Polyp detection rates as quality indicator in clinical versus screening colonoscopy.
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ABSTRACT:
SUBMITTER: Hoff G
PROVIDER: S-EPMC6338539 | biostudies-literature | 2019 Feb
REPOSITORIES: biostudies-literature
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Polyp detection rates as quality indicator in clinical versus screening colonoscopy.
Hoff G G Botteri E E Høie O O Garborg K K Wiig H H Huppertz-Hauss G G Moritz V V Bretthauer M M Holme Ø Ø
Endoscopy international open 20190118 2
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Project description:Background: The European Colonoscopy Quality Investigation (ECQI) Group aims to raise awareness for improvement in colonoscopy standards across Europe. We analysed data collected on a sample of procedures conducted across Europe to evaluate the achievement of the polyp detection rate (PDR) target. We also investigated factors associated with PDR, in the hope of establishing areas that could lead to a quality improvement. Methods: 6445 form completions from 12 countries between 2 June 2016 and 30 April 2018 were considered for this analysis. We performed an exploratory analysis looking at PDR according to European Society of Gastrointestinal Endoscopy (ESGE) definition. Stepwise multivariable logistic regression analysis was conducted to determine the most influential associated factors after adjusting for the other pre-specified variables. Results: In our sample there were 3365 screening and diagnostic procedures performed in those over 50 years. The PDR was 40.5%, which is comparable with the ESGE minimum standard of 40%. The variables found to be associated with PDR were in descending order: use of high-definition equipment, body mass index (BMI), patient gender, age group, and the reason for the procedure. Use of HD equipment was associated with a significant increase in the reporting of flat lesions (14.3% vs. 5.7%, p < 0.0001) and protruded lesions (34.7% vs. 25.4%, p < 0.0001). Conclusions: On average, the sample of European practice captured by the ECQI survey meets the minimum PDR standard set by the ESGE. Our findings support the ESGE recommendation for routine use of HD colonoscopy.
| S-EPMC8954761 | biostudies-literature
Project description:ImportanceHigh-quality colonoscopy reduces the risks of colorectal cancer by increasing the adenoma detection rate. Routine use of an automatic quality control system (AQCS) to assist in colorectal adenoma detection should be considered.ObjectiveTo evaluate the effect of an AQCS on the adenoma detection rate among colonoscopists who were moderate- and low-level detectors during routine colonoscopy.Design, setting, and participantsThis multicenter, single-blind, randomized clinical trial was conducted at 6 centers in China from August 1, 2021, to September 30, 2022. Data were analyzed from March 1 to June 30, 2023. Individuals aged 18 to 80 years were enrolled. Exclusion criteria were a history of inflammatory bowel disease, advanced colorectal cancer, and polyposis syndromes; known colorectal polyps without complete removal previously; a history of colorectal surgery; known stenosis or obstruction with contraindication for biopsy or prior failed colonoscopy; pregnancy or lactation; and refusal to participate. Intention-to-treat and per-protocol analysis was used.InterventionsStandard colonoscopy or AQCS-assisted colonoscopy.Main outcomes and measuresAdenoma detection rate.ResultsA total of 1254 participants (mean [SD] age, 51.21 [12.10] years; 674 [53.7%] male) were randomized (627 standard colonoscopy, 627 AQCS-assisted colonoscopy). Intention-to-treat analysis showed a significantly higher adenoma detection rate in the AQCS-assisted group vs standard colonoscopy group (32.7% vs 22.6%; relative risk [RR], 1.60; 95% CI, 1.23-2.09; P < .001). The adenoma detection rates were significantly higher in the AQCS group when considering pathology (nonadvanced adenomas, 30.1% vs 21.2%; RR, 1.52; 95% CI, 1.16-1.99; P = .002), and morphology (flat or sessile, 29.3% vs 20.4%, RR, 1.52; 95% CI, 1.16-2.00; P = .003). Use of AQCS significantly increased the adenoma detection rate of both the lower-level detectors (30.0% vs 20.0%; RR, 1.71; 95% CI, 1.24-2.35; P = .001) and the medium-level detectors (38.1% vs 27.7%; RR, 1.61; 95% CI, 1.07-2.43; P = .02). Similar increases were found for adenoma detection rates in the academic and nonacademic centers (academic: 29.3% vs 20.8%; RR, 1.58; 95% CI, 1.10-2.29; P = .01; nonacademic: 36.1% vs 24.5%; RR, 1.74; 95% CI, 1.23-2.46; P = .002). The number of adenomas per colonoscopy was significantly higher in the AQCS-assisted group (0.86 vs 0.48; RR, 1.50; 95% CI, 1.17-1.91; P = .001). The mean withdrawal time without intervention was slightly increased with AQCS assistance (6.78 vs 6.46 minutes; RR, 1.38; 95% CI, 1.26-1.52; P < .001). No serious adverse events were reported.Conclusions and relevanceIn this randomized clinical trial, AQCS assistance during routine colonoscopy increased adenoma detection rates and several related polyp parameters compared with standard colonoscopy in the lower- and medium-level detectors in academic and nonacademic settings. Routine use of AQCS to assist in colorectal adenoma detection and quality improvement should be considered.Trial registrationClinicalTrials.gov Identifier: NCT04901130.
