United European gastroenterology journal · 2022
Real-time use of artificial intelligence (CADEYE) in colorectal cancer surveillance of patients with Lynch syndrome-A randomized controlled pilot trial (CADLY).
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Hüneburg Robert, Bucksch Karolin, Schmeißer Friederike, Heling Dominik, Marwitz Tim, Aretz Stefan, Kaczmarek Dominik J, Kristiansen Glen, Hommerding Oliver, Strassburg Christian P, Engel Christoph, Nattermann Jacob
Аннотация
BACKGROUND: Lynch syndrome (LS), an autosomal dominant disorder caused by pathogenic germline variants in DNA mismatch repair (MMR) genes, represents the most common hereditary colorectal cancer (CRC) syndrome. Lynch syndrome patients are at high risk of CRC despite regular endoscopic surveillance.
OBJECTIVE: Our aim was to investigate the diagnostic performance of artificial intelligence (AI)-assisted colonoscopy in comparison to High-Definition white-light endoscopy (HD-WLE) for the first time.
METHODS: Patients ≥18 years with LS, with a pathogenic germline variant (MLH1, MHS2, MSH6), and at least one previous colonoscopy (interval 10-36 months) were eligible. Patients were stratified by previous CRC and affected MMR gene with a 1:1 allocation ratio (AI-assisted vs. HD white-light endoscopy) in this exploratory pilot trial.
RESULTS: Between Dec-2021 and Dec-2022, 101 LS patients were randomised and 96 patients were finally analyzed after exclusion of 5 patients due to insufficient bowel preparation. In the HD-WLE arm, adenomas were detected in 12/46 patients compared to 18/50 in the AI arm (26.1% [95% CI 14.3-41.1] vs. 36.0% [22.9-50.8]; p = 0.379). The use of AI-assisted colonoscopy especially increased detection of flat adenomas (Paris classification 0-IIb) (examinations with detected flat adenomas: 3/46 [6.5%] vs. 10/50 [20%]; p = 0.07; numbers of detected flat adenomas: 4/20 vs. 17/30, p = 0.018). The median withdrawal time did not differ significantly between HD-WLE and AI (14 vs. 15 min; p = 0.170).
CONCLUSION: We here present first data suggesting that real-time AI-assisted colonoscopy is a promising approach to optimize endoscopic surveillance in LS patients, in particular to improve the detection of flat adenomas.
OBJECTIVE: Our aim was to investigate the diagnostic performance of artificial intelligence (AI)-assisted colonoscopy in comparison to High-Definition white-light endoscopy (HD-WLE) for the first time.
METHODS: Patients ≥18 years with LS, with a pathogenic germline variant (MLH1, MHS2, MSH6), and at least one previous colonoscopy (interval 10-36 months) were eligible. Patients were stratified by previous CRC and affected MMR gene with a 1:1 allocation ratio (AI-assisted vs. HD white-light endoscopy) in this exploratory pilot trial.
RESULTS: Between Dec-2021 and Dec-2022, 101 LS patients were randomised and 96 patients were finally analyzed after exclusion of 5 patients due to insufficient bowel preparation. In the HD-WLE arm, adenomas were detected in 12/46 patients compared to 18/50 in the AI arm (26.1% [95% CI 14.3-41.1] vs. 36.0% [22.9-50.8]; p = 0.379). The use of AI-assisted colonoscopy especially increased detection of flat adenomas (Paris classification 0-IIb) (examinations with detected flat adenomas: 3/46 [6.5%] vs. 10/50 [20%]; p = 0.07; numbers of detected flat adenomas: 4/20 vs. 17/30, p = 0.018). The median withdrawal time did not differ significantly between HD-WLE and AI (14 vs. 15 min; p = 0.170).
CONCLUSION: We here present first data suggesting that real-time AI-assisted colonoscopy is a promising approach to optimize endoscopic surveillance in LS patients, in particular to improve the detection of flat adenomas.