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CC BY 4.0 · Endosc Int Open 2024; 12(10): E1102-E1117
DOI: 10.1055/a-2403-3103
Review

Improving the endoscopic recognition of early colorectal carcinoma using artificial intelligence: current evidence and future directions

Ayla Thijssen 
1   GROW Research Institute for Oncology and Reproduction, Maastricht University, Maastricht, Netherlands (Ringgold ID: RIN5211)
2   Department of Gastroenterology and Hepatology, Maastricht Universitair Medisch Centrum+, Maastricht, Netherlands (Ringgold ID: RIN199236)
,
Ramon-Michel Schreuder 
3   Department of Gastroenterology and Hepatology, Catharina Hospital, Eindhoven, Netherlands (Ringgold ID: RIN3168)
1   GROW Research Institute for Oncology and Reproduction, Maastricht University, Maastricht, Netherlands (Ringgold ID: RIN5211)
,
Nikoo Dehghani
4   Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
,
Marieke Schor
5   University Library, Department of Education and Support, Maastricht University, Maastricht, Netherlands (Ringgold ID: RIN5211)
,
Peter H.N. de With
4   Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
,
Fons van der Sommen 
4   Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
,
Jurjen J. Boonstra
6   Department of Gastroenterology and Hepatology, Leids Universitair Medisch Centrum, Leiden, Netherlands (Ringgold ID: RIN4501)
,
Leon M.G. Moons
7   Department of Gastroenterology and Hepatology, University Medical Center Utrecht, Utrecht, Netherlands
,
Erik J. Schoon
3   Department of Gastroenterology and Hepatology, Catharina Hospital, Eindhoven, Netherlands (Ringgold ID: RIN3168)
1   GROW Research Institute for Oncology and Reproduction, Maastricht University, Maastricht, Netherlands (Ringgold ID: RIN5211)
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Abstract

Background and study aims Artificial intelligence (AI) has great potential to improve endoscopic recognition of early stage colorectal carcinoma (CRC). This scoping review aimed to summarize current evidence on this topic, provide an overview of the methodologies currently used, and guide future research.

Methods A systematic search was performed following the PRISMA-Scr guideline. PubMed (including Medline), Scopus, Embase, IEEE Xplore, and ACM Digital Library were searched up to January 2024. Studies were eligible for inclusion when using AI for distinguishing CRC from colorectal polyps on endoscopic imaging, using histopathology as gold standard, reporting sensitivity, specificity, or accuracy as outcomes.

Results Of 5024 screened articles, 26 were included. Computer-aided diagnosis (CADx) system classification categories ranged from two categories, such as lesions suitable or unsuitable for endoscopic resection, to five categories, such as hyperplastic polyp, sessile serrated lesion, adenoma, cancer, and other. The number of images used in testing databases varied from 69 to 84,585. Diagnostic performances were divergent, with sensitivities varying from 55.0% to 99.2%, specificities from 67.5% to 100% and accuracies from 74.4% to 94.4%.

Conclusions This review highlights that using AI to improve endoscopic recognition of early stage CRC is an upcoming research field. We introduced a suggestions list of essential subjects to report in research regarding the development of endoscopy CADx systems, aiming to facilitate more complete reporting and better comparability between studies. There is a knowledge gap regarding real-time CADx system performance during multicenter external validation. Future research should focus on development of CADx systems that can differentiate CRC from premalignant lesions, while providing an indication of invasion depth.

Keywords

Endoscopy Lower GI Tract - Colorectal cancer - Polyps / adenomas / ... - Diagnosis and imaging (inc chromoendoscopy, NBI, iSCAN, FICE, CLE...) - Quality and logistical aspects - Image and data processing, documentatiton

Supplementary Material



Publikationsverlauf

Eingereicht: 22. Mai 2024

Angenommen nach Revision: 21. August 2024

Accepted Manuscript online:
26. August 2024

Artikel online veröffentlicht:
10. Oktober 2024

© 2024. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution License, permitting unrestricted use, distribution, and reproduction so long as the original work is properly cited. (https://creativecommons.org/licenses/by/4.0/).