| S-EPMC11783196 | biostudies-literature
Project description:BackgroundThe guidelines for colonoscopy present withdrawal time (WT) and adenoma detection rate (ADR) as the quality indicator. The purpose of this retrospective study is to analyze the predicting factors with polyp detection rate (PDR) as a surrogate for ADR by using comprehensive health checkup data, and assess the correlation between PDR per each colonic segment and WT, and factors influencing WT.MethodsOne thousand and thirty six consecutive health checkup cases from April 2015 to March 2016 were enrolled in this study, and 880 subjects who undertook colonoscopy without polyp removal or biopsy were divided into the two groups (polyp not detected group vs polyp detected group). The two groups were compared by subjects and clinical characteristics with univariate analysis followed by multivariate analysis. Colonoscopies with longer WT (≥ 6 min) and those with shorter WT (< 6 min) were compared by PDR per each colonic segment, and also by subjects and clinical characteristics.ResultsA total of 1009 subjects included two incomplete colonoscopies (CIR, 99.9%) and overall PDR was 35.8%. A multiple logistic regression model demonstrated that age, gender, and WT were significantly related factors for polyp detection (odds ratio, 1.036; 1.771; 1.217). PDR showed a linear increase as WT increased from 3 min to 9 min (r = 0.989, p = 0.000) and PDR with long WT group was higher than that with short WT group per each colonic segment, significantly in transverse (2.3 times, p = 0.004) and sigmoid colon (2.1 times, p = 0.001). Not only bowel preparation quality but also insertion difficulty evaluated by endoscopist were significant factors relating with WT (odds ratio, 3.811; 1.679).ConclusionThis study suggests that endoscopists should be recommended to take more time up to 9 min of WT to observe transverse and sigmoid colon, especially when they feel no difficulty during scope insertion.
| S-EPMC5362195 | biostudies-literature
Project description:Colorectal cancer (CRC) is one of the most common types of cancer with a high mortality rate. Colonoscopy is the preferred procedure for CRC screening and has proven to be effective in reducing CRC mortality. Thus, a reliable computer-aided polyp detection and classification system can significantly increase the effectiveness of colonoscopy. In this paper, we create an endoscopic dataset collected from various sources and annotate the ground truth of polyp location and classification results with the help of experienced gastroenterologists. The dataset can serve as a benchmark platform to train and evaluate the machine learning models for polyp classification. We have also compared the performance of eight state-of-the-art deep learning-based object detection models. The results demonstrate that deep CNN models are promising in CRC screening. This work can serve as a baseline for future research in polyp detection and classification.
