Department of Surgery, Faculty of Medicine, the Chinese University of Hong Kong
Hong Kong
NCT Number: NCT07395570
Gastric cancer remains the 5th most common cancers worldwide. It also ranked 5th in the cancer related mortality, causing more than 650'000 deaths per year. Survival of gastric cancer is directly related to the stage of the presentation, with early stage cancers having a significantly better survival. Patients with stage I gastric cancer generally have a 5-year survival of more than 90%. In particular, T1a cancer confined to the mucosa are amenable for endoscopic resection, and patients who underwent such treatment have an excellent survival of 97.2% at 5 years. These patients are not only able to survive longer but also with good quality of life through organ preservation.
However, diagnosis of gastric cancer at an early stage has always been difficult. A meta-analysis of 22 studies from both East and Western population showed a gastric cancer miss rate of 9.4%. Early gastric cancer usually presents with subtle mucosal changes that are hard to detect endoscopically, especially for endoscopists with limited experience in early cancer diagnosis. Background chronic inflammation and high frequency of non-neoplastic lesions often pose significant diagnostic challenges for endoscopists to detect real neoplastic changes. In high incidence countries such as Japan and Korea, the combination of national screening programme as well as good endoscopy training program facilitated high proportion of early gastric cancer detection. Previous studies have showed that significant survival outcome difference between countries with high versus low early cancer detection rate.
Artificial intelligence has emerged as one of the promising technologies that helps enhance endoscopic performance. Numerous high quality randomized studies have demonstrated that computer assisted detection (CADe) system significantly improved colonic adenoma detection rate during screening colonoscopy. Development of gastric cancer CADe system has been much slower than colonic polyp detection. Despite the publication of numerous retrospective studies utilizing endoscopic images in differentiating benign versus malignant gastric lesions, there were only very few completed systems available for clinical real time application. A single centre randomized controlled trial from China demonstrated an improvement in the gastric neoplasm miss rate from 27.3% to 6.1 % when utilizing a novel CADe system.
A novel CADe prototype system (OIP-Ge1, Olympus Medical Corporations, Tokyo, Japan) has recently been developed. The system was developed through collaboration of multiple experts in diagnosing early gastric cancer, collecting more than 100'000 endoscopic images from dozens of high volume centres in Japan. There is currently no prospective clinical data on the actual performance of the prototype CADe system, especially when applied in regions with low proportion of early gastric cancer detection.
The purpose of this study is to investigate the clinical utility of the new CADe system in detection of gastric neoplasia among high risk patients.
If the current study confirms the significant difference in miss rate of gastric neoplasia with the CADe system, a multicentred international randomized controlled trial is planned to compare the efficacy of gastric neoplasia detection with or without the system.
This study is active but is not currently recruiting participants.
Notify Me18 year and older
All sexes
Interventional
Not applicable
Hong Kong
Healthy volunteers accepted: No
Only the study team can determine whether someone qualifies for participation.
Inclusion criteria
Exclusion criteria
The endoscopy would be performed using standardized video processing system (EVIS-X1 CV-1500, Olympus Medical Corporations, Tokyo, Japan) and gastroscope (GIF-EZ1500, GIF-XZ1200). A soft black hood (MAJ-1989) would be attached to the distal end of the endoscope. The video processing system would be connected to the OIP-Ge1 (Olympus Medical Corporations, Tokyo, Japan), the novel CADe system, allowing simultaneous artificial intelligence assisted lesion detection, when turned on using conventional white light imaging (WLI).
Time frame: 1 day
Number of patients in whom at least one histologically confirmed gastric neoplasia (Vienna III-V) is not identified during the first endoscopy without CADe, but is identified during the second endoscopy with CADe, divided by the total number of patients undergoing both examinations (unit: %) Miss rate (%) = [Number of patients with ≥1 neoplasia detected only on 2nd endoscopy with CADe] ÷ [Total number of patients undergoing both 1st and 2nd endoscopies] × 100.
Time frame: 1 day
Suspicious lesion(s) detected would appear as a green box on the main video monitor. Consistent appearance of the green box for more than 2 seconds on the same target area would be considered as a positively detected lesion by the CADe system. Gastric neoplasia detection rate is defined as the gastric neoplasia detection rate during the 2nd endoscopy examination with the assistance of CADe system. (unit: %) Detection rate (%) = [Number of patients with ≥1 neoplasia detected on 2nd endoscopy with CADe] ÷ [Total number of patients undergoing 2nd endoscopy with CADe] × 100.
Time frame: 1 day
Time in seconds from green box to appear to the stable dentification of a histologically confirmed neoplastic lesion (Vienna III-V), recorded automatically by the CADe system or by time-stamped video review. (unit: second)
Time frame: 1 day
Number of patients in whom at least one endoscopically identified non-neoplastic lesion is detected during the CADe-assisted examination divided by total number of patients undergoing CADe-assisted examination (unit: %).
Detection rate (%) = [Number of patients with ≥1 non-neoplastic gastric lesion detected] ÷ [Total number of patients examined] × 100.
Time frame: 1 day
Number of patients in whom at least one histologically confirmed gastric neoplasia is visible on the recorded video of the second endoscopy (on retrospective expert review) but is not detected by CADe (no stable box ≥2 seconds), divided by the total number of patients with gastric neoplasia on that second examination (unit: %).
Miss rate (%) = [Number of patients with ≥1 neoplastic lesion visible on video but not detected by CADe] ÷ [Total number of patients with gastric neoplasia during 2nd endoscopy] × 100.
Time frame: 1 day
Time in minutes from scope insertion to scope withdrawal, including the first examination without CADe and the second examination with CADe, measured using the endoscopy unit's time stamps. (unit: minute)
Time frame: 1 day
Total count of lesions (neoplastic and non-neoplastic) detected during each examination (first without CADe, second with CADe), divided by the number of procedures, reported as mean ± SD per procedure.
Mean lesions per procedure = [Total number of lesions detected in all procedures] ÷ [Total number of procedures].
Time frame: 1 day
Digital high-definition video capture system integrated with the endoscopy tower, summarized as percentage of procedures with complete, analyzable recordings. (unit: yes/no, %) Proportion with complete recording (%) = [Number of procedures with complete video adequate for review] ÷ [Total number of procedures] × 100.
Chinese University of Hong Kong
Other
Efficacy of Computer Aided Detection (CADe) System in Detecting Gastric Neoplasia - a Prospective Tandem Study
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