Literature DB >> 31519023

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

Cesare Hassan1, Raf Bisschops2, Pradeep Bhandari3, Emmanuel Coron4, Helmut Neumann5, Oliver Pech6, Loredana Correale1, Alessandro Repici7.   

Abstract

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. © Georg Thieme Verlag KG Stuttgart · New York.

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Year:  2019        PMID: 31519023     DOI: 10.1055/a-0995-0084

Source DB:  PubMed          Journal:  Endoscopy        ISSN: 0013-726X            Impact factor:   10.093


  2 in total

1.  Resect and discard: Is it ready or time to shift gear?

Authors:  Raf Bisschops; Mário Dinis-Ribeiro
Journal:  Endosc Int Open       Date:  2020-06-16

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

Authors:  Istvan Racz; Andras Horvath; Noemi Kranitz; Gyongyi Kiss; Henriett Regoczi; Zoltan Horvath
Journal:  Clin Endosc       Date:  2021-09-23
  2 in total

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