Key result
Digital image analysis with the Genie algorithm strongly correlated with stereology (r=0.946) and outperformed pathologist visual scoring for quantifying myocardial fibrosis.
Why the study?
Does digital image analysis improve the accuracy of myocardial fibrosis quantification compared to pathologist visual scoring in endomyocardial biopsies?
Observational (n=38)
Does digital image analysis improve the accuracy of myocardial fibrosis quantification compared to pathologist visual scoring in endomyocardial biopsies?
Mean Difference: -1.61
Absolute Event Rate: 11.6% vs 13.21%
p-value: p=<0.001
Digital image analysis provides accurate quantification of myocardial fibrosis in endomyocardial biopsies, outperforming traditional pathologist visual scoring.
Digital analysis may improve fibrosis quantification accuracy in cardiomyopathy; leaves open prospective validation before routine adoption.
BACKGROUND: Cardiac fibrosis disrupts the normal myocardial structure and has a direct impact on heart function and survival. Despite already available digital methods, the pathologist's visual score is still widely considered as ground truth and used as a primary method in histomorphometric evaluations. The aim of this study was to compare the accuracy of digital image analysis tools and the pathologist's visual scoring for evaluating fibrosis in human myocardial biopsies, based on reference data obtained by point counting performed on the same images. METHODS: Endomyocardial biopsy material from 38 patients diagnosed with inflammatory dilated cardiomyopathy was used. The extent of total cardiac fibrosis was assessed by image analysis on Masson's trichrome-stained tissue specimens using automated Colocalization and Genie software, by Stereology grid count and manually by Pathologist's visual score. RESULTS: A total of 116 slides were analyzed. The mean results obtained by the Colocalization software (13.72 ± 12.24%) were closest to the reference value of stereology (RVS), while the Genie software and Pathologist score gave a slight underestimation. RVS values correlated strongly with values obtained using the Colocalization and Genie (r>0.9, p<0.001) software as well as the pathologist visual score. Differences in fibrosis quantification by Colocalization and RVS were statistically insignificant. However, significant bias was found in the results obtained by using Genie versus RVS and pathologist score versus RVS with mean difference values of: -1.61% and 2.24%. Bland-Altman plots showed a bidirectional bias dependent on the magnitude of the measurement: Colocalization software overestimated the area fraction of fibrosis in the lower end, and underestimated in the higher end of the RVS values. Meanwhile, Genie software as well as the pathologist score showed more uniform results throughout the values, with a slight underestimation in the mid-range for both. CONCLUSION: Both applied digital image analysis methods revealed almost perfect correlation with the criterion standard obtained by stereology grid count and, in terms of accuracy, outperformed the pathologist's visual score. Genie algorithm proved to be the method of choice with the only drawback of a slight underestimation bias, which is considered acceptable for both clinical and research evaluations. VIRTUAL SLIDES: The virtual slide(s) for this article can be found here: http://www.diagnosticpathology.diagnomx.eu/vs/9857909611227193.
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Daunoravičius et al. (2014) conducted an observational in Inflammatory dilated cardiomyopathy (n=38). Digital image analysis (Genie software) vs. Stereology grid count (reference standard) was evaluated on Cardiac fibrosis area fraction (MD -1.61%, p=<0.001). Digital image analysis with the Genie algorithm strongly correlated with stereology (r=0.946) and outperformed pathologist visual scoring for quantifying myocardial fibrosis.