Challenging detection of hard-to-find gastric cancers with artificial intelligence-assisted endoscopy

Variation in the rate of detection of minute and small early gastric cancers at diagnostic endoscopy may reflect the performance of individual endoscopists

OBJECTIVE

The documented variation in gastric cancer (GC) detection among endoscopists has often been dismissed as a coincidental artefact of the low incidence of gastric neoplasms; it is not considered associated with differences in physicians' performance of the esophagogastroduodenoscopy procedure. This study is to confirm whether significant variations among endoscopists in early GC detection suggest the individual performance of the upper endoscopy.

DESIGN

A retrospective observational study at a single centre in Japan assessed the results of 218 early GCs detected during 25 688 routine esophagogastroduodenoscopies by 12 endoscopists. The main outcome was the rate of early GC detection for each endoscopist under the same circumstances. Other measures included the major diameters and locations of the lesions, Helicobacter pylori infection status, and baseline patient characteristics that could affect the prevalence of GC.

RESULTS

The early GC detection rates exhibited wide variation among endoscopists (0.09%-2.87%) despite performing routine esophagogastroduodenoscopies in a population with a similar background. Endoscopists were assigned to a low-detection group (n=6; detection rate: 0.47% (range: 0.09%-0.55%)) and a high-detection group (n=5; detection rate: 0.83% (range: 0.63%-1.12%)), with the single highest detector analysed separately due to his distinct detection rate (2.87%). Endoscopists in the high-detection group had better detection rates for minute (major diameter ≤5 mm) and small (major diameter 6-10 mm) GCs than the low-detection group (0.19%/0.23% vs 0.085%/0.098%). These differences were significant (p<0.01), although there were no significant differences in detection of larger tumours (major diameter ≥11 mm; 0.40% vs 0.28%; p=0.13). The tumour location and H. pylori status were similar in the low-detection group, high-detection group and for the highest detector.

CONCLUSION

Significant variation in the detection of hard-to-find, smaller GCs may reflect individual performance of the examination.

Clinical value of artificial intelligence in hepatocellular carcinoma: Current status and prospect

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Artificial intelligence in gastric cancer: a translational narrative review

Increasing clinical contributions and novel techniques have been made by artificial intelligence (AI) during the last decade. The role of AI is increasingly recognized in cancer research and clinical application. Cancers like gastric cancer, or stomach cancer, are ideal testing grounds to see if early undertakings of applying AI to medicine can yield valuable results. There are numerous concepts derived from AI, including machine learning (ML) and deep learning (DL). ML is defined as the ability to learn data features without being explicitly programmed. It arises at the intersection of data science and computer science and aims at the efficiency of computing algorithms. In cancer research, ML has been increasingly used in predictive prognostic models. DL is defined as a subset of ML targeting multilayer computation processes. DL is less dependent on the understanding of data features than ML. Therefore, the algorithms of DL are much more difficult to interpret than ML, even potentially impossible. This review discussed the role of AI in the diagnostic, therapeutic and prognostic advances of gastric cancer. Models like convolutional neural networks (CNNs) or artificial neural networks (ANNs) achieved significant praise in their application. There is much more to be fully covered across the clinical administration of gastric cancer. Despite growing efforts, adapting AI to improving diagnoses for gastric cancer is a worthwhile venture. The information yield can revolutionize how we approach gastric cancer problems. Though integration might be slow and labored, it can be given the ability to enhance diagnosing through visual modalities and augment treatment strategies. It can grow to become an invaluable tool for physicians. AI not only benefits diagnostic and therapeutic outcomes, but also reshapes perspectives over future medical trajectory.