Georg Thieme Verlag KG
Rüdigerstraße 14, 70469 Stuttgart, Germany

 

  • References

  • 1 Breekveldt ECH, Lansdorp-Vogelaar I, Toes-Zoutendijk E. et al. Colorectal cancer incidence, mortality, tumour characteristics, and treatment before and after introduction of the faecal immunochemical testing-based screening programme in the Netherlands: a population-based study. Lancet Gastroenterol Hepatol 2022; 7: 60-68
    CrossrefPubMedSuche in Google Scholar
  • 2 Denis B, Gendre I, Perrin P. et al. Management of large polyps in a colorectal cancer screening program with fecal immunochemical test: a community- and population-based observational study. Endosc Int Open 2021; 9: E1649-E1657
    Thieme ConnectPubMedSuche in Google Scholar
  • 3 Meulen LWT, van de Wetering AJP, Debeuf MPH. et al. Optical diagnosis of T1 CRCs and treatment consequences in the Dutch CRC screening programme. Gut 2020; 69: 2049-2051
    CrossrefPubMedSuche in Google Scholar
  • 4 Backes Y, Schwartz MP, Ter Borg F. et al. Multicentre prospective evaluation of real-time optical diagnosis of T1 colorectal cancer in large non-pedunculated colorectal polyps using narrow band imaging (the OPTICAL study). Gut 2019; 68: 271-279
    CrossrefPubMedSuche in Google Scholar
  • 5 Pimentel-Nunes P, Libanio D, Bastiaansen BAJ. et al. Endoscopic submucosal dissection for superficial gastrointestinal lesions: European Society of Gastrointestinal Endoscopy (ESGE) Guideline – Update 2022. Endoscopy 2022; 54: 591-622
    Thieme ConnectPubMedSuche in Google Scholar
  • 6 Messmann H, Bisschops R, Antonelli G. et al. Expected value of artificial intelligence in gastrointestinal endoscopy: European Society of Gastrointestinal Endoscopy (ESGE) Position Statement. Endoscopy 2022; 54: 1211-1231
    Thieme ConnectPubMedSuche in Google Scholar
  • 7 Tricco AC, Lillie E, Zarin W. et al. PRISMA Extension for Scoping Reviews (PRISMA-ScR): Checklist and Explanation. Ann Intern Med 2018; 169: 467-473
    CrossrefPubMedSuche in Google Scholar
  • 8 Bramer WM, Giustini D, de Jonge GB. et al. De-duplication of database search results for systematic reviews in EndNote. J Med Libr Assoc 2016; 104: 240-243
    CrossrefPubMedSuche in Google Scholar
  • 9 Choi SJ, Kim ES, Choi K. Prediction of the histology of colorectal neoplasm in white light colonoscopic images using deep learning algorithms. Sci Rep 2021; 11: 5311
    CrossrefPubMedSuche in Google Scholar
  • 10 Jheng YC, Wang YP, Lin HE. et al. A novel machine learning-based algorithm to identify and classify lesions and anatomical landmarks in colonoscopy images. Surg Endosc 2022; 36: 640-650
    CrossrefPubMedSuche in Google Scholar
  • 11 Mori Y, Kudo SE, Misawa M. et al. Artificial intelligence-assisted colonic endocytoscopy for cancer recognition: a multicenter study. Endosc Int Open 2021; 9: E1004-E1011
    Thieme ConnectPubMedSuche in Google Scholar
  • 12 Ozawa T, Ishihara S, Fujishiro M. et al. Automated endoscopic detection and classification of colorectal polyps using convolutional neural networks. Therap Adv Gastroenterol 2020; 13: 1756284820910659
    CrossrefPubMedSuche in Google Scholar
  • 13 Park H-C, Kim Y-J, Lee S-W. Adenocarcinoma recognition in endoscopy images using optimized convolutional neural networks. Applied Sci 2020; 10: 1650
    CrossrefPubMedSuche in Google Scholar
  • 14 Pu LZCT, Maicas G, Tian Y. et al. Computer-aided diagnosis for characterization of colorectal lesions: comprehensive software that includes differentiation of serrated lesions. Gastrointest Endosc 2020; 92: 891-899
