis-based application, FibroQuant, for automatically and rapidly quantifying perisinusoidal, perivenular and portal-periportal and septal fibrosis and portal-periportal and septal morphology in liver histologic specimens. METHODS: The implemented image-processing algorithms automatically segment interstitial fibrosis areas, while extraction of portal-periportal and septal region is carried out with an automatic algorithm and a simple interactive step. For validation, all automatically extracted areas were also manually segmented and quantified. RESULTS: Statistical analysis showed significant intra- and interoperator variability in manual segmentation of all areas. Automatic quantifications did not significantly differ from mean manual evaluations of the same areas. Comparison of our image analysis quantifications with staging histologic evaluations of liver fibrosis showed significant correlations (Spearman's, 0.72<r<0.83; p<0.0001) and that the latter are based more on the distribution patterns than on the quantity of fibrosis. CONCLUSIONS: FibroQuant is a sensitive, precise, objective and reproducible method of fibrosis quantification, which complements semi-quantitative histologic evaluation systems. This novel tool could be of special value in clinical trials and for improving the prognosis and follow-up among patients with fibrosis-inducing hepatic diseases.
Authors: Sekou R Rawlins; Ola El-Zammar; J Michael Zinkievich; Nancy Newman; Robert A Levine Journal: Dig Dis Sci Date: 2010-05-12 Impact factor: 3.199
Authors: Fabio Grizzi; Carlo Russo; Barbara Franceschini; Mariagrazia Di Rocco; Valter Torri; Emanuela Morenghi; Luigi Rainiero Fassati; Nicola Dioguardi Journal: World J Gastroenterol Date: 2006-12-21 Impact factor: 5.742
Authors: Yue Zhang; Ning Jin; Jie Deng; Yang Guo; Sarah B White; Guang-Yu Yang; Reed A Omary; Andrew C Larson Journal: Magn Reson Imaging Date: 2013-04-15 Impact factor: 2.546