Prediction Of Polyp Histology Research Articles

Reliability of Kudo’s Glandular Pit Pattern in Predicting Colorectal Lesion Histology at Routine Colonoscopy with Digital Chromoendoscopy

Background. The large number of lesions detected via high-definition (HD) imaging during colonoscopy calls for the reliable real-time histological characterization of polyps, especially diminutive and small ones, to permit tailored management based on the neoplastic risk, such as a “resect-and-discard” or a “diagnose-and-leave” strategy for low-risk adenomas and hyperplastic polyps (HPs). The Kudo classification of glandular pit pattern is currently used for predicting polyp histology. Aim. The aim in this study was to assess whether Kudo’s glandular pit pattern, assessed via HD digital chromoendoscopy (i-Scan) without magnification and optical enhancement, reliably predicts polyp histology and differentiates neoplastic lesions (NLs) from non-neoplastic lesions (non-NLs) during routine colonoscopy. Methods. Consecutive colorectal lesions recorded in a database over 12 months, with Kudo’s glandular pit pattern classification, were retrospectively compared with histology. The diagnostic accuracy and negative predictive value (NPV) for adenomatous histology of Kudo’s pit patterns were assessed separately for diminutive (≤5 mm) and small (6–9 mm) polyps, accordingly to the American Society for Gastrointestinal Endoscopy (ASGE) Preservation and Incorporation of Valuable Endoscopic Innovations (PIVI), and in large (≥10 mm) lesions. Results. A total of 2230 lesions were recorded: 898 diminutive, 704 small, and 628 large. Kudo’s type II pit pattern was prevalent in diminutive polyps and recognized mostly in HPs (83.27%); it was also found in 38.8% of adenomas. In the right colon, Kudo’s type II pit pattern was prevalent in adenomas (70.04% vs. 20.74% in HPs); among the serrated lesions, it was evenly distributed between HPs and adenomas. Kudo’s type IIIL/IIIs/IV pit pattern was prevalent in NLs (61% vs. 8.37% of non-NLs) in diminutive polyps, evenly distributed between non-NLs and NLs in small polyps, and found only in NLs in large polyps. Kudo’s type Vi/Vn pit pattern correctly identified all but one adenocarcinoma. The NPV for adenomatous histology did not reach the recommended 90% PIVI threshold for differentiation between NLs and non-NLs in diminutive polyps showing Kudo’s type II pit pattern and in small polyps showing type IIIL/IIIs/IV pit pattern. Conclusions. Kudo’s pit pattern classification carried out with digital chromoendoscopy (i-Scan) during routine colonoscopy does not allow the reliable differentiation between non-NLs and NLs in diminutive and small polyps, so a “diagnose-and-leave” strategy for diminutive polyps may leave undetected adenomas, while a “resect-and-discard” strategy could miss lesions requiring closer follow-up.

Computer-Aided Diagnosis for Leaving Colorectal Polyps In Situ : A Systematic Review and Meta-analysis.

