Key result
FibroSoft accurately quantifies cardiac fibrosis in mice, strongly correlating with collagen 1 expression.
Why the study?
Current automatic tools for quantifying histological fibrosis do not perform well under variant conditions and lack flexibility for user-selected staining methods.
Does FibroSoft accurately quantify fibrosis in histological sections compared to Western blot in a mouse model of cardiac hypertrophy and heart failure?
Does FibroSoft accurately quantify fibrosis in histological sections compared to Western blot in a mouse model of cardiac hypertrophy and heart failure?
Effect estimate: R2 0.8276
p-value: p=0.00052
FibroSoft is a novel, open-source, machine learning-based tool that accurately quantifies histological fibrosis staining in preclinical models, strongly correlating with Western blot protein expression.
Provides validated fibrosis quantification in mouse hypertrophy models; leaves open clinical translation pending human validation.
Pathological remodeling of the extracellular matrix is a hallmark of cardiovascular disease, causing cardiac dysfunction by reducing ejection fraction and impaired electrical conductance, leading to arrythmias. Hence, accurate quantification of fibrosis deposition in histological sections is of extreme importance for preclinical and clinical studies. Current automatic tools do not perform well under variant conditions. Moreover, users do not have the option to evaluate data from staining methods of their choice according to their purpose. To overcome these challenges, we underline a novel machine learning-based tool (FibroSoft) and we show its feasibility in a model of cardiac hypertrophy and heart failure in mice. Our results demonstrate that FibroSoft can identify fibrosis in diseased myocardium and the obtained results are user-independent. In addition, the results acquired using our software strongly correlate to those obtained by Western blot analysis of collagen 1 expression. Additionally, we could show that this method can be used for Masson’s Trichrome and Picosirius Red stained histological images. The evaluation of our method also indicates that it can be used for any particular histology segmentation and quantification. In conclusion, our approach provides a powerful example of the feasibility of machine learning strategies to enable automatic analysis of histological images.
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Remes et al. (2022) studied Cardiac hypertrophy and heart failure (murine model) (n=24). FibroSoft software analysis vs. Western blot analysis was evaluated on Correlation between Picosirius Red staining quantified by FibroSoft and Western blot analysis of collagen 1 expression (R2 0.8276, p=0.00052). FibroSoft, a novel machine learning-based tool, accurately quantified fibrosis in a mouse model of cardiac hypertrophy, with results strongly correlating to Western blot analysis of collagen 1.
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