Predicting Helicobacter pylori Antibiotic Resistance from Routine Hematoxylin and Eosin Histopathology Using a Vision-Language Model-Guided Foundation Model Framework.

Authors

Siping Xiong, Shuguang Liu, Wei Zhang, Degui Liao, Tian Tang, Chao Zeng, Yimin Guo

Published in

Infection and drug resistance. Volume 19. Pages 624553. Epub Jul 31, 2026.

Abstract

Helicobacter pylori (H. pylori) eradication is increasingly compromised by antibiotic resistance, particularly to clarithromycin (CLA) and fluoroquinolones (FQs). Culture-based and molecular susceptibility testing remain resource-intensive and inaccessible in many settings. Whether routine hematoxylin and eosin (H&E) histopathology encodes indirect signatures of resistance has not been explored.
We developed a weakly supervised deep learning framework to predict phenotypic H. pylori resistance directly from gastric biopsy whole-slide images (WSIs). Patches were filtered using vision-language models (VLMs; Qwen3-VL and MedGemma) performing two zero-shot classification tasks-grading patch image quality and identifying gastric surface or gland-neck epithelium-based solely on visible histo-morphological features. Retained patches were encoded with VIRCHOW2, a histopathology-specific vision foundation model (ViT-H/14, pretrained on 3.1 million WSIs), and aggregated via cross-attention for slide-level prediction. Polymerase chain reaction (PCR)-confirmed resistance phenotypes served as ground truth in a multicenter cohort of 755 patients.
In the independent test set (n = 151), the framework achieved an area under the receiver operating characteristic curve (AUC) of 0.903 for CLA resistance and 0.959 for FQs resistance. At the default threshold (0.5), sensitivity was 85.2% for CLA and 98.1% for FQs, supporting use as a screening tool. Threshold optimization by the Youden index improved specificity to 95.9% for CLA and 93.9% for FQs, enabling rule-in risk stratification. Decision curve analysis confirmed net clinical benefit across a broad range of threshold probabilities.
Routine H&E-stained gastric biopsies contain probabilistic morphological correlates of H. pylori resistance phenotypes that deep learning can exploit. This approach provides complementary decision support for resistance risk stratification, particularly in resource-limited settings, pending prospective validation.

PMID:
42553869
Bibliographic data and abstract were imported from PubMed on 05 Aug 2026.

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