Computer-aided diagnosis (CADx) allows prediction of polyp histology during colonoscopy, which may reduce unnecessary removal of nonneoplastic polyps. However, the potential benefits and harms of CADx are still unclear. To quantify the benefit and harm of using CADx in colonoscopy for the optical diagnosis of small (≤5-mm) rectosigmoid polyps. Medline, Embase, and Scopus were searched for articles published before 22 December 2023. Histologically verified diagnostic accuracy studies that evaluated the real-time performance of physicians in predicting neoplastic change of small rectosigmoid polyps without or with CADx assistance during colonoscopy. The clinical benefit and harm were estimated on the basis of accuracy values of the endoscopist before and after CADx assistance. The certainty of evidence was assessed using the GRADE (Grading of Recommendations Assessment, Development and Evaluation) framework. The outcome measure for benefit was the proportion of polyps predicted to be nonneoplastic that would avoid removal with the use of CADx. The outcome measure for harm was the proportion of neoplastic polyps that would be not resected and left in situ due to an incorrect diagnosis with the use of CADx. Histology served as the reference standard for both outcomes. Ten studies, including 3620 patients with 4103 small rectosigmoid polyps, were analyzed. The studies that assessed the performance of CADx alone (9 studies; 3237 polyps) showed a sensitivity of 87.3% (95% CI, 79.2% to 92.5%) and specificity of 88.9% (CI, 81.7% to 93.5%) in predicting neoplastic change. In the studies that compared histology prediction performance before versus after CADx assistance (4 studies; 2503 polyps), there was no difference in the proportion of polyps predicted to be nonneoplastic that would avoid removal (55.4% vs. 58.4%; risk ratio [RR], 1.06 [CI, 0.96 to 1.17]; moderate-certainty evidence) or in the proportion of neoplastic polyps that would be erroneously left in situ (8.2% vs. 7.5%; RR, 0.95 [CI, 0.69 to 1.33]; moderate-certainty evidence). The application of optical diagnosis was only simulated, potentially altering the decision-making process of the operator. Computer-aided diagnosis provided no incremental benefit or harm in the management of small rectosigmoid polyps during colonoscopy. European Commission. (PROSPERO: CRD42023402197).

S136 Real World Validation of an Artificial Intelligence Characterization Support System for Prediction of Polyp Histology in Colonoscopy: Interim Analysis of a Prospective Multicenter Study

Introduction: Colonoscopy is the gold standard for screening of colorectal cancer. Diminutive and hyperplastic polyps do not increase a patient’s risk for colorectal cancer. Prediction of hyperplastic polyp histology is crucial for the resect and discard strategy, which saves cost and decreases procedure time in colonoscopy. This study aims to validate the performance of computer-aided diagnosis (CADx) in distinguishing between hyperplastic and neoplastic polyps during colonoscopy in a real-world setting. Methods: We conducted a prospective multicentre study comparing CADx (Fujifilm Corp., Tokyo) with endocopist optical prediction of polyp histology. We first recorded the optical diagnosis according to the NICE classification by the endoscopist. Following this, the CADx tool was switched on and its prediction recorded. Imaged polyps were resected for histological analysis, which formed the gold standard. Primary outcome was diagnostic accuracy (defined by sensitivity for diagnosis of hyperplastic polyps and concordance rate). Bowel preparation, polyp size and difficulty in location were recorded for subgroup analysis. Results: 414 patients were assessed for eligibility across 4 large tertiary institutions in Singapore between February 2021 and June 2022. 625 polyps (303 hyperplastic, 322 neoplastic) were detected in 257 patients. Concordance rates for CADx and endoscopist predictions were 74.1% [95% confidence interval (CI) 70.5%-77.5%] and 73.1% (95% CI 69.5%-76.6%%), respectively (p=NS). Sensitivity for diagnosis of hyperplastic polyps was 84.2% (95% CI 79.6%-88.1%) and 77.6% (95% CI 72.4%-82.1%) for CADx and endoscopists, respectively (p=NS). CADx also showed superior performance in predicting hyperplastic histology in diminutive polyps compared to endoscopist optical prediction using the NICE classification (sensitivity 81.7%; 95% CI 76.2%-86.4%, versus 76.3%; 95% CI 70.4%-81.5%, respectively). Diagnostic accuracy was similar when analysed according to bowel preparation and difficulty in polyp location during colonoscopy (defined as polyp location behind fold, around bend, or unable to position at 6 o’clock). Conclusion: CADx showed a trend towards increased diagnostic accuracy in prediction of hyperplastic polyp histology during colonoscopy compared to endoscopist prediction in this interim analysis. The superior performance in sensitivity for disgnosis of hyperplastic polyps was also seen in diminutive polyps, but not with poor bowel preparation and difficult polyp location.

Artificial intelligence-based colorectal polyp histology prediction using narrow-band image-magnifying colonoscopy: a stepping stone for clinical practice.

Artificial intelligence-based colorectal polyp histology prediction using narrow-band image-magnifying colonoscopy: a stepping stone for clinical practice.

Artificial Intelligence-Based Colorectal Polyp Histology Prediction: High Accuracy in Larger Polyps.

