Limited discriminative performance of endoscopic deep learning for Helicobacter pylori status assessment in gastric...

Helicobacter pylori infection is a major risk factor for gastric cancer, but evidence for predicting H. pylori status from endoscopic images in patients with established gastric cancer remains limited. We evaluated the performance of endoscopic image-based classification models for H. pylori status in a retrospective gastric cancer cohort.

We retrospectively collected 602 endoscopic images from 337 patients with gastric cancer treated at a tertiary cancer center. After preprocessing and quality control, 576 images from 329 patients were retained for model development and evaluation. Helicobacter pylori status was defined using routine clinical testing, including serum anti-H. pylori IgG and/or the ¹³C urea breath test. Endoscopic image classification models were evaluated across repeated stratified train, validation, and test splits. The primary performance metric was the area under the receiver operating characteristic curve (AUC).

Model discrimination was limited. The best-performing approach achieved a median test AUC of 0.6255, with sensitivity of 0.7308, specificity of 0.3714, accuracy of 0.6092, and F1 score of 0.6800. Fine-tuning showed a higher median AUC than retraining, but discrimination remained limited in both strategies.

In this retrospective gastric cancer cohort, endoscopic image-based classification showed limited ability to discriminate H. pylori status. These findings suggest that image-based assessment of H. pylori status in patients with gastric cancer remains challenging and may require more rigorous phenotyping, larger datasets, and validation across clinically diverse populations.