| S-EPMC8370621 | biostudies-literature
Project description:Colorectal cancer (CRC) is a leading cause of cancer-related deaths worldwide. The best method to prevent CRC is with a colonoscopy. During this procedure, the gastroenterologist searches for polyps. However, there is a potential risk of polyps being missed by the gastroenterologist. Automated detection of polyps helps to assist the gastroenterologist during a colonoscopy. There are already publications examining the problem of polyp detection in the literature. Nevertheless, most of these systems are only used in the research context and are not implemented for clinical application. Therefore, we introduce the first fully open-source automated polyp-detection system scoring best on current benchmark data and implementing it ready for clinical application. To create the polyp-detection system (ENDOMIND-Advanced), we combined our own collected data from different hospitals and practices in Germany with open-source datasets to create a dataset with over 500,000 annotated images. ENDOMIND-Advanced leverages a post-processing technique based on video detection to work in real-time with a stream of images. It is integrated into a prototype ready for application in clinical interventions. We achieve better performance compared to the best system in the literature and score a F1-score of 90.24% on the open-source CVC-VideoClinicDB benchmark.
| S-EPMC9967208 | biostudies-literature
Project description:IntroductionQuality measures for colonoscopy such as adenoma detection rate (ADR) have been proposed to be surveilled for ensuring minimum standards. However, its direct measurement is time consuming and often neglected. Extrapolating ADR and other quality measures from polyp detection rate (PDR) can be a pragmatic alternative.ObjectiveTo determine quotients for estimating ADR and sessile serrated adenoma/polyp detection rate (SSA/P-DR) from PDR in an Australian cohort.MethodsConsecutive adult patient colonoscopies during a 1-year period were retrospectively assessed in a single Australian tertiary endoscopy center. Adenoma detection quotient (ADQ) and SSA/P detection quotient (SSA/P-DQ) were defined as the division of ADR and SSA/P-DR by PDR, respectively. The primary outcome was the number of procedures to achieve a stable cumulative ADQ and SSA/P-DQ. Secondary outcomes included evaluation of ADQ and SSA/P-DQ in different subsets.ResultsIn total, 2,657 colonoscopies were performed by 15 endoscopists in 2016. The ADR, SSA/P-DR, and PDR found were 32.2, 6.7, and 47.3%, respectively. The ADQ and SSA/P-DQ values found were 0.68 and 0.14, respectively. After approximately 500 procedures, both ADQ and SSA/P-DQ became stable. Interclass correlation coefficient (ICC) for the prediction of ADR from ADQ was excellent for all endoscopists that performed >177 procedures in that year (ICC 0.84).ConclusionsADQ and SSA/P-DQ values were consistent when over 500 procedures were analyzed. ADQ had an excellent correlation with ADR when >177 procedures per endoscopist were evaluated.
| S-EPMC7445665 | biostudies-literature
Project description:ObjectivesThe aim of this study was to test the ability of a commercially available natural language processing (NLP) tool to accurately extract examination quality-related and large polyp information from colonoscopy reports with varying report formats.BackgroundColonoscopy quality reporting often requires manual data abstraction. NLP is another option for extracting information; however, limited data exist on its ability to accurately extract examination quality and polyp findings from unstructured text in colonoscopy reports with different reporting formats.Study designNLP strategies were developed using 500 colonoscopy reports from Kaiser Permanente Northern California and then tested using 300 separate colonoscopy reports that underwent manual chart review. Using findings from manual review as the reference standard, we evaluated the NLP tool's sensitivity, specificity, positive predictive value (PPV), and accuracy for identifying colonoscopy examination indication, cecal intubation, bowel preparation adequacy, and polyps ≥10 mm.ResultsThe NLP tool was highly accurate in identifying examination quality-related variables from colonoscopy reports. Compared with manual review, sensitivity for screening indication was 100% (95% confidence interval: 95.3%-100%), PPV was 90.6% (82.3%-95.8%), and accuracy was 98.2% (97.0%-99.4%). For cecal intubation, sensitivity was 99.6% (98.0%-100%), PPV was 100% (98.5%-100%), and accuracy was 99.8% (99.5%-100%). For bowel preparation adequacy, sensitivity was 100% (98.5%-100%), PPV was 100% (98.5%-100%), and accuracy was 100% (100%-100%). For polyp(s) ≥10 mm, sensitivity was 90.5% (69.6%-98.8%), PPV was 100% (82.4%-100%), and accuracy was 95.2% (88.8%-100%).ConclusionNLP yielded a high degree of accuracy for identifying examination quality-related and large polyp information from diverse types of colonoscopy reports.
