Helicobacter pylori Coccoid Forms as a Possible Target of Eradication Therapy

Pathohistological Changes in the Gastric Mucosa in Correlation with the Immunohistochemically Detected Spiral and Coccoid Forms of Helicobacter pylori

BACKGROUND

The coccoid form of Helicobacter pylori (H. pylori) is resistant to antibiotics. There are only a few studies that have analyzed the frequency of coccoid H. pylori in patients with gastritis. The aim of this work was to examine the correlation between the H. pylori form and the pathohistological characteristics of the stomach in patients with gastritis.

MATERIALS AND METHODS

This research was cross-sectional and focused on the gastric mucosa samples of 397 patients from one general hospital in Croatia. Two independent pathologists analyzed the samples regarding the pathohistological characteristics and the form of H. pylori.

RESULTS

There was a statistically significant difference in the gender of patients with H. pylori gastritis. Only the coccoid form of H. pylori was present in 9.6% of patients. There was a statistically significant difference in the frequency of a certain form of the bacterium depending on its localization in the stomach. The intensity of the bacterium was low in the samples where only the coccoid or spiral form was described. In cases of infection in the antrum, premalignant lesions and the coccoid form of H. pylori were more often present.

CONCLUSION

In the diagnosis of H. pylori infection, the determination of the form of the bacterium via immunohistochemistry should be included to increase the rate of eradication therapy and reduce the incidence of gastric malignancy.

A study on the diagnosis of the Helicobacter pylori coccoid form with artificial intelligence technology

Background

Helicobacter pylori (H. pylori) is an important pathogenic microorganism that causes gastric cancer, peptic ulcers and dyspepsia, and infects more than half of the world's population. Eradicating H. pylori is the most effective means to prevent and treat these diseases. H. pylori coccoid form (HPCF) causes refractory H. pylori infection and should be given more attention in infection management. However, manual HPCF recognition on slides is time-consuming and labor-intensive and depends on experienced pathologists; thus, HPCF diagnosis is rarely performed and often overlooked. Therefore, simple HPCF diagnostic methods need to be developed.

Materials and methods

We manually labeled 4,547 images from anonymized paraffin-embedded samples in the China Center for H. pylori Molecular Medicine (CCHpMM, Shanghai), followed by training and optimizing the Faster R-CNN and YOLO v5 models to identify HPCF. Mean average precision (mAP) was applied to evaluate and select the model. The artificial intelligence (AI) model interpretation results were compared with those of the pathologists with senior, intermediate, and junior experience levels, using the mean absolute error (MAE) of the coccoid rate as an evaluation metric.

Results

For the HPCF detection task, the YOLO v5 model was superior to the Faster R-CNN model (0.688 vs. 0.568, mean average precision, mAP); the optimized YOLO v5 model had a better performance (0.803 mAP). The MAE of the optimized YOLO v5 model (3.25 MAE) was superior to that of junior pathologists (4.14 MAE, p < 0.05), no worse than intermediate pathologists (3.40 MAE, p > 0.05), and equivalent to a senior pathologist (3.07 MAE, p > 0.05).

Conclusion

HPCF identification using AI has the advantage of high accuracy and efficiency with the potential to assist or replace pathologists in clinical practice for HPCF identification.