Endoscopy 2020; 52(01): 52-60
DOI: 10.1055/a-0995-0084
Original article
© Georg Thieme Verlag KG Stuttgart · New York

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

Cesare Hassan
1  Department of Gastroenterology, Nuovo Regina Margherita Hospital, Rome, Italy
Raf Bisschops
2  Department of Gastroenterology and Hepatology, Catholic University of Leuven (KUL), TARGID, University Hospitals Leuven, Leuven, Belgium
Pradeep Bhandari
3  Solent Centre for Digestive Diseases, Portsmouth Hospital, Portsmouth, United Kingdom
Emmanuel Coron
4  Department of Hepatogastroenterology, Centre Hospitalier Universitaire Hotel Dieu, Nantes, France
Helmut Neumann
5  First Medical Department, University Medical Center Mainz, Mainz, Germany
Oliver Pech
6  Department of Gastroenterology and Interventional Endoscopy, Krankenhaus Barmherzige Brüder Regensburg, Regensburg, Germany
Loredana Correale
1  Department of Gastroenterology, Nuovo Regina Margherita Hospital, Rome, Italy
Alessandro Repici
7  Digestive Endoscopy Unit, Humanitas University, Milan, Italy
› Author Affiliations
Further Information

Publication History

submitted: 18 March 2019

accepted after revision: 12 July 2019

Publication Date:
13 September 2019 (online)


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.

Appendix s1, Figs. 1s – 3s, Tables 1s – 5s