Journal of Pathology Informatics · Published 2026-07-02 · DOI 10.1016/j.jpi.2026.100689
Zsolt Bedőházi, Zsófia Sztupinszki, Ragnar P. Kristjánsson, Mikkel Werling, Stephen Hamilton-Dutoit, Kristina L. Lauridsen, Lisa Ottander, Trine L. Plesner, Peter Hollander, Ingrid Glimelius, Lene Sjö, Estrid Høgdall, Carsten Utoft Niemann, Klaus Rostgaard, Péter Pollner, Henrik Hjalgrim, István Csabai
Accurate stratification of Hodgkin lymphoma (HL) by immunologic/histological subtypes and Epstein-Barr virus (EBV) status is essential for epidemiological and translational research, yet large-scale testing is impractical and expensive. Digital pathology models that utilize routinely used hematoxylin and eosin (H&E) whole-slide images (WSIs) could close this gap. We developed and validated a hierarchical Vision Transformer pipeline that aggregates cell-, patch-, and region-level context to predict EBV status and the three most prevalent immunological/histological HL subtypes: nodular sclerosis (NS), mixed cellularity (MC), and nodular lymphocyte-predominant HL (NLPHL)—from H&E-stained WSIs, and additionally evaluated a standard attention-based multiple-instance learning (ABMIL) baseline for direct architectural comparison. The development pool comprised 1643 HL cases (1952 WSIs) from 18 Danish hospitals and was used for hospital-preserving 5-fold cross-validation; external validation was performed on an independent hold-out cohort of 458 cases (532 WSIs) from five hold-out hospitals. For subtype prediction, analyses were restricted to the 1560 cases belonging to NS, MC, or NLPHL. On the external EBV cohort (N=458) the hierarchical pipeline achieved an area under the receiver operating characteristic curve (ROC–AUC) of 0.73 (95% confidence interval (CI) 0.68–0.77), precision–recall (PR)–AUC 0.57 (95% CI 0.49–0.66) with recall (sensitivity) 0.74 (95% CI 0.67–0.80) and macro-F1 score 0.60 (95% CI 0.54, 0.65). For 3-class subtype prediction on 359 external cases, discrimination reached ROC–AUC 0.84 (95% CI 0.80–0.88) and PR–AUC 0.63 (95% CI 0.56–0.71) with a macro-F1 of 0.56 (95% CI 0.48, 0.64) and macro-recall 0.55 (95% CI 0.47, 0.63); residual errors were dominated by NS-MC confusions. An ABMIL baseline using the same patch embeddings achieved ROC–AUC 0.76 (95% CI 0.72–0.81) for EBV and 0.89 (95% CI 0.86–0.92) subtype prediction, outperforming the hierarchical model on both tasks. This multicenter study shows that both hierarchical and attention-based architectures can determine EBV status and major HL subtypes directly from routine H&E slides with externally validated performance across hospitals, whereas the finding that the simpler baseline outperformed the hierarchical model suggests that strong foundation-model embeddings combined with attention-based pooling may reduce the need for explicit multi-scale modelling in cohorts of this size.
Abstract from DOAJ. Public domain (CC0 1.0).
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Bedőházi, Z., Sztupinszki, Z., Kristjánsson, R., et al. (2026). Hierarchical vision transformers for Epstein-Barr virus status and histological subtype prediction in Hodgkin lymphoma whole-slide images. Journal of Pathology Informatics. https://doi.org/10.1016/j.jpi.2026.100689