Artificial Intelligence-Based Colorectal Polyp Histology Prediction: High Accuracy in Larger Polyps.

Artificial Intelligence-Based Colorectal Polyp Histology Prediction by Using Narrow-Band Image-Magnifying Colonoscopy.

Background/AimsWe have been developing artificial intelligence based polyp histology prediction (AIPHP) method to classify Narrow Band Imaging (NBI) magnifying colonoscopy images to predict the hyperplastic or neoplastic histology of polyps. Our aim was to analyze the accuracy of AIPHP and narrow-band imaging international colorectal endoscopic (NICE) classification based histology predictions and also to compare the results of the two methods. MethodsWe studied 373 colorectal polyp samples taken by polypectomy from 279 patients. The documented NBI still images were analyzed by the AIPHP method and by the NICE classification parallel. The AIPHP software was created by machine learning method. The software measures five geometrical and color features on the endoscopic image. ResultsThe accuracy of AIPHP was 86.6% (323/373) in total of polyps. We compared the AIPHP accuracy results for diminutive and non-diminutive polyps (82.1% vs. 92.2%; p=0.0032). The accuracy of the hyperplastic histology prediction was significantly better by NICE compared to AIPHP method both in the diminutive polyps (n=207) (95.2% vs. 82.1%) (p<0.001) and also in all evaluated polyps (n=373) (97.1% vs. 86.6%) (p<0.001) ConclusionsOur artificial intelligence based polyp histology prediction software could predict histology with high accuracy only in the large size polyp subgroup.

The Workgroup Serrated Polyps and Polyposis (WASP) classification for optical diagnosis of colorectal diminutive polyps with iScan and the impact of the revised World Health Organization (WHO) criteria.

Background and aimsThe Workgroup Serrated Polyps and Polyposis (WASP) developed criteria for optical diagnosis of colorectal polyps. The aims of this study were: (1) to improve optical diagnosis of diminutive colorectal polyps, especially SSLs, after training endoscopists in applying WASP criteria on videos of polyps obtained with iScan and (2) to evaluate if the WASP criteria are still useful when polyps are pathologically revised according to the World Health Organization (WHO) 2019 criteria.MethodsTwenty‐one endoscopists participated in a training session and predicted polyp histology on 30 videos of diminutive polyps, before and after training (T0 and T1). After three months, they scored another 30 videos (T2). Primary outcome was overall diagnostic accuracy (DA) at T0, T1 and T2. Polyps were histopathologically classified according to the WHO 2010 and 2019 criteria.ResultsOverall DA (both diminutive adenomas and SSLs) significantly improved from 0.58 (95% CI 0.55–0.62) at T0 to 0.63 (95% CI 0.60–0.66, p = 0.004) at T1. For SSLs, DA did not change with 0.51 (95% CI 0.46–0.56) at T0 and 0.55 (95% CI 0.49–0.60, p = 0.119) at T1. After three months, overall DA was 0.58 (95% CI 0.54–0.62, p = 0.787, relative to T0) while DA for SSLs was 0.48 (95% CI 0.42–0.55, p = 0.520) at T2. After pathological revision according to the WHO 2019 criteria, DA of all polyps significantly changed at all time points.ConclusionA training session in applying WASP criteria on endoscopic videos made with iScan did not improve endoscopists' long‐term ability to optically diagnose diminutive polyps. The change of DA following polyp revision according to the revised WHO 2019 criteria suggests that the WASP classification may need revision.

Predictive rules for optical diagnosis of < 10-mm colorectal polyps based on a dedicated software.

