Stratification of gastric cancer risk using a deep neural network

Abstract

Background and Aim Stratifying gastric cancer (GC) risk and endoscopy findings in high‐risk individuals may provide effective surveillance for GC. We developed a computerized image‐ analysis system for endoscopic images to stratify the risk of GC. Methods The system was trained using images taken during endoscopic examinations with non‐magnified white‐light imaging. Patients were classified as high‐risk (patients with GC), moderate‐risk (patients with current or past Helicobacter pylori infection or gastric atrophy), or low‐risk (patients with no history of H. pylori infection or gastric atrophy). After selection, 20,960, 17,404, and 68,920 images were collected as training images for the high‐, moderate‐, and low‐risk groups, respectively. Results Performance of the artificial intelligence (AI) system was evaluated by the prevalence of GC in each group using an independent validation dataset of patients who underwent endoscopic examination and H. pylori serum antibody testing. In total, 12,824 images from 454 patients were included in the analysis. The time required for diagnosing all the images was 345 seconds. The AI system diagnosed 46, 250, and 158 patients as low‐, moderate‐, and high risk, respectively. The prevalence of GC in the low‐, moderate‐, and high‐risk groups was 2.2, 8.8, and 16.4%, respectively (P = 0.0017). Three experienced endoscopists also successfully stratified the risk; however, interobserver agreement was not satisfactory (kappa value of 0.27, indicating fair agreement). Conclusion The current AI system detected significant differences in the prevalence of GC among the low‐, moderate‐, and high‐risk groups, suggesting its potential for stratifying GC risk.

Expert-level classification of gastritis by endoscopy using deep learning: a multicenter diagnostic trial

“…They divided the images into four groups according to their locations. [21] Our system predicts the locations in more details. We believe it will be a powerful tool for gastric cancer risk assessment.…”

“…In previous studies, AI has been applied to detection of Helicobacter pylori -associated gastritis and AG 8 9 20 . Gastric cancer risk stratification system also has been developed 21 . Nevertheless, DCNN-assisted classification of endoscopic gastritis rarely has been studied.…”

Artificial intelligence in gastric cancer: Application and future perspectives

“…Nakahira et al[ 12 ] described that the analysis system of AI-assisted endoscopic images could effectively stratify the risk of gastric cancer and further evaluated the consistency of the AI model with the consensus diagnoses of three endoscopists. Zhu et al[ 13 ] also constructed a CNN computer-aided detection system to determine the invasion depth of early gastric cancer.…”

Systems Biology of Gastric Cancer: Perspectives on the Omics-Based Diagnosis and Treatment

“…The resulting CNN correctly diagnosed 71 of 77 gastric cancer lesions with a overall sensitivity of 92.2% (Hirasawa et al, 2018). Moreover; endoscopic images were used to stratify gastric cancer risk by CNNs, which can diagnose patients as low, moderate, and high risk, respectively (Nakahira et al, 2020).…”