    CrossrefPubMedSuche in Google Scholar
  • 15 Shimizu T, Sasaki Y, Ito K. et al. A trial deep learning-based model for four-class histologic classification of colonic tumor from narrow band imaging. Sci Rep 2023; 13: 7510
    CrossrefPubMedSuche in Google Scholar
  • 16 Tanwar S, Vijayalakshmi S, Sabharwal M. et al. Detection and classification of colorectal polyp using deep learning. BioMed Res Int 2022; 2022
    CrossrefPubMedSuche in Google Scholar
  • 17 Wang K, Zhuang S, Miao J. et al. Adaptive frequency learning network with anti-aliasing complex convolutions for colon diseases subtypes. IEEE J Biomed Health Informatics 2023; 4816-4827
    CrossrefPubMedSuche in Google Scholar
  • 18 Xu G, Wu Y, Li B. et al. Dynamic attention deconvolutional single shot detector for polyp detection and classification in narrow-band imaging. In: Medical Imaging 2023: Image-Guided Procedures, Robotic Interventions, and Modeling. SPIE; 2023: 613-618
    Suche in Google Scholar
  • 19 Zhou D, Tian F, Tian X. et al. Diagnostic evaluation of a deep learning model for optical diagnosis of colorectal cancer. Nature Commun 2020; 11: 2961
    CrossrefPubMedSuche in Google Scholar
  • 20 Ito N, Kawahira H, Nakashima H. et al. Endoscopic diagnostic support system for cT1b colorectal cancer using deep learning. Oncology 2018; 96: 44-50
    CrossrefPubMedSuche in Google Scholar
  • 21 Minami S, Saso K, Miyoshi N. et al. Diagnosis of depth of submucosal invasion in colorectal cancer with AI using deep learning. Cancers 2022; 14: 5361
    CrossrefPubMedSuche in Google Scholar
  • 22 Nakajima Y, Zhu X, Nemoto D. et al. Diagnostic performance of artificial intelligence to identify deeply invasive colorectal cancer on non-magnified plain endoscopic images. Endosc Int Open 2020; 8: E1341-E1348
    Thieme ConnectPubMedSuche in Google Scholar
  • 23 Nemoto D, Guo Z, Katsuki S. et al. Computer-aided diagnosis of early-stage colorectal cancer using nonmagnified endoscopic white-light images (with videos). Gastrointest Endosc 2023; 98: 90-99 e94
    CrossrefPubMedSuche in Google Scholar
  • 24 Katayama D, Wu Y, Koide T. et al. Development of Computer-Aided Diagnosis System Using Single FCN Capable for Indicating Detailed Inference Results in Colon NBI Endoscopy. In: 2023 International Technical Conference on Circuits/Systems, Computers, and Communications (ITC-CSCC). IEEE; 2023: 1-6
    Suche in Google Scholar
  • 25 Lu Y, Wu J, Zhuo X. et al. Real-time artificial intelligence-based histologic classifications of colorectal polyps using narrow-band imaging. Frontiers Oncology 2022; 12: 879239
    CrossrefPubMedSuche in Google Scholar
  • 26 Lu Z, Xu Y, Yao L. et al. Real-time automated diagnosis of colorectal cancer invasion depth using a deep learning model with multimodal data (with video). Gastrointest Endosc 2022; 95: 1186-1194.e1183
    CrossrefPubMedSuche in Google Scholar
  • 27 Lui TK, Wong KK, Mak LL. et al. Endoscopic prediction of deeply submucosal invasive carcinoma with use of artificial intelligence. Endosc Int Open 2019; 7: E514-E520
    Thieme ConnectPubMedSuche in Google Scholar
  • 28 Luo X, Wang J, Han Z. et al. Artificial intelligence − enhanced white-light colonoscopy with attention guidance predicts colorectal cancer invasion depth. Gastrointest Endosc 2021; 94: 627-638.e621
    CrossrefPubMedSuche in Google Scholar
  • 29 Okamoto Y, Yoshida S, Izakura S. et al. Development of multi-class computer-aided diagnostic systems using the NICE/JNET classifications for colorectal lesions. J Gastroenterol Hepat 2022; 37: 104-110