The BASIC classification for predicting in vivo colorectal polyp histology incorporates both surface and pit/vessel descriptor domains. This study aimed to define new BASIC classes for adenomatous and hyperplastic polyps. A video library (102 still images/videos of < 10-mm polyps using white-light [WLI] and blue-light imaging [BLI]) was reviewed by seven expert endoscopists. Polyps were rated according to the individual descriptors of the three BASIC domains (surface/pit/vessel). A model to predict polyp histology (adenomatous or hyperplastic) was developed using multivariable logistic regression and subsequent "leave-one-out" cross-validation. New BASIC rules were then defined by Delphi agreement. The overall accuracy of these rules when used by experts was evaluated according to the level of confidence and light type. The strength of prediction for adenomatous histology from 2175 observations assessed by area under the curve (AUC; 95 % confidence interval) was poor-to-fair for the surface descriptors (0.50 [0.33 - 0.69] for mucus; 0.68 [0.57 - 0.79] for irregular surface), but stronger for pits (0.87 [0.80 - 0.96] for featureless/round/not round) and vessels (0.80 [0.65 - 0.87] for not present/lacy/pericryptal). By combining the domains, a good-to-excellent prediction was shown (AUC 0.89 [0.81 - 0.96]). After the definition of new BASIC rules for adenomatous and hyperplastic polyps, accuracy for high confidence BLI predictions was 90.3 % (86.3 % - 93.2 %), which was superior to high confidence WLI (83.7 % [77.3 % - 87.7 %]) and low confidence BLI predictions (77.7 % [61.1 % - 88.6 %]). Based on the strength of prediction, the new BASIC classes for adenomatous and hyperplastic histology show favorable results for accuracy and confidence levels.

A Review of New and Emerging Techniques For Optical Diagnosis of Colonic Polyps.

Endoscopic imaging is a rapidly progressing field and benefits from miniaturization of advanced imaging technologies, which may allow accurate real-time characterization of lesions. The concept of the "optical biopsy" to predict polyp histology has gained prominence in recent years and may become clinically applicable with the advent of new imaging technology. This review aims to discuss current evidence and examine the emerging technologies as applied to the optical diagnosis of colorectal polyps. A structured literature search and review has been carried out of the evidence for diagnostic accuracy of image-enhanced endoscopy and emerging endoscopic imaging technologies. The image-enhanced endoscopy techniques are reviewed, including their basic scientific principles and current evidence for effectiveness. These include the established image-enhancement technologies such as narrow-band imaging, i-scan, and Fuji intelligent chromoendoscopy. More recent technologies including optical enhancement, blue laser imaging, and linked color imaging are discussed. Adjunctive imaging techniques in current clinical use are discussed, such as autofluorescence imaging and endocytoscopy. The emerging advanced imaging techniques are reviewed, including confocal laser endomicroscopy, optical coherence tomography, and Raman spectroscopy. Large studies of the established image-enhancement techniques show some role for the optical diagnosis of polyp histology, although results have been mixed, and at present only the technique of narrow-band imaging is appropriate for the diagnosis of low-risk polyps when used by an expert operator. Other image-enhancement techniques will require further study to validate their accuracy but show potential to support the use of a "resect-and-discard" approach to low-risk polyps. New technologies show exciting potential for real-time diagnosis, but further clinical studies in humans have yet to be performed.

Near focus NBI endoscopy plus acetic acid for optical polyp characterization in the colorectum - A proof of principle study

Background and study aim: Optical polyp characterization (OPC) in the colorectum is an upcoming challenge for endoscopists. Narrow band imanging (NBI) has been proposed to be helpful for OPC. However, data from clinical studies have shown that quality of OPC differs markedly between endoscopists. The aim of this study was to test the value of a combined NBI plus acetic acid (NBI + AA) approach for OPC in the colorectum.Patients and methods: This was a prospective, single-arm study at a tertiary referral center in Germany. The study was designed as a proof of principle study. Initially polyps were characterized using High-definition white light (HDWL) only. Additionally, the same polyps were investigated using NBI + AA (1.5% solution) in order to predict polyp pathology in a real time setting. The near focus function was used for both HDWL and NBI + AA assessment. The primary endpoint was accuracy of colorectal polyp prediction when using NBI + AA.Results: A total of 63 polyps were detected in 55 patients. NBI + AA based accuracy of real-time predictions was 85.5% compared to 80.6% using HDWL (p = .450). Accuracy was 90.2% in the high confidence setting for both NBI + AA and HDWL predictions. A higher share of polyps were assessed with high confidence when using NBI + AA compared to HDWL (p = .006). The use of NBI + AA led to a better identification of polyp margins (p < .001) compared to HDWL.Conclusions: The use of acetic acid led to a high level of accuracy and confidence in the prediction of polyp histology. These data justify further investigation in a randomized controlled study.Trial registration: ClinicalTrials.gov identifier: NCT02760381.

Optical diagnosis of colorectal polyps with Blue Light Imaging using a new international classification.

Blue Light Imaging (BLI) is a new imaging technology that enhances mucosal surface and vessel patterns. A specific BLI classification was recently developed to enable better characterisation of colorectal polyps (BLI Adenoma Serrated International Classification (BASIC)). The aim of this study was to validate the diagnostic performance of BASIC in predicting polyp histology in experienced and trainee endoscopists. Five experienced and five trainee endoscopists evaluated high-definition white light (HDWL) and BLI images from 45 small polyps to assess baseline accuracy, sensitivity, specificity, and positive and negative predictive values (NPVs) of polyp histology. Each endoscopist was trained with the BLI classification before repeating the exercise. Results were compared pre- and post-training. The overall pre-training accuracy improved from 87% to 94%. The sensitivity and NPV of adenoma diagnosis also improved significantly from 79% to 96% and 81% to 95% with BASIC training. This improvement was noted in both groups. The interobserver level of agreement was very good (K = 0.90) in the experienced cohort and good (K = 0.66) in the trainee group post-training. BLI is a useful tool for optical diagnosis, and the use of BASIC with adequate training can significantly improve the accuracy, sensitivity and NPV of adenoma diagnosis.

Optical Technologies for Endoscopic Real-Time Histologic Assessment of Colorectal Polyps: A Meta-Analysis.

Accurate, real-time, endoscopic risk stratification of colorectal polyps would improve decision-making and optimize clinical efficiency. Technologies to manipulate endoscopic optical outputs can be used to predict polyp histology in vivo; however, it remains unclear how accuracy has progressed and whether it is sufficient for routine clinical implementation. A meta-analysis was conducted by searching MEDLINE, Embase, and the Cochrane Library. Studies were included if they prospectively deployed an endoscopic optical technology for real-time in vivo prediction of adenomatous colorectal polyps. Polyposis and inflammatory bowel diseases were excluded. Bayesian bivariate meta-analysis was performed, presenting 95% confidence intervals (CI). One hundred two studies using optical technologies on 33,123 colorectal polyps were included. Digital chromoendoscopy differentiated neoplasia (adenoma and adenocarcinoma) from benign polyps with sensitivity of 92.2% (90.6%-93.9% CI) and specificity of 84.0% (81.5%-86.3% CI), with no difference between constituent technologies (narrow-band imaging, Fuji intelligent Chromo Endoscopy, iSCAN) or with only diminutive polyps. Dye chromoendoscopy had sensitivity of 92.7% (90.1%-94.9% CI) and specificity of 86.6% (82.9%-89.9% CI), similarly unchanged for diminutive polyps. Spectral analysis of autofluorescence had sensitivity of 94.4% (84.0%-99.1% CI) and specificity of 50.9% (13.2%-88.8% CI). Endomicroscopy had sensitivity of 93.6% (85.3%-98.3% CI) and specificity of 92.5% (81.8%-98.1% CI). Computer-aided diagnosis had sensitivity of 88.9% (74.2%-96.7% CI) and specificity of 80.4% (52.6%-95.7% CI). Prediction confidence and endoscopist experience alone did not significantly improve any technology. The only subgroup to demonstrate a negative predictive value for adenoma above 90% was digital chromoendoscopy, making high confidence predictions of diminutive recto-sigmoid polyps. Chronologic meta-analyses show a falling negative predictive value over time. A significant publication bias exists. This novel approach to meta-analysis demonstrates that existing optical technologies are increasingly unlikely to allow safe "resect and discard" strategies and that step-change innovation may be required. A "diagnose and leave" strategy may be supported for diminutive recto-sigmoid polyps diagnosed with high confidence; however, limitations exist in the evidence base for this cohort.

