NCT06275997

GAIN Project: Gastric Cancer and Artificial Intelligence

Gastric Cancer and Artificial Intelligence: a National-level Project

• Status: Not Yet Recruiting
• Phase: N/A
• Study type: Interventional
• Enrollment: 6,600 (estimated)
• Sponsor: Istituto Clinico Humanitas · Academic / Other
• Sex: All
• Age: 60 Years
• Healthy volunteers: Not accepted

Summary

Our GAIN project comprises four core work packages (WPs): WP1. Nation-level randomized controlled trial; WP2. Development of an innovative AI tool; WP3. Novel microsimulation modelling; WP4. Patient inclusion. The nation-level multi-center tandem randomized controlled trial (WP1) will contribute to a better understanding of how the real-time AI algorithm can reduce miss rate of early gastric cancer and dysplasia during gastroscopy. Moreover, the innovation project will contribute to development of a novel AI tool (WP2) that can stratify the risk of gastric cancer by identifying in vivo precancerous conditions. Furthermore, a microsimulation modelling will allow us to predict how the use of AI can prevent gastric cancer and affect cost and patients' burdens. The assessment of the balance between benefits and harms is quite crucial especially for this type of medical device because the value of innovative tools is sometimes overestimated due to stakeholders' enthusiasm (WP3). Finally, we will take care of patients' perspective throughout the study project by including patient organization in both WP1, 2, and 3 (WP4).

Conditions

• Gastric Cancer

Interventions

Type	Name	Description
DEVICE	Integration of Artificial Intelligence (AI) assistance to screening gastroscopy	Two novel deep learning systems, namely one for endoscopy and one for pathology, will be trained and validated for the diagnosis of gastric atrophy and metaplasia, including extension and severity. Both of the algorithms will be validated against the cases not used for the training phases. Approximately, the partition will be 5 to 1. The benefit and harm of AI-assistance for early diagnosis of gastric cancer will be simulated by developing a Markov model on the natural history of gastric cancer from dysplasia to early and advanced cancer, as well as by the impact of a GS on its natural history. This will also simulate the potential effect of lead- and length-time bias. These data will be incorporated in the simulation model in order to include them in the decision-making process on whether AI-assistance for gastric cancer detection should be or not recommended to health systems.

Timeline

• Start date: 2024-06-10
• Primary completion: 2026-06-01
• Completion: 2028-06-01
• First posted: 2024-02-23
• Last updated: 2024-06-04