Dataset Information

Real-time use of artificial intelligence (CADEYE) in colorectal cancer surveillance of patients with Lynch syndrome-A randomized controlled pilot trial (CADLY).

ABSTRACT:

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.

SUBMITTER: Huneburg R

PROVIDER: S-EPMC9892476 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature

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Publications

Real-time use of artificial intelligence (CADEYE) in colorectal cancer surveillance of patients with Lynch syndrome-A randomized controlled pilot trial (CADLY).

Hüneburg Robert R Bucksch Karolin K Schmeißer Friederike F Heling Dominik D Marwitz Tim T Aretz Stefan S Kaczmarek Dominik J DJ Kristiansen Glen G Hommerding Oliver O Strassburg Christian P CP Engel Christoph C Nattermann Jacob J

United European gastroenterology journal 20221226 1

<h4>Background</h4>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.<h4>Objective</h4>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 t ...[more]

PMID: 36571259

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Project description:ObjectiveLynch Syndrome (LS) carriers have a significantly increased risk of developing colorectal cancer (CRC) during their lifetimes. Further stratification of this patient population may help in identifying additional risk factors that predispose to colorectal carcinogenesis. In most LS patients CRC may arise from adenomas, although an alternative non-polypoid carcinogenesis pathway has been proposed for PMS2 carriers. Using data from our institutional LS cohort, our aim was to describe our current colorectal screening outcomes with a focus on the incidence of adenomas in the context of different MMR genotypes and patient demographics such as gender, race, and ethnicity.DesignWe collected demographics, genetic, colonoscopy, and pathology results from a total of 163 LS carriers who obtained regular screening care at MD Anderson Cancer Center. Data were extracted from the electronic health records into a REDCap database for analysis. Logistic regressions were performed to measure the association between MMR variants and the likelihood of adenomas, advanced adenomas, and CRC. Then, we analyzed the cumulative incidences of these outcomes for the first 36 months following enrollment using Kaplan-Meier incidence curves, and Cox proportional hazard regressions.ResultsOn multivariate analysis, age (≥45 years old) was associated with an increased risk of developing adenomas (P=0.034). Patients with a prior or active cancer status were less likely to develop adenomas (P=0.015), despite of the lack of association between surgical history with this outcome (P=0.868). We found no statistically significant difference in likelihood of adenoma development between MLH1 and MSH2/EPCAM, MSH6, and PMS2 carriers. Moreover, we observed no statistically significant difference in the likelihood of advanced adenomas or CRC for any measured covariates. On Cox proportional hazard, compared to MLH1 carriers, the incidence of adenomas was highest among MSH2/EPCAM carriers during for the first 36-months of follow-up (P<0.001). We observed a non-statistically significant trend for Hispanics having a higher and earlier cumulative incidence of adenomas compared to non-Hispanics (P=0.073). No MMR carrier was more likely to develop advanced adenomas. No difference in the incidence of CRC by MMR gene (P=0.198).ConclusionScreening recommendations for CRC in LS patients should be based on specific MMR variants and should also be tailored to consider patient demographics.

Project description:Artificial intelligence (AI) has increasingly been employed in multiple fields, and there has been significant interest in its use within gastrointestinal endoscopy. Computer-aided detection (CAD) can potentially improve polyp detection rates and decrease miss rates in colonoscopy. However, few clinical studies have evaluated real-time CAD during colonoscopy. In this study, we analyze the efficacy of a novel real-time CAD system during colonoscopy. This was a single-arm prospective study of patients undergoing colonoscopy with a real-time CAD system. This AI-based system had previously been trained using manually labeled colonoscopy videos to help detect neoplastic polyps (adenomas and serrated polyps). In this pilot study, 300 patients at two centers underwent elective colonoscopy with the CAD system. These results were compared to 300 historical controls consisting of consecutive colonoscopies performed by the participating endoscopists within 12 months prior to onset of the study without the aid of CAD. The primary outcome was the mean number of adenomas per colonoscopy. Use of real-time CAD trended towards increased adenoma detection (1.35 vs 1.07, p = 0.099) per colonoscopy though this did not achieve statistical significance. Compared to historical controls, use of CAD demonstrated a trend towards increased identification of serrated polyps (0.15 vs 0.07) and all neoplastic (adenomatous and serrated) polyps (1.50 vs 1.14) per procedure. There were significantly more non-neoplastic polyps detected with CAD (1.08 vs 0.57, p < 0.0001). There was no difference in ≥ 10 mm polyps identified between the two groups. A real-time CAD system can increase detection of adenomas and serrated polyps during colonoscopy in comparison to historical controls without CAD, though this was not statistically significant. As this pilot study is underpowered, given the findings we recommend pursuing a larger randomized controlled trial to further evaluate the benefits of CAD.

Dataset Information

Real-time use of artificial intelligence (CADEYE) in colorectal cancer surveillance of patients with Lynch syndrome-A randomized controlled pilot trial (CADLY).

Background

Objective

Methods

Results

Conclusion

Publications

Real-time use of artificial intelligence (CADEYE) in colorectal cancer surveillance of patients with Lynch syndrome-A randomized controlled pilot trial (CADLY).

Similar Datasets

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