Computer-aided prediction of polyp histology on white light colonoscopy using surface pattern analysis.

This study aimed to evaluate a new computational histology prediction system based on colorectal polyp textural surface patterns using high definition white light images. Textural elements (textons) were characterized according to their contrast with respect to the surface, shape, and number of bifurcations, assuming that dysplastic polyps are associated with highly contrasted, large tubular patterns with some degree of bifurcation. Computer-aided diagnosis (CAD) was compared with pathological diagnosis and the diagnosis made by endoscopists using Kudo and Narrow-Band Imaging International Colorectal Endoscopic classifications. Images of 225 polyps were evaluated (142 dysplastic and 83 nondysplastic). The CAD system correctly classified 205 polyps (91.1 %): 131/142 dysplastic (92.3 %) and 74/83 (89.2 %) nondysplastic. For the subgroup of 100 diminutive polyps (≤ 5 mm), CAD correctly classified 87 polyps (87.0 %): 43/50 (86.0 %) dysplastic and 44/50 (88.0%) nondysplastic. There were no statistically significant differences in polyp histology prediction between the CAD system and endoscopist assessment. A computer vision system based on the characterization of the polyp surface in white light accurately predicted colorectal polyp histology.

Post-polypectomy surveillance interval based on flexible spectral color imaging enhancement (FICE) with magnifying zoom imaging for optical biopsy.

Abstract Background and study aims Post-polypectomy surveillance interval (SI) is determined based on the number, size, and histology of colorectal polyps. Electronic chromoendoscopy in association with magnifying imaging colonoscopy allows “in vivo” polyp histology prediction. Colorectal polyps ≤ 5 mm can be resected and discarded without pathologic assessment if the endoscopic technology when used with high confidence provides ≥ 90 % agreement between the post-polypectomy SI and the SI based on pathological assessment. The aim of this study was to evaluate the agreement between the post-polypectomy SI based on flexible spectral color imaging enhancement (FICE) chromoendoscopy in association with magnified imaging and the pathology-based SI. Patients and methods Each diagnosed colorectal polyp received a histology prediction (neoplastic or non-neoplastic) based on the FICE capillary-vessel pattern classification. Each prediction was classified as high or low confidence. SI based on the FICE prediction was compared to the pathology-based SI recommendation according to the US Multi-Society Task Force on Colorectal Cancer guideline. Sensitivity, specificity and accuracy of FICE in diagnosing neoplastic lesions were compared with the pathology assessment. Interobserver and intraobserver agreement for FICE-based SI predictions was evaluated using the kappa coefficient. Results A total of 267 polyps had histology prediction assessed with high confidence in 136 patients. Sensitivity of FICE was 98.7 % (95 % CI: 93.5 – 99.3) and specificity was 62.5 % (95 % CI: 43.6 – 78.9). Prediction accuracy was 94.4 % (95 % CI: 88.6 – 96 – 1) in differentiating between neoplastic and non-neoplastic lesions. Therefore, magnifying FICE colonoscopy-based SI recommendation was consistent with pathological assessment in 88.3 % of general cases (95 % CI: 82.1 – 92.6) and in 89.7 % (95 % CI: 83 – 94.5) of the high-confidence evaluation cases. The intraobserver agreement value for FICE-based SI predictions was 0.87 (high-confidence evaluations), and the interobserver agreement values were 0.78 (high- and low-confidence evaluations) and 0.82 (high-confidence evaluations) (95 % CI: 0.79 – 0.95). Conclusions FICE-based SI demonstrated 89.7 % concordance with the pathology-based SI.

Leaving colorectal polyps in place can be achieved with high accuracy using blue light imaging (BLI).

A negative predictive value of more than 90% is proposed by the American Society of Gastrointestinal Endoscopy Preservation and Incorporation of Valuable Endoscopic Innovations (PIVI) statement for a new technology in order to leave distal diminutive colorectal polyps in place without resection. To our knowledge, no prior prospective study has yet evaluated the feasibility of the most recently introduced blue light imaging (BLI) system for real-time endoscopic prediction of polyp histology for the specific endpoint of leaving hyperplastic polyps in place. Prospective assessment of real-time prediction of colorectal polyps by using BLI. In total, 177 consecutive patients undergoing screening or surveillance colonoscopy were included. Colorectal polyps were evaluated in real-time by using high-definition endoscopy and the BLI technology without optical magnification. Before resection, the endoscopist described each polyp according to size, shape and surface characteristics (pit and vascular pattern, colour and depression), and histology was predicted with a level of confidence (high or low). Histology was predicted with high confidence in 92.5% of polyps. Sensitivity of BLI for prediction of adenomatous histology was 92.68%, with a specificity and accuracy of 94.87 and 93.75%, respectively. Following the recommendation of the PIVI statement, positive and negative predictive values were calculated with values of 95 and 92.5%, respectively. Prediction of surveillance based on both US and European guidelines was correctly predicted in 91% of patients. The most recently introduced BLI technology is accurate enough to leave distal colorectal polyps in place without resection. BLI also allowed for assignment of postpolypectomy surveillance intervals. This approach therefore has the potential to reduce costs and risks associated with the redundant removal of diminutive colorectal polyps.

Development and validation of the SIMPLE endoscopic classification of diminutive and small colorectal polyps.

Prediction of histology of small polyps facilitates colonoscopic treatment. The aims of this study were: 1) to develop a simplified polyp classification, 2) to evaluate its performance in predicting polyp histology, and 3) to evaluate the reproducibility of the classification by trainees using multiplatform endoscopic systems. In phase 1, a new simplified endoscopic classification for polyps - Simplified Identification Method for Polyp Labeling during Endoscopy (SIMPLE) - was created, using the new I-SCANOE system (Pentax, Tokyo, Japan), by eight international experts. In phase 2, the accuracy, level of confidence, and interobserver agreement to predict polyp histology before and after training, and univariable/multivariable analysis of the endoscopic features, were performed. In phase 3, the reproducibility of SIMPLE by trainees using different endoscopy platforms was evaluated. Using the SIMPLE classification, the accuracy of experts in predicting polyps was 83 % (95 % confidence interval [CI]77 % - 88 %) before and 94 % (95 %CI89 % - 97 %) after training (P = 0.002). The sensitivity, specificity, positive predictive value, and negative predictive value after training were 97 %, 88 %, 95 %, and 91 %. The interobserver agreement of polyp diagnosis improved from 0.46 (95 %CI0.30 - 0.64) before to 0.66 (95 %CI0.48 - 0.82) after training. The trainees demonstrated that the SIMPLE classification is applicable across endoscopy platforms, with similar post-training accuracies for narrow-band imaging NBI classification (0.69; 95 %CI0.64 - 0.73) and SIMPLE (0.71; 95 %CI0.67 - 0.75). Using the I-SCANOE system, the new SIMPLE classification demonstrated a high degree of accuracy for adenoma diagnosis, meeting the ASGE PIVI recommendations. We demonstrated that SIMPLE may be used with either I-SCANOE or NBI.

Bright spot regions segmentation and classification for specular highlights detection in colonoscopy videos

A novel specular highlights detection method in colonoscopy videos is presented. The method is based on a model of appearance defining specular highlights as bright spots which are highly contrasted with respect to adjacent regions. Our approach proposes two stages: segmentation and then classification of bright spot regions. The former defines a set of candidate regions obtained through a region growing process with local maxima as initial region seeds. This process creates a tree structure which keeps track, at each growing iteration, of the region frontier contrast; final regions provided depend on restrictions over contrast value. Non-specular regions are filtered through a classification stage performed by a linear SVM classifier using model-based features from each region. We introduce a new validation database with more than 25, 000 regions along with their corresponding pixel-wise annotations. We perform a comparative study against other approaches. Results show that our method is superior to other approaches, with our segmented regions being closer to actual specular regions in the image. Finally, we also present how our methodology can also be used to obtain an accurate prediction of polyp histology.

