Optical diagnosis of colorectal polyp images using a newly developed computer-aided diagnosis system (CADx) compared with intuitive optical diagnosis

Quirine E. W. van der Zander; Ramon M. Schreuder; Roger Fonollà; Thom Scheeve; Fons van der Sommen; Bjorn Winkens; Patrick Aepli; Bu'Hussain Hayee; Andreas B. Pischel; Milan Stefanovic; Sharmila Subramaniam; Pradeep Bhandari; Peter H. N. de With; Ad A. M. Masclee; Erik J. Schoon

doi:10.1055/a-1343-1597

Endoscopy, Inhaltsverzeichnis

Endoscopy 2021; 53(12): 1219-1226
DOI: 10.1055/a-1343-1597

Original article

Optical diagnosis of colorectal polyp images using a newly developed computer-aided diagnosis system (CADx) compared with intuitive optical diagnosis

Quirine E. W. van der Zander ‡^*

¹Division of Gastroenterology and Hepatology, Maastricht University Medical Center + Maastricht, the Netherlands

²GROW, School for Oncology and Developmental Biology, Maastricht University, Maastricht, the Netherlands

,

Ramon M. Schreuder‡^*

³Division of Gastroenterology and Hepatology, Catharina Hospital Eindhoven, Eindhoven, the Netherlands

,

Roger Fonollà

⁴Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands

,

Thom Scheeve

⁴Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands

,

Fons van der Sommen

⁴Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands

,

Bjorn Winkens

⁵Department of Methodology and Statistics, CAPHRI, Care and Public Health Research Institute, Maastricht University, Maastricht, the Netherlands

,

Patrick Aepli

⁶Division of Gastroenterology and Hepatology, Luzerner Kantonsspital, Lucerne, Switzerland

,

Bu'Hussain Hayee

⁷Division of Gastroenterology and Hepatology, King’s College Hospital, London, United Kingdom

,

Andreas B. Pischel

⁸Division of Gastroenterology and Hepatology, University Hospital Gothenburg, Gothenburg, Sweden

,

Milan Stefanovic

⁹Division of Gastroenterology and Hepatology, Diagnostični Center Bled, Ljubljana, Slovenia

,

Sharmila Subramaniam

¹⁰Division of Gastroenterology and Hepatology, Queen Alexandra Hospital, Portsmouth, United Kingdom

,

Pradeep Bhandari

¹⁰Division of Gastroenterology and Hepatology, Queen Alexandra Hospital, Portsmouth, United Kingdom

,

Peter H. N. de With

⁴Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands

,

Ad A. M. Masclee

¹Division of Gastroenterology and Hepatology, Maastricht University Medical Center + Maastricht, the Netherlands

,

Erik J. Schoon

²GROW, School for Oncology and Developmental Biology, Maastricht University, Maastricht, the Netherlands

³Division of Gastroenterology and Hepatology, Catharina Hospital Eindhoven, Eindhoven, the Netherlands

› Institutsangaben

Abstract

Background Optical diagnosis of colorectal polyps remains challenging. Image-enhancement techniques such as narrow-band imaging and blue-light imaging (BLI) can improve optical diagnosis. We developed and prospectively validated a computer-aided diagnosis system (CADx) using high-definition white-light (HDWL) and BLI images, and compared the system with the optical diagnosis of expert and novice endoscopists.

Methods CADx characterized colorectal polyps by exploiting artificial neural networks. Six experts and 13 novices optically diagnosed 60 colorectal polyps based on intuition. After 4 weeks, the same set of images was permuted and optically diagnosed using the BLI Adenoma Serrated International Classification (BASIC).

