Clinical trial NCT05141409: The COMBO CAD Study
Official title: The COMBO CAD Study: Characterization cOMparison Between twO CAD Systems
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: Artificial Intelligence
Lead sponsor: Istituto Clinico Humanitas
Sponsor class: OTHER
First posted: 2021-12-02
Enrollment: 500
Sex: ALL
Minimum age (years): 40
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: All patients aged 40 or older undergoing a colonoscopy for gastrointestinal symptoms, fecal immunohistochemical test positivity, primary screening or post-polypectomy surveillance.

Brief summary: Implementation of clinical strategies based on optical diagnosis of \<5 mm colorectal polyps may lead to a substantial saving of economic and financial resources. Despite this, 84.2% of European endoscopists reported not to use such strategies - also named as leave-in situ and resect- and-discard - in their practice due to the fear of an incorrect optical diagnosis.

Indeed, accuracy of optical diagnosis is operator-dependent, and values reported in the community setting are below the safety thresholds proposed for its incorporation in clinical practice.

Artificial intelligence (AI) is being increasingly explored in different domains of medicine, particularly those entailing image analysis. As optical diagnosis involves subitaneous processing of multiple images, searching for specific visual clues, and recognizing well-defined visual patterns, AI systems has the potential to help endoscopists in distinguish neoplastic from non-neoplastic polyps, making the characterization process more reliable and objective. Computer-Aided-Diagnosis systems aiming at characterization are called CADx.

Preliminary data on CADx showed a high feasibility and accuracy of AI for optical diagnosis of colorectal polyp, and initial experiences in clinical practice confirmed preliminary results.

To assess the potential benefit and risk of AI-assisted optical diagnosis with standard colonoscopy, we exploited two new Computer-Aided-Diagnosis systems (CAD-EYE® Fujifilm Co., and GI-Genius® Medtronic) that provide the endoscopist with a real-time polyp characterization without the need of optical magnification.


Eligibility criteria: Inclusion Criteria:

- All patients aged 40 or older undergoing a colonoscopy for gastrointestinal symptoms, fecal immunohistochemical test positivity, primary screening or post-polypectomy surveillance

Exclusion Criteria:

* subjects with personal history of CRC, or IBD.
* Subjects 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.

Primary outcomes:
measure: AI-assisted optical diagnosis performance
timeFrame: 6 Months
Secondary outcomes:
measure: AI alone optical diagnosis performance
timeFrame: 6 Months
Other outcomes:
Locations:
city: Rozzano
country: Italy
facility: Department of Gastroenterology, Humanitas Research Hospital
state: Milano
Study officials:
References: