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Endoscopy 2022; 54(12): 1171-1179
DOI: 10.1055/a-1849-6878
Original article

Efficacy of a computer-aided detection system in a fecal immunochemical test-based organized colorectal cancer screening program: a randomized controlled trial (AIFIT study)

Emanuele Rondonotti 
1   Gastroenterology Unit, Valduce Hospital, Como, Italy
,
Dhanai Di Paolo
1   Gastroenterology Unit, Valduce Hospital, Como, Italy
2   Foundation IRCCS Ca’ Granda Ospedale Maggiore Policlinico, Department of Gastroenterology and Hepatology, Milan, Italy
,
Erik Rosa Rizzotto
3   Gastroenterology Unit, St. Antonio Hospital, Azienda Ospedaliera Universitaria, Padova, Italy
,
Costanza Alvisi
4   USD Endoscopia Digestiva, ASST Pavia, Pavia, Italy
,
Elisabetta Buscarini
5   Gastroenterology Unit, Azienda Ospedaliera “Ospedale Maggiore”, Crema, Italy
,
Marco Spadaccini
6   Department of Biomedical Sciences, Humanitas University, Rozzano, Milan, Italy
7   Endoscopy Unit, Humanitas Clinical and Research Center IRCCS, Rozzano, Milan, Italy
,
Giacomo Tamanini
1   Gastroenterology Unit, Valduce Hospital, Como, Italy
,
Silvia Paggi
1   Gastroenterology Unit, Valduce Hospital, Como, Italy
,
Arnaldo Amato 
1   Gastroenterology Unit, Valduce Hospital, Como, Italy
,
Giulia Scardino
1   Gastroenterology Unit, Valduce Hospital, Como, Italy
,
Samanta Romeo
5   Gastroenterology Unit, Azienda Ospedaliera “Ospedale Maggiore”, Crema, Italy
,
Saverio Alicante
5   Gastroenterology Unit, Azienda Ospedaliera “Ospedale Maggiore”, Crema, Italy
,
Fabio Ancona
3   Gastroenterology Unit, St. Antonio Hospital, Azienda Ospedaliera Universitaria, Padova, Italy
,
Ennio Guido
3   Gastroenterology Unit, St. Antonio Hospital, Azienda Ospedaliera Universitaria, Padova, Italy
,
Vincenza Marzo
4   USD Endoscopia Digestiva, ASST Pavia, Pavia, Italy
,
Fabio Chicco
4   USD Endoscopia Digestiva, ASST Pavia, Pavia, Italy
,
Simona Agazzi
4   USD Endoscopia Digestiva, ASST Pavia, Pavia, Italy
,
Cesare Rosa
4   USD Endoscopia Digestiva, ASST Pavia, Pavia, Italy
,
Loredana Correale
6   Department of Biomedical Sciences, Humanitas University, Rozzano, Milan, Italy
,
Alessandro Repici
6   Department of Biomedical Sciences, Humanitas University, Rozzano, Milan, Italy
7   Endoscopy Unit, Humanitas Clinical and Research Center IRCCS, Rozzano, Milan, Italy
,
Cesare Hassan
6   Department of Biomedical Sciences, Humanitas University, Rozzano, Milan, Italy
7   Endoscopy Unit, Humanitas Clinical and Research Center IRCCS, Rozzano, Milan, Italy
,
Franco Radaelli
1   Gastroenterology Unit, Valduce Hospital, Como, Italy
,
on behalf of the AIFIT Study Group › InstitutsangabenTrial Registration: ClinicalTrials.gov Registration number (trial ID): NCT04691401 Type of study: Prospective, Multicentre, Randomized, Controlled Trial
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Einführung zu diesem Artikel:

Author commentary on Emanuele Rondonotti et al.Endoscopy 2022; 54(12): v44-v44
DOI: 10.1055/a-1865-8110

Einführung zu diesem Artikel:

Will artificial intelligence-assisted colonoscopy work in cancer screening programs?Endoscopy 2022; 54(12): 1180-1181
DOI: 10.1055/a-1890-5043

Abstract

Background Computer-aided detection (CADe) increases adenoma detection in primary screening colonoscopy. The potential benefit of CADe in a fecal immunochemical test (FIT)-based colorectal cancer (CRC) screening program is unknown. This study assessed whether use of CADe increases the adenoma detection rate (ADR) in a FIT-based CRC screening program.

Methods In a multicenter, randomized trial, FIT-positive individuals aged 50–74 years undergoing colonoscopy, were randomized (1:1) to receive high definition white-light (HDWL) colonoscopy, with or without a real-time deep-learning CADe by endoscopists with baseline ADR > 25 %. The primary outcome was ADR. Secondary outcomes were mean number of adenomas per colonoscopy (APC) and advanced adenoma detection rate (advanced-ADR). Subgroup analysis according to baseline endoscopists’ ADR (≤ 40 %, 41 %–45 %, ≥ 46 %) was also performed.

