Introduction The histological assessment of liver biopsies by pathologists serves as the gold standard for diagnosing metabolic dysfunction-associated steatotic liver disease (MASLD) and staging disease progression. Various machine learning and image analysis tools have been reported to automate the quantification of fatty liver and enhance patient risk stratification. However, the current software is either not open-source or not directly applicable to the whole slide images (WSIs).

Methods In this paper, we introduce “Liver-Quant,” an open-source Python package designed for quantifying fat and fibrosis in liver WSIs. Employing colour and morphological features, Liver-Quant measures the Steatosis Proportionate Area (SPA) and Collagen Proportionate Area (CPA). The method’s accuracy and robustness were evaluated using an internal dataset of 424 WSIs from adult patients collected retrospectively from the archives at Leeds Teaching Hospitals NHS Trust between 2016 and 2022 and an external public dataset of 109 WSIs. For each slide, semi-quantitative scores were automatically extracted from free-text pathological reports. Furthermore, we investigated the impact of three different staining dyes including Van Gieson (VG), Picro Sirius Red (PSR), and Masson’s Trichrome (MTC) on fibrosis quantification.

Results The Spearman rank coefficient (ρ) was calculated to assess the correlation between the computed SPA/CPA values and the semi-quantitative pathologist scores. For steatosis quantification, we observed a substantial correlation (ρ=0.92), while fibrosis quantification exhibited a moderate correlation with human scores (ρ=0.51). To assess stain variation on CPA measurement, we collected N=18 cases and applied the three stains. Employing stain normalisation, an excellent agreement was observed in CPA measurements among the three stains using Bland-Altman plots. However, without stain normalisation, PSR emerged as the most effective dye due to its enhanced contrast in the Hue channel, displaying a strong correlation with human scores (ρ=0.9), followed by VG (ρ=0.8) and MTC (ρ=0.59). Additionally, we explored the impact of the apparent magnification on SPA and CPA. High resolution images collected at 0.25 microns per pixel (MPP) [apparent magnification = 40x] or 0.50 MPP [apparent magnification = 20x] were found to be essential for accurate SPA measurement, whereas for CPA measurement, low resolution images collected at 10 MPP [apparent magnification = 1x] were sufficient.

Conclusion The Liver-Quant package offers an open-source solution for rapid and precise liver quantification in WSIs applicable to multiple histological stains.

The authors have declared no competing interest.

The authors declare that there are no competing interests. MF, CM, AC, AW, and DT are funded by the National Pathology Imaging Co-operative (NPIC). NPIC (project no. 104687) is supported by a 50m GBP investment from the Data to Early Diagnosis and Precision Medicine strand of the Government's Industrial Strategy Challenge Fund, managed and delivered by UK Research and Innovation (UKRI). This project has been made possible in part by grant number 2021-237595 from the Chan Zuckerberg Initiative DAF, an advised fund of Silicon Valley Community. The funders had no role in the study design, data collection, analysis, or writing of the manuscript. For the purpose of open access, the author has applied a Creative Commons Attribution (CC BY) licence to any Author Accepted Manuscript version arising from this submission.

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Image data was obtained from the UK National Pathology Imaging Co-operative after ethical approval from the Office for Research Ethics Committees Northern Ireland (ORECNI), research ethics committee reference: 22/NI/0033

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