Mo2021 Predicting Polyp Histology - Development and Validation of an International Simple Endoscopic Classification of Small Colorectal Polyps Using the Novel I-Scan Optical Enhancement Colonoscope

Mo2021 Predicting Polyp Histology - Development and Validation of an International Simple Endoscopic Classification of Small Colorectal Polyps Using the Novel I-Scan Optical Enhancement Colonoscope

Narrow-band imaging vs. high definition white light for optical diagnosis of small colorectal polyps: a randomized multicenter trial.

The aim of the study was to compare the latest narrow-band imaging (NBI) device with high-definition white light (HDWL) endoscopy for accuracy of real-time optical diagnosis of small colorectal polyps. We conducted a randomized, prospective, multicenter trial at three study sites in Germany. In the NBI arm, endoscopists used NBI for the prediction of polyp pathology on the basis of the NBI International Colorectal Endoscopic classification. In the HDWL arm, NBI was not used for optical classification of polyp histology. The primary outcome was accuracy of optical diagnoses (neoplastic vs. non-neoplastic) in small polyps measuring < 10 mm. Secondary end points included sensitivity and negative predictive value (NPV). A total of 380 patients were randomized 1:1 to either the NBI or HDWL arm. A total of 421 polyps measuring < 10 mm were detected (55.8 % neoplastic, 44.2 % non-neoplastic). Accuracy, sensitivity, and NPV were 73.7 %, 82.4 %, and 75.5 %, respectively, in the NBI arm and 79.2 %, 79.8 %, and 73.4 %, respectively, in the HDWL arm (P = 0.225, P = 0.667, P = 0.765). More polyps were assessed with high confidence in the HDWL arm (82.6 %) than in the NBI arm (73.7 %; P = 0.038). The NPV of the prediction of neoplastic histology in diminutive polyps (≤ 5 mm) rated with high confidence was 90.3 % in the NBI arm. We detected significant differences between the participating study sites in the performance data of predictions. The levels of accuracy for real-time prediction of polyp histology (< 10 mm) did not differ between NBI and HDWL for optical diagnosis. Variation in the performance of optical diagnosis was apparent between study centers. ClinicalTrials.gov (NCT02009774).

PTU-029 Nice Classification for Optical Diagnosis of Colonic Lesions is Equally Applicable When Using Blue Laser Imaging

Introduction Optical diagnosis of colonic polyps offers the potential for significant improvements in colonoscopy efficiency and cost saving. BLI is a new, push button, image enhancement endoscopy technology using two laser light sources to accentuate mucosal surface and vascular pattern of polyps. It’s visual appearance is similar to NBI. This study assessed whether the validated NICE classification system for colonic polyps (NBI) was applicable for BLI images of polyps. Objectives To assess whether NICE classification could be used to interpret BLI images To assess the diagnostic performance of BLI imaging modalities in characterising colonic polyps Methods High quality still images of 25 polyps were retrieved from an endoscopy image database. For each polyp we examined 4 different images: BLI, BLI with magnification (BLI MAG), BLI bright (BLI BR) and BLI BR with magnification (BLI BR MAG) (total of 100 images). Four experienced endoscopists were asked to make an optical diagnosis based on NICE (NBI International Colo rectal Endoscopic classification) for each BLI modality images. Results A total of 100 polyp images were reviewed. Reviewers were able to identify a minimum of one NICE feature in all cases (100%) and all three NICE features (colour, vessel and surface pattern) in 90% of the images. There was no significant difference in the accuracy of predicting polyp histology between BLI BR and BLI BR MAG (89.6% vs 92%, p = 0.47) or BLI and BLI MAG (90.5% vs 92%, p = 0.8). However, the confidence levels were significantly increased when presented with magnified images (BLI BR vs BLI BR MAG, 55.4% vs 91.3%, p Conclusion Our preliminary data highlights that the NICE classification could be used efficiently to interpret BLI images. The confidence levels in making an optical diagnosis were significantly increased with BLI magnification. Further studies are needed to validate these observations. Disclosure of Interest None Declared