Results CADx had a diagnostic accuracy of 88.3 % using HDWL images and 86.7 % using BLI images. The overall diagnostic accuracy combining HDWL and BLI (multimodal imaging) was 95.0 %, which was significantly higher than that of experts (81.7 %, P = 0.03) and novices (66.7 %, P < 0.001). Sensitivity was also higher for CADx (95.6 % vs. 61.1 % and 55.4 %), whereas specificity was higher for experts compared with CADx and novices (95.6 % vs. 93.3 % and 93.2 %). For endoscopists, diagnostic accuracy did not increase when using BASIC, either for experts (intuition 79.5 % vs. BASIC 81.7 %, P = 0.14) or for novices (intuition 66.7 % vs. BASIC 66.5 %, P = 0.95).

Conclusion CADx had a significantly higher diagnostic accuracy than experts and novices for the optical diagnosis of colorectal polyps. Multimodal imaging, incorporating both HDWL and BLI, improved the diagnostic accuracy of CADx. BASIC did not increase the diagnostic accuracy of endoscopists compared with intuitive optical diagnosis.

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References
1 Abu Dayyeh BK, Thosani N, Konda V. et al. ASGE Technology Committee systematic review and meta-analysis assessing the ASGE PIVI thresholds for adopting real-time endoscopic assessment of the histology of diminutive colorectal polyps. Gastrointest Endosc 2015; 81: 502.e01-502.e16
2 Rex DK, Kahi C, O’Brien M. et al. The American Society for Gastrointestinal Endoscopy PIVI (Preservation and Incorporation of Valuable Endoscopic Innovations) on real-time endoscopic assessment of the histology of diminutive colorectal polyps. Gastrointest Endosc 2011; 73: 419-422
3 Vleugels JLA, Dijkgraaf MGW, Hazewinkel Y. et al. Effects of training and feedback on accuracy of predicting rectosigmoid neoplastic lesions and selection of surveillance intervals by endoscopists performing optical diagnosis of diminutive polyps. Gastroenterology 2018; 154: 1682-1693
4 Ladabaum U, Fioritto A, Mitani A. et al. Real-time optical biopsy of colon polyps with narrow band imaging in community practice does not yet meet key thresholds for clinical decisions. Gastroenterology 2013; 144: 81-91
5 van de Wetering AJP, Meulen LWT, Bogie RMM. et al. Optical diagnosis of diminutive polyps in the Dutch Bowel Cancer Screening Program: are we ready to start?. Endosc Int Open 2020; 8: E257-e265
6 Yoshida N, Yagi N, Inada Y. et al. Ability of a novel blue laser imaging system for the diagnosis of colorectal polyps. Dig Endosc 2014; 26: 250-258
7 Djinbachian R, Dube AJ, von Renteln D. Optical diagnosis of colorectal polyps: recent developments. Curr Treat Options Gastroenterol 2019; 17: 99-114
8 Bisschops R, Hassan C, Bhandari P. et al. BASIC (BLI Adenoma Serrated International Classification) classification for colorectal polyp characterization with blue light imaging. Endoscopy 2018; 50: 211-220
9 Nakano A, Hirooka Y, Yamamura T. et al. Comparison of the diagnostic ability of blue laser imaging magnification versus pit pattern analysis for colorectal polyps. Endosc Int Open 2017; 5: E224-e231
10 Rondonotti E, Paggi S, Amato A. et al. Blue-light imaging compared with high-definition white light for real-time histology prediction of colorectal polyps less than 1 centimeter: a prospective randomized study. Gastrointest Endosc 2019; 89: 554-564
11 Subramaniam S, Hayee B, Aepli P. et al. Optical diagnosis of colorectal polyps with blue light imaging using a new international classification. United European Gastroenterol J 2019; 7: 316-325
12 Kominami Y, Yoshida S, Tanaka S. et al. Computer-aided diagnosis of colorectal polyp histology by using a real-time image recognition system and narrow-band imaging magnifying colonoscopy. Gastrointest Endosc 2016; 83: 643-649
