Distinguishing alcohol-associated steatohepatitis (ASH) from metabolic dysfunction-associated steatohepatitis (MASH) is challenging given the absence of pathognomonic differentiators. We developed and evaluated a weakly supervised deep learning model, as a single-institution proof-of-concept study, to test whether routine hematoxylin and eosin (H&E) whole-slide images (WSIs) of liver biopsies contain sufficient morphological information to predict steatohepatitis etiology at the slide level.A retrospective cohort of 1147 WSIs was assembled (train set: 1007, holdout test set: 140). Models were trained using 5-fold patient-level cross-validation at ×20 and ×40 magnification. Model interpretability was assessed through attention-based clustering with blinded pathologist review of high-attention patches.The ×20 model achieved a mean area under the receiver operating characteristic of 0.86 ± 0.01 on the test set, with balanced accuracy of 0.80. The ×40 model performed comparably. Specificity was high (0.93 at ×20), with an ASH sensitivity of 0.67 at ×20. Fibrosis-stratified showed preserved performance at advanced fibrosis (stage ≥3) with balanced accuracy of 82.5% and ASH sensitivity of 77.4%. Attention-based clustering localized ASH-enriched regions to active injury patterns including Mallory–Denk bodies, neutrophilic inflammation, cholestatic change, and pericellular fibrosis, whereas MASH-enriched regions showed steatosis with lower inflammatory activity.Weakly supervised deep learning applied to routine liver H&E WSIs can discriminate ASH from MASH with performance preserved at advanced fibrosis. The comparable performance of ×20 and ×40 magnification, and the alignment of model attention with established histological features, support the use of routine morphology as a decision-support input in cases with incomplete or conflicting clinical histories, particularly when the etiological distinction has the greatest implications for management.
Whole-slide imaging (WSI) is gradually being adopted for primary diagnosis in many pathology subspecialties. The benefits of WSI for the pathology workflow are numerous and include the use of artificial intelligence (AI) tools to support WSI analysis. However, digitization of hematopathology workflows has remained a challenge, in part due to the complexity of the hematopathology workflow and the unique characteristics of blood and bone marrow smears. Although AI applications to support hematopathology tasks, such as nucleated differential counts, are well-described, general implementation of these tools has been limited due to the lack of widely available WSIs. Consequently, there is an unmet need for new and innovative solutions to WSI in hematopathology. This review focuses on WSI of hematopathology cytological specimens, particularly, peripheral blood and bone marrow smears, and summarizes unique challenges and AI solutions to support WSI analysis in this area. We also discuss future opportunities for hematopathologist-driven innovation in AI and WSI in hematopathology.
Digital pathology increasingly seeks to extract quantitative vascular and microenvironmental features from routine histology images, but thin-section hematoxylin and eosin (H&E) slides can fragment vessels and obscure their spatial organization, constraining downstream computational analysis. Slide-free fluorescence-imitating brightfield imaging (FIBI) produces histology-like images directly from fresh or fixed tissue or from already prepared paraffin blocks within minutes and can better preserve apparent microvascular continuity in breast cancer and other specimens. In this exploratory digital pathology study, we acquired paired FIBI and H&E images from paraffin-embedded breast tissue blocks, manually segmented blood vessels in tumor and tumor-adjacent stroma, and quantified vascular architecture using both standard geometry-based metrics and topology-derived descriptors of inter-vessel arrangement, including a persistent-homology-based “vessel spacing” metric that captures multiscale clustering. Here, geometry refers to properties of individual vessels (size, length, and branching), whereas topology summarizes how vessels are arranged as a network, including how closely or loosely they cluster. FIBI images exhibited an easily appreciable increase in vascular information compared with matched H&E slides, with vessels appearing more continuous and more clearly resolved in both geometry (e.g., larger area, more branched, etc.) and network topology (e.g., decreased inter-vessel separation). Motivated by this qualitative impression of increased informational content, the goal of this study was to quantitatively assess and validate these differences by contrasting complementary geometry and topology-derived metrics of vessels in pairs of H&E and FIBI images. In particular, persistent homology-derived topology metrics provided information that was not linearly explained by standard geometric descriptors. Together, these findings demonstrate a practical digital pathology pipeline that integrates slide-free FIBI acquisition, vessel annotation, geometry- and topology-based feature extraction, and suggest that FIBI-derived vascular signatures, particularly when coupled with automated vessel segmentation, may provide informative input for future computational and artificial intelligence-based pathology models, and potentially clinical applications.