| S-EPMC5847417 | biostudies-literature
Project description:ObjectivePolyp detection rate (PDR) is an accepted measure of colonoscopy quality. Several factors may influence PDR including time of procedure and order of colonoscopy within a session. Our unit provides evening colonoscopy lists (6-9 pm). We examined whether colonoscopy performance declines in the evening.DesignData for all National Health Service (NHS) outpatient colonoscopies performed at Norfolk and Norwich University Hospital in 2011 were examined. Timing, demographics, indication and colonoscopy findings were recorded. Statistical analysis was performed using multivariate regression.ResultsData from 2576 colonoscopies were included: 1163 (45.1%) in the morning, 1123 (43.6%) in the afternoon and 290 (11.3%) in the evening. Overall PDR was 40.80%. Males, increasing age and successful caecal intubation were all significantly associated with higher polyp detection. The indications 'faecal occult blood screening' (p<0.001) and 'polyp surveillance' (p<0.001) were strongly positively associated and 'anaemia' (p=0.01) was negatively associated with PDR. Following adjustment for covariates, there was no significant difference in PDR between sessions. With the morning as the reference value, the odds ratio for polyp detection in the afternoon and evening were 0.93 (95% CI = 0.72-1.18) and 1.15 (95%CI = 0.82-1.61) respectively. PDR was not affected by rank of colonoscopy within a list, sedation dose or trainee-involvement.ConclusionsTime of day did not affect polyp detection rate in clinical practice. Evening colonoscopy had equivalent efficacy and is an effective tool in meeting increasing demands for endoscopy. Standardisation was shown to have a considerable effect as demographics, indication and endoscopist varied substantially between sessions. Evening sessions were popular with a younger population.
| S-EPMC4118755 | biostudies-literature
Project description:BackgroundArtificial intelligence (AI) assistance has been considered as a promising way to improve colonoscopic polyp detection, but there are limited prospective studies on real-time use of AI systems.MethodsWe conducted a prospective, multicenter, randomized controlled trial of patients undergoing colonoscopy at six centers. Eligible patients were randomly assigned to conventional colonoscopy (control group) or AI-assisted colonoscopy (AI group). AI assistance was our newly developed AI system for real-time colonoscopic polyp detection. Primary outcome is polyp detection rate (PDR). Secondary outcomes include polyps per positive patient (PPP), polyps per colonoscopy (PPC), and non-first polyps per colonoscopy (PPC-Plus).ResultsA total of 2352 patients were included in the final analysis. Compared with the control, AI group did not show significant increment in PDR (38.8% vs. 36.2%, p = 0.183), but its PPC-Plus was significantly higher (0.5 vs. 0.4, p < 0.05). In addition, AI group detected more diminutive polyps (76.0% vs. 68.8%, p < 0.01) and flat polyps (5.9% vs. 3.3%, p < 0.05). The effects varied somewhat between centers. In further logistic regression analysis, AI assistance independently contributed to the increment of PDR, and the impact was more pronounced for male endoscopists, shorter insertion time but longer withdrawal time, and elderly patients with larger waist circumference.ConclusionThe intervention of AI plays a limited role in overall polyp detection, but increases detection of easily missed polyps; ChiCTR.org.cn number, ChiCTR1800015607.
| S-EPMC8525182 | biostudies-literature
Project description:Deep learning object-detection models are being successfully applied to develop computer-aided diagnosis systems for aiding polyp detection during colonoscopies. Here, we evidence the need to include negative samples for both (i) reducing false positives during the polyp-finding phase, by including images with artifacts that may confuse the detection models (e.g., medical instruments, water jets, feces, blood, excessive proximity of the camera to the colon wall, blurred images, etc.) that are usually not included in model development datasets, and (ii) correctly estimating a more realistic performance of the models. By retraining our previously developed YOLOv3-based detection model with a dataset that includes 15% of additional not-polyp images with a variety of artifacts, we were able to generally improve its F1 performance in our internal test datasets (from an average F1 of 0.869 to 0.893), which now include such type of images, as well as in four public datasets that include not-polyp images (from an average F1 of 0.695 to 0.722).
| S-EPMC10001273 | biostudies-literature