GAIN Project: Gastric Cancer and Artificial Intelligence

NCT06275997 | Not Yet Recruiting

• 1 other identifier: GAIN
• Study Type: interventional
• Target: 6,600
• Locations: 0 countries
• Sites: N/A

Brief 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

Outcome Measures

Primary Outcomes (1)

• Miss rate reduction

  change of the miss rate of early gastric cancer and dysplastic lesions at upper-endoscopy when using AI-assistance (tandem).

  2025: 12 months enrollment

Secondary Outcomes (2)

• Change number of Detections

  1 day procedure and follow up for 2 years

• patient satisfaction

  2025: during the 12 months enrollment

Study Arms (4)

Parallel arm 1

NO INTERVENTION

patients will undergo standard high-definition and high-quality upper-GI endoscopy for the detection of gastric lesions with histological mapping according to Sydney system

Parallel arm 2

ACTIVE COMPARATOR

patients will undergo high-definition and high quality upper-GI endoscopy with real-time assistance by real-time artificial intelligence for the detection of early gastric cancer and gastric dysplasia.

Device: Integration of Artificial Intelligence (AI) assistance to screening gastroscopy

Cross-over arm 1 (control)

OTHER

patients will undergo two standard high-definition and high-quality upper-GI endoscopies in tandem: the first will be without Artificial Intelligence assistance, and the second with Artificial Intelligence in order to define the miss rate for standard unassisted upper-GI endoscopy.

Device: Integration of Artificial Intelligence (AI) assistance to screening gastroscopy

Cross-over arm 2

ACTIVE COMPARATOR

patients will undergo two standard high-definition and high-quality upper-GI endoscopies in tandem: the first will be with Artificial Intelligence assistance, and the second without Artificial Intelligence in order to define the decrease of miss rate when assistance by Artificial Intelligence is implemented.

Device: Integration of Artificial Intelligence (AI) assistance to screening gastroscopy

Interventions

Integration of Artificial Intelligence (AI) assistance to screening gastroscopy DEVICE

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.

Cross-over arm 1 (control); Cross-over arm 2; Parallel arm 2

Eligibility Criteria

• Age: 60 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• All \>60 years-old patients undergoing upper-gastrointestinal (GI) endoscopy for selected indications in Italian areas at high-risk of gastric cancer (Lombardia, Emilia Romagna, Veneto, Friuli-Venezia Giulia).

You may not qualify if:

• contraindications to upper-GI endoscopy.
• contraindications to biopsy.
• active upper-GI bleeding or urgent upper-GI endoscopy.
• patients with previous upper-GI surgery involving the stomach.
• patients who were not able or refused to give informed written consent.

Sponsors & Collaborators

• Istituto Clinico Humanitas: lead

MeSH Terms

Conditions

Stomach Neoplasms

Condition Hierarchy (Ancestors)

Gastrointestinal Neoplasms; Digestive System Neoplasms; Neoplasms by Site; Neoplasms; Digestive System Diseases; Gastrointestinal Diseases; Stomach Diseases

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: NONE
• Purpose: PREVENTION
• Intervention Model: PARALLEL
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Model Details: Parallel/Crossover Study Model; Patients will be randomized 1:1:1:1

Study Record Dates

• First Submitted: February 2, 2024
• First Posted: February 23, 2024
• Study Start: June 10, 2024
• Primary Completion: June 1, 2026
• Study Completion (Estimated): June 1, 2028
• Last Updated: June 4, 2024

Record last verified: 2024-02