Artificial Intelligence to Implement Cost-saving Strategies for Colonoscopy Screening Based on in Vivo Prediction of Polyp Histology

N/ARecruitingNCT06041945

Istituto Clinico Humanitas1,800 enrolled

Overview

This three parallel-arms, randomized, multicenter trial is aimed at investigating the value of AI-assisted optical biopsy for differentiating between neoplastic and non-neoplastic polyps which will lead to the implementation of cost-saving strategies in screening programs. A cost-effectiveness analyses with the use of modern trial emulation analyses of large observational and clinical trial datasets and real-cost data will be conducted. To improve personalized treatment with a novel colonoscopy CADx risk-prediction tool, the investigators will even develop a novel deep learning algorithm for the optical biopsy of the alternative pathway of colorectal cancer carcinogenesis, namely the serrated pathway and develop cost-effectiveness models of AI-assisted optical biopsy in colorectal cancer screening that provides reliable information to identify cancer risk regardless of physicians' skill.

Study Type

INTERVENTIONAL

Allocation

RANDOMIZED

Purpose

PREVENTION

Masking

NONE

Enrollment

1,800

Conditions

Colonic Neoplasms

Interventions

Standard, high-definition colonoscopy with the use of CADe/CADx assistance, leave-in-situDEVICE

Polyps will be left in situ if diminutive (≤5 mm) in size, located in the rectum or sigma and optically diagnosed by the endoscopist using the system to be hyperplastic with high confidence, otherwise resected and sent to pathology.

Eligibility

Sex: ALLMin age: 40 YearsHealthy volunteers:

Medical Language ↔ Plain English

Inclusion Criteria: * All \>40 years-old patients undergoing colonoscopy for selected indications Exclusion Criteria: * patients with personal history of CRC, or IBD * patients affected with Lynch syndrome or Familiar Adenomatous Polyposis. * patients with inadequate bowel preparation (defined as Boston Bowel Preparation Scale \<2 in any colonic segment). * patients with previous colonic resection. * patients on antithrombotic therapy, precluding polyp resection. * patients who were not able or refused to give informed written consent.

Outcomes

Primary Outcomes

Non-inferiority in Adenoma Detection Rate

Non-inferiority in the Adenoma Detection Rate, defined as the proportion of participants with at least one adenoma (per-patient analysis) in the three arms, when adopting a cost-saving leave-in-situ strategy for non-neoplastic rectosigmoid diminutive polyps.

Time frame: 4 years

Secondary Outcomes

Negative Predictive Value for colorectal neoplasia

Negative Predictive Value for colorectal neoplasia when adopting a leave-in-situ strategy for rectosigmoid diminutive polyps based on the use of Artificial Intelligence.

Time frame: 4 years

Concordance between post-polypectomy surveillance and when adopting a leave-in-situ strategy

Concordance between post-polypectomy surveillance based on strategies based on optical diagnosis with Artificial Intelligence and those based on histology, and when adopting a leave-in-situ strategy for colorectal diminutive polyps based on the use of Artificial Intelligence

Time frame: 4 years

Change in the cost of polypectomy and histology in screening programs

Change in the cost of polypectomy and histology in screening programs, when implementing strategies based on Artificial Intelligence-based optical biopsy, without changes in benefit related with detection of colorectal neoplasia

Time frame: 4 years

Artificial Intelligence to Implement Cost-saving Strategies for Colonoscopy Screening Based on in Vivo Prediction of Polyp Histology

Overview

Conditions

Interventions

Eligibility

Locations (2)

Outcomes

Primary Outcomes

Secondary Outcomes

Central Contacts