    CrossrefPubMedSuche in Google Scholar
  • 30 Song EM, Park B, Ha C-A. et al. Endoscopic diagnosis and treatment planning for colorectal polyps using a deep-learning model. Sci Rep 2020; 10: 30
    CrossrefPubMedSuche in Google Scholar
  • 31 Takemura Y, Yoshida S, Tanaka S. et al. Computer-aided system for predicting the histology of colorectal tumors by using narrow-band imaging magnifying colonoscopy (with video). Gastrointest Endosc 2012; 75: 179-185
    CrossrefPubMedSuche in Google Scholar
  • 32 Tamai N, Saito Y, Sakamoto T. et al. Effectiveness of computer-aided diagnosis of colorectal lesions using novel software for magnifying narrow-band imaging: a pilot study. Endosc Int Open 2017; 5: E690-E694
    Thieme ConnectPubMedSuche in Google Scholar
  • 33 Tokunaga M, Matsumura T, Nankinzan R. et al. Computer-aided diagnosis system using only white-light endoscopy for the prediction of invasion depth in colorectal cancer. Gastrointest Endosc 2021; 93: 647-653
    CrossrefPubMedSuche in Google Scholar
  • 34 Yang YJ, Cho B-J, Lee M-J. et al. Automated classification of colorectal neoplasms in white-light colonoscopy images via deep learning. J Clin Med 2020; 9: 1593
    CrossrefPubMedSuche in Google Scholar
  • 35 Houwen B, Hassan C, Coupe VMH. et al. Definition of competence standards for optical diagnosis of diminutive colorectal polyps: European Society of Gastrointestinal Endoscopy (ESGE) Position Statement. Endoscopy 2022; 54: 88-99
    Thieme ConnectPubMedSuche in Google Scholar
  • 36 Backes Y, Moss A, Reitsma JB. et al. Narrow band imaging, magnifying chromoendoscopy, and gross morphological features for the optical diagnosis of t1 colorectal cancer and deep submucosal invasion: a systematic review and meta-analysis. Am J Gastroenterol 2017; 112: 54-64
    CrossrefPubMedSuche in Google Scholar
  • 37 Dekker E, Houwen B, Puig I. et al. Curriculum for optical diagnosis training in Europe: European Society of Gastrointestinal Endoscopy (ESGE) Position Statement. Endoscopy 2020; 52: C10
    Thieme ConnectPubMedSuche in Google Scholar
  • 38 Park SH, Han K. Methodologic guide for evaluating clinical performance and effect of artificial intelligence technology for medical diagnosis and prediction. Radiology 2018; 286: 800-809
    CrossrefPubMedSuche in Google Scholar
  • 39 Kelly CJ, Karthikesalingam A, Suleyman M. et al. Key challenges for delivering clinical impact with artificial intelligence. BMC Med 2019; 17: 195
    CrossrefPubMedSuche in Google Scholar
  • 40 Harrell FE. Regression modeling strategies. Switzerland: Springer Cham; 2015;
    CrossrefPubMedSuche in Google Scholar
  • 41 Rondonotti E, Hassan C, Tamanini G. et al. Artificial intelligence-assisted optical diagnosis for the resect-and-discard strategy in clinical practice: the Artificial intelligence BLI Characterization (ABC) study. Endoscopy 2023; 55: 14-22
    Thieme ConnectPubMedSuche in Google Scholar
  • 42 Jaspers TJ, Boers TG, Kusters CH. et al. Robustness evaluation of deep neural networks for endoscopic image analysis: Insights and strategies. Med Image Analysis 2024; 94: 103157
    CrossrefPubMedSuche in Google Scholar
  • 43 Takemura Y, Yoshida S, Tanaka S. et al. Computer-aided system for predicting the histology of colorectal tumors by using narrow-band imaging magnifying colonoscopy (with video). Gastrointest Endosc 2012; 75: 179-185
    CrossrefPubMedSuche in Google Scholar