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Trial Title:
Evaluation of a Computer-aided Diagnosis System (CADx) in the Early Detection of Gastric Cancer in France. Cancer in France.
NCT ID:
NCT05928819
Condition:
Gastric Cancer
Conditions: Official terms:
Stomach Neoplasms
Conditions: Keywords:
diagnostic tool
AI
CAD
gastric cancer
therapeutic choice guidance
Study type:
Observational
Overall status:
Recruiting
Study design:
Time perspective:
Prospective
Intervention:
Intervention type:
Procedure
Intervention name:
Evaluation of the proportion of gastric neoplastic lesions detected by a computer-aided diagnosis system (CADx) compared with experienced endoscopists.
Description:
Evaluation of the proportion of gastric neoplastic lesions detected by a computer-aided
diagnosis system (CADx) compared with experienced endoscopists.
Arm group label:
Gastric lesion diagnostic
Summary:
Upper gastrointestinal (GI) cancers are one of the most common cancers worldwide. Except
for cardia cancers, the incidence of gastric cancer has decreased consistently since
1980, but remains at a high level. In France, gastric cancers are the 6th most common
cause of cancer-related mortality. The risk factors of upper GI cancers are well known
and their control could prevent the development of cancers: smoking cessation, reduction
of obesity, alcohol, eradication of Helicobacter pylori. But late presentation with upper
GI cancer results in a poorer prognosis. Patients with advanced (Stage IV) gastric cancer
have a five-year survival rate of 3.7% whereas patients whose gastric cancer is
discovered in its early stage (Stage I) have a significantly higher five-year survival
rate of 88.4%. Therefore, endoscopic detection of upper GI lesions at an earlier stage is
the single most effective measure for reducing cancer mortality. But upper GI cancer is
also often missed during examinations, and some studies demonstrated a missed cancer rate
of 2.3-13.9% in Western populations. In the past decade, accurate diagnosis during
endoscopy has become particularly important as dysplastic lesions and early gastric
cancers can be treated effectively with both endoscopic mucosal resection (EMR) and
endoscopic submucosal dissection (ESD), avoiding the morbidity and mortality associated
with gastrectomy. However, these early neoplastic lesions can be sometimes difficult to
distinguish from background mucosa, even with advanced imaging techniques (high
definition, chromoendoscopy).
In recent years, image recognition using artificial intelligence (AI) with deep learning
has dramatically improved and opened the door to more detailed image analysis and real
time application in various medical field, including endoscopy. For example, in the
colorectal cancer screening area, real time computer-aided detection systems (CADe) can
lead to significant increases in both polyp and adenoma detection rates.
CADe has also shown good performance in detection of Barrett's neoplasia during live
endoscopic procedures in order to more accurately locate the area to be biopsied.
Recently, a Chinese study showed that CADe achieved high diagnostic accuracy in detecting
upper GI cancers, with sensitivity similar to that of expert endoscopists and superior to
that of non-experts. This system could support non-experts by improving their diagnostic
accuracy to a level similar to that of experts and provide assistance for improving the
effectiveness of upper GI cancer diagnosis and screening.
Although encouraging results have been published regarding the use of AI in the diagnosis
of upper GI cancers, the clinical applicability of such systems in a European population
has yet to be investigated.
Therefore, we want to evaluate the diagnostic capability of a recent CADx compared to
endoscopists in order to improve the real-time detection of early gastric cancers in our
European center Edouard Herriot Hospital, Lyon, France, as well as 3 other tertiary
centers in France (Limoges, Rennes and Nancy University Hospitals).
With a high prevalence of stomach cancer, Japan is a world leader in high-quality
diagnostic upper GI endoscopy, and the clinical routine in this country differs
substantially from Western practice, with population-based screening programs. We will
use for our study a CADx developed by AI medical service Inc. (1-18-1, Higashiikebukuro,
Toshima-ku, Tokyo 170-0013, Japan), a Japanese company developing AI systems that
supports endoscopist's diagnosis for the digestive tract. A recent study involving AI
medical service system showed good results in the diagnosis of early gastric cancer
compared to endoscopists, with a significantly higher sensitivity.
Criteria for eligibility:
Study pop:
Every patient referred to our center for upper gastrointestinal endoscopy for
investigation and/or resection of gastric neoplastic lesion can join the cohort of this
study and will benefit from diagnosis and treatment by experienced endoscopists.
Sampling method:
Probability Sample
Criteria:
Inclusion Criteria:
- both gender patients even or older than 18 years old
- patient in need of proven diagnostic or therapeutic gastroscopy for gastric lesion
resection
- patient with French Health Insurance coverage
- obtaining of oral non opposition to research after loyal, clear and complete
delivery of information
Exclusion Criteria:
- previous attempt of lesion resection
- patient with no gastric lesion
- inadequate examination quality (gastroparesis)
- patient with health disorders needing short procedure times
Gender:
All
Minimum age:
18 Years
Maximum age:
N/A
Locations:
Facility:
Name:
Hôpital Edouard Herriot
Address:
City:
Lyon
Zip:
69437
Country:
France
Status:
Recruiting
Contact:
Last name:
Mathieu PIOCHE, MD
Phone:
04.72.11.01.45
Phone ext:
+33
Email:
mathieu.pioche@chu-lyon.fr
Contact backup:
Last name:
Laurent MAGAUD, CRA
Phone:
04 72 11 51 64
Phone ext:
+33
Email:
laurent.magaud@chu-lyon.fr
Start date:
January 1, 2023
Completion date:
August 31, 2024
Lead sponsor:
Agency:
Hospices Civils de Lyon
Agency class:
Other
Source:
Hospices Civils de Lyon
Record processing date:
ClinicalTrials.gov processed this data on November 12, 2024
Source: ClinicalTrials.gov page:
https://clinicaltrials.gov/ct2/show/NCT05928819