13 Byrne MF, Chapados N, Soudan F. et al. Real-time differentiation of adenomatous and hyperplastic diminutive colorectal polyps during analysis of unaltered videos of standard colonoscopy using a deep learning model. Gut 2019; 68: 94-100
14 Misawa M, Kudo SE, Mori Y. et al. Characterization of colorectal lesions using a computer-aided diagnostic system for narrow-band imaging endocytoscopy. Gastroenterology 2016; 150: 1531-1532
15 Chen PJ, Lin MC, Lai MJ. et al. Accurate classification of diminutive colorectal polyps using computer-aided analysis. Gastroenterology 2018; 154: 568-575
16 Erickson-Bhatt SJ, Boppart SA. Biophotonics for assessing breast cancer. In: Meglinski I. , ed. Biophotonics for medical applications. Cambridge: Woodhead Publishing; 2015: 175-214
17 World Medical Association. World Medical Association Declaration of Helsinki: ethical principles for medical research involving human subjects. JAMA 2013; 310: 2191-2194
18 European Parliament, Council of the European Union. Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC (General Data Protection Regulation). Official Journal of the European Union 2016; L119: 1-88
19 Leggett B, Whitehall V. Role of the serrated pathway in colorectal cancer pathogenesis. Gastroenterology 2010; 138: 2088-2100
20 Schlemper RJ, Riddell RH, Kato Y. et al. The Vienna classification of gastrointestinal epithelial neoplasia. Gut 2000; 47: 251-255
21 Foss FA, West KP, McGregor AH. Pathology of polyps detected in the bowel cancer screening programme. Diagn Histopathol 2011; 17: 495-504
22 Selvaraju RR, Cogswell M, Das A. et al. Grad-CAM: Visual explanations from deep networks via gradient-based localization. Proceedings of the IEEE International Conference on Computer Vision (ICCV); 2017 Oct 22–29; Venice, Italy. 2017: 618-626
23 Tan M, Le Q. EfficientNet: rethinking model scaling for convolutional neural networks. Proceedings of the 36th International Conference on Machine Learning; 2019. Proceedings of Machine Learning Research PMLR 2019; 97: 6105-6114
24 Van den Brink N, Holbrechts B, Brand PLP. et al. Role of intuitive knowledge in the diagnostic reasoning of hospital specialists: a focus group study. BMJ Open 2019; 9: e022724
25 Subramaniam S, Hayee B, Aepli P. et al. Optical diagnosis of colorectal polyps with blue light imaging using a new international classification. United European Gastroenterol J 2019; 7: 316-325
26 Altman DG. Practical statistics for medical research. New York: Chapman & Hall/CRC Press; 1999
27 IJspeert JEG, Bastiaansen BA, van Leerdam ME. et al. Development and validation of the WASP classification system for optical diagnosis of adenomas, hyperplastic polyps and sessile serrated adenomas/polyps. Gut 2016; 65: 963-970
28 Rosner B. Fundamentals of biostatistics. Mason, OH, United States: Cengage Learning, Inc; 2015
29 McCarthy WF. Assessment of sample size and power for the analysis of clustered matched-pair data. COBRA Preprint Series. 2007: 28
30 Min M, Su S, He W. et al. Computer-aided diagnosis of colorectal polyps using linked color imaging colonoscopy to predict histology. Sci Rep 2019; 9: 2881
31 Song EM, Park B, Ha CA. et al. Endoscopic diagnosis and treatment planning for colorectal polyps using a deep-learning model. Sci Rep 2020; 10: 30
32 Bisschops R, East JE, Hassan C. et al. Advanced imaging for detection and differentiation of colorectal neoplasia: European Society of Gastrointestinal Endoscopy (ESGE) Guideline – Update 2019. Endoscopy 2019; 51: 1155-1179
33 Hassan C, Bisschops R, Bhandari P. et al. Predictive rules for optical diagnosis of <10-mm colorectal polyps based on a dedicated software. Endoscopy 2020; 52: 52-60

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