ArticleClinical and translational gastroenterology2026
A Transformer-Residual Network-Integrated Deep Learning Model for Digital Pathology-Based Grading and Risk Stratification of Metabolic Dysfunction-Associated Steatotic Liver Disease-Related Liver Fibrosis.
Article in Clinical and translational gastroenterology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
introductionMetabolic dysfunction-associated steatotic liver disease (MASLD) affects up to 30% of the global population and drives progression to fibrosis, cirrhosis, and liver cancer, yet early-stage identification remains limited by subjective histopathology and low-sensitivity imaging. We aimed to develop and validate a deep learning model integrating Transformer and Residual Network architectures using digital histopathology images of human liver biopsies for MASLD-related fibrosis staging and identification of pathological phenotypes associated with advanced fibrosis, with interpretability analyses to support pathology-assisted decision-making.
methodsFour hundred sixty-seven Masson-stained liver biopsies from Duke (n = 338), Medizinische Hochschule Hannover (MHH) (n = 72), and Boehringer Ingelheim (BI)-int (n = 57) repositories were digitized at 20× resolution. Fibrosis was scored 0-4 per Younossi criteria. A four-encoder architecture combining 1 × 1 strided convolutions, multihead self-attention Transformer blocks, and Residual Network residual units was trained with global average pooling and linear classification. Performance metrics included accuracy, confusion matrices, continuous artificial intelligence (AI) scores, and gradient-weighted Class Activation Mapping visualization; analyses were conducted in Python/Jupyter with pandas, Matplotlib, and PyTorch.
resultsTraining accuracy reached 0.80 and validation 0.85 without overfitting. Stage-specific accuracy ranged 0.74-0.95, peaking at bridging fibrosis (stage 3). Gradient-weighted Class Activation Mapping heatmaps localized model attention to severe fibrotic septa, corroborating advanced fibrotic architectural changes. Continuous AI scores aligned tightly with discrete ground-truth labels, with boundary-adjacent misclassifications reflecting subtle histologic overlap. DISCUSSION: The proposed model enables automated and interpretable assessment of MASLD-related fibrosis staging from digital liver biopsy histopathology and provides a quantitative tool for pathology phenotype-based risk stratification. Further clinical translation requires integration of clinical risk factors and validation in longitudinal outcome-based frameworks.
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