The “valley sign” in small and diminutive adenomas: prevalence, interobserver agreement, and validation as an adenoma marker

Rex, Douglas K.; Ponugoti, Prasanna L.; Kahi, Charles J.

doi:10.1016/j.gie.2016.10.011

Cited by 18 publications

(9 citation statements)

References 8 publications

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“…First, the innovative approach of combining surface and pit/ vascular descriptors in the same BASIC classification resulted in a clear benefit in terms of accuracy. When restricting our analysis to surface descriptors, such benefit was mainly related to an irregular appearance of the surface of the adenomas, indirectly confirming the predictive strength of the previously described "valley sign" [13]. We preferred the term "irregular" to "valley sign" in order that we can incorporate additional findings from other categories, such as sessile serrated polyps, in the next validations of BASIC.…”

Section: Discussionsupporting

confidence: 69%

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

et al. 2019

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Background 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. Methods 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. Results 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 %]). Conclusions Based on the strength of prediction, the new BASIC classes for adenomatous and hyperplastic histology show favorable results for accuracy and confidence levels.

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Section: Discussionsupporting

confidence: 69%

“…Recently, specific features of the polyp surface have also been shown to predict polyp histology. The valley signa slight pseudodepression on the edge of the polyphas been shown to be highly specific for the diagnosis of adenoma [13], while a cloudy or irregular appearance and indistinct borders have been associated with sessile serrated polyp histology [14].…”

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confidence: 99%

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

et al. 2019

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“…The strengths of our study include the large number of lesions, the prospective identification of consecutive lesions, the standardized method for assuring polyp size, and the blinded assessment of the endoscopy photographs by two outside experts. In addition, we provide the actual photographs of lesions interpreted as normal tissue and as hyperplastic polyps in order to demonstrate that the lesions had classic Type II features of the NICE classification [9], and were correctly predicted endoscopically to be adenomas.…”

Section: Discussionmentioning

confidence: 99%

Disagreement between high confidence endoscopic adenoma prediction and histopathological diagnosis in colonic lesions ≤ 3 mm in size

et al. 2019

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Background Diminutive colorectal polyps resected during colonoscopy are sometimes histologically interpreted as normal tissue. The aim of this observational study was to explore whether errors in specimen handling or processing account in part for polyps ≤ 3 mm in size being interpreted as normal tissue by pathology when they were considered high confidence adenomas by an experienced endoscopist at colonoscopy. Methods One endoscopist photographed 900 consecutive colorectal lesions that were ≤ 3 mm in size and considered endoscopically to be high confidence conventional adenomas. The photographs were reviewed blindly to eliminate poor quality images. The remaining 644 endoscopy images were reviewed by two external experts who predicted the histology while blinded to the pathology results. Results Of 644 consecutive lesions ≤ 3 mm in size considered high confidence conventional adenomas by a single experienced colonoscopist, 15.4 % were reported as normal mucosa by pathology. The prevalence of reports of normal mucosa in polyps removed by cold snare and cold forceps were 15.2 % and 16.0 %, respectively. When endoscopy photographs were reviewed by two blinded outside experts, the lesions found pathologically to be adenomas and normal mucosa were interpreted as high confidence adenomas by endoscopic appearance in 96.9 % and 93.9 %, respectively, by Expert 1 (P = 0.15), and in 99.6 % and 100 %, respectively, by Expert 2 (P = 0.51). Conclusion Retrieval and/or processing of tissue specimens of tiny colorectal polyps resulted in some lesions being diagnosed as normal tissue by pathology despite being considered endoscopically to be high confidence adenomas. These findings suggest that pathology interpretation is not a gold standard for lesion management when this phenomenon is observed.

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“…We then tested the model’s accuracy on a consecutive sample of diminutive (≤5 mm) colorectal polyps that were video recorded in NBI and resected by DKR for histological analysis. DKR recorded the test videos for prospective use in another trial 27. All videos were fully deidentified.…”

Section: Methodsmentioning

confidence: 99%

Real-time differentiation of adenomatous and hyperplastic diminutive colorectal polyps during analysis of unaltered videos of standard colonoscopy using a deep learning model

Byrne

Chapados²,

Soudan³

et al. 2017

Gut

515

408

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backgroundIn general, academic but not community endoscopists have demonstrated adequate endoscopic differentiation accuracy to make the 'resect and discard' paradigm for diminutive colorectal polyps workable. Computer analysis of video could potentially eliminate the obstacle of interobserver variability in endoscopic polyp interpretation and enable widespread acceptance of 'resect and discard'. study design and methods We developed an artificial intelligence (AI) model for real-time assessment of endoscopic video images of colorectal polyps. A deep convolutional neural network model was used. Only narrow band imaging video frames were used, split equally between relevant multiclasses. Unaltered videos from routine exams not specifically designed or adapted for AI classification were used to train and validate the model. The model was tested on a separate series of 125 videos of consecutively encountered diminutive polyps that were proven to be adenomas or hyperplastic polyps. results The AI model works with a confidence mechanism and did not generate sufficient confidence to predict the histology of 19 polyps in the test set, representing 15% of the polyps. For the remaining 106 diminutive polyps, the accuracy of the model was 94% (95% CI 86% to 97%), the sensitivity for identification of adenomas was 98% (95% CI 92% to 100%), specificity was 83% (95% CI 67% to 93%), negative predictive value 97% and positive predictive value 90%. conclusions An AI model trained on endoscopic video can differentiate diminutive adenomas from hyperplastic polyps with high accuracy. Additional study of this programme in a live patient clinical trial setting to address resect and discard is planned. IntroductIonEndoscopists combine their knowledge of the spectrum of endoscopic appearances of precancerous lesions with meticulous mechanical exploration and cleaning of mucosal surfaces to maximise lesion detection during colonoscopy. An extension of detection is endoscopic prediction of lesion histology, including differentiation of precancerous lesions from non-neoplastic lesions, and prediction of deep submucosal invasion of cancer.1 2 Image analysis can guide whether lesion removal is necessary and direct an endoscopist to the best resection method. 1-3Image analysis during colonoscopy has achieved increasing acceptance as a means to accurately predict the histology of diminutive lesions, 4 5 which have minimal risk of cancer, 6 so that these diminutive lesions could be resected and discarded without pathological assessment or left in place without resection in the case of diminutive distal colon hyperplastic polyps.3 Discarding most diminutive lesions without pathological assessment has the potential for large cost saving with minimal risk.

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The “valley sign” in small and diminutive adenomas: prevalence, interobserver agreement, and validation as an adenoma marker

Cited by 18 publications

References 8 publications

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

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

Disagreement between high confidence endoscopic adenoma prediction and histopathological diagnosis in colonic lesions ≤ 3 mm in size

Real-time differentiation of adenomatous and hyperplastic diminutive colorectal polyps during analysis of unaltered videos of standard colonoscopy using a deep learning model

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