Results 800 individuals (median age 61.0 years [interquartile range 55–67]; 409 men) were included: 405 underwent CADe-assisted colonoscopy and 395 underwent HDWL colonoscopy alone. ADR and APC were significantly higher in the CADe group than in the HDWL arm: ADR 53.6 % (95 %CI 48.6 %–58.5 %) vs. 45.3 % (95 %CI 40.3 %–50.45 %; RR 1.18; 95 %CI 1.03–1.36); APC 1.13 (SD 1.54) vs. 0.90 (SD 1.32; P  = 0.03). No significant difference in advanced-ADR was found (18.5 % [95 %CI 14.8 %–22.6 %] vs. 15.9 % [95 %CI 12.5 %–19.9 %], respectively). An increase in ADR was observed in all endoscopist groups regardless of baseline ADR.

Conclusions Incorporating CADe significantly increased ADR and APC in the framework of a FIT-based CRC screening program. The impact of CADe appeared to be consistent regardless of endoscopist baseline ADR.

Supplementary material



Publikationsverlauf

Eingereicht: 01. Februar 2022

Angenommen nach Revision: 11. Mai 2022

Accepted Manuscript online:
11. Mai 2022

Artikel online veröffentlicht:
12. Juli 2022

© 2022. Thieme. All rights reserved.

Georg Thieme Verlag KG
Rüdigerstraße 14, 70469 Stuttgart, Germany

 

  • References

  • 1 Valori R, Rey JF, Atkin WS. et al. European guidelines for quality assurance in colorectal cancer screening and diagnosis. Endoscopy 2012; 44: SE88-105
    PubMedSuche in Google Scholar
  • 2 Castaneda D, Popov VB, Verheyen E. et al. New technologies improve adenoma detection rate, adenoma miss rate, and polyp detection rate: a systematic review and meta-analysis. Gastrointest Endosc 2018; 88: 209-222
    CrossrefPubMedSuche in Google Scholar
  • 3 Paggi S, Mogavero G, Amato A. et al. Linked color imaging reduces the miss rate of neoplastic lesions in the right colon: a randomized tandem colonoscopy study. Endoscopy 2018; 50: 396-402
    Suche in Google Scholar
  • 4 Kudo SE, Mori Y, Misawa M. et al. Artificial intelligence and colonoscopy: current status and future perspectives. Dig Endosc 2019; 31: 363-371
    CrossrefPubMedSuche in Google Scholar
  • 5 Ahmad OF, Soares AS, Mazomenos E. et al. Artificial intelligence and computer-aided diagnosis in colonoscopy: current evidence and future directions. Lancet Gastroenterol Hepatol 2019; 4: 71-80
    CrossrefPubMedSuche in Google Scholar
  • 6 Alagappan M, Brown JRG, Mori Y. et al. Artificial intelligence in gastrointestinal endoscopy: the future is almost here. World J Gastrointest Endosc 2018; 10: 239-249
    CrossrefPubMedSuche in Google Scholar
  • 7 Repici A, Badalamenti M, Maselli R. et al. Efficacy of real-time computer-aided detection of colorectal neoplasia in a randomized trial. Gastroenterology 2020; 159: 512-520
    Suche in Google Scholar
  • 8 Hassan C, Spadaccini M, Iannone A. et al. Performance of artificial intelligence for colonoscopy regarding adenoma and polyp detection: a meta-analysis. Gastrointest Endosc 2020; 93: 77-85
    CrossrefPubMedSuche in Google Scholar
  • 9 Penz D, Ferlitsch A, Waldmann E. et al. Impact of adenoma detection rate on detection of advanced adenomas and endoscopic adverse events in a study of over 200,000 screening colonoscopies. Gastrointest Endosc 2020; 91: 135-141
    CrossrefPubMedSuche in Google Scholar
  • 10 Cubiella J, González A, Almazán R. et al. Overtreatment in nonmalignant lesions detected in a colorectal cancer screening program: a retrospective cohort study. BMC Cancer 2021; 21: 869
    CrossrefPubMedSuche in Google Scholar
  • 11 Repici A, Spadaccini M, Antonelli G. et al. Artificial intelligence and colonoscopy experience: lessons from two randomised trials. Gut 2022; 71: 757-765
    Suche in Google Scholar
  • 12 Mangas-Sanjuan C, Santana E, Cubiella J. et al. Variation in colonoscopy performance measures according to procedure indication. Clin Gastroenterol Hepatol 2020; 18: 1216-1223
    CrossrefPubMedSuche in Google Scholar
  • 13 Weight J, Repici A, Antonelli G. et al. Performance of a new integrated computer-assisted system (CADe/CADx) for detection and characterization of colorectal neoplasia. Endoscopy 2022; 54: 180-184
    Thieme ConnectPubMedSuche in Google Scholar
  • 14 Cross AJ, Robbins EC, Saunders BP. et al. Higher adenoma detection rates at screening associated with lower long-term colorectal cancer incidence and mortality. Clin Gastroenterol Hepatol 2022; 20: e148-e167
    CrossrefPubMedSuche in Google Scholar
  • 15 Liu X, Cruz Rivera S, Moher D. et al. Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension. Nat Med 2020; 26: 1634-1374
    PubMedSuche in Google Scholar
  • 16 Calderwood AH, Jacobson BC. Comprehensive validation of the Boston Bowel Preparation Scale. Gastrointest Endosc 2010; 72: 686-692
    CrossrefPubMedSuche in Google Scholar
  • 17 Endoscopic Classification Review Group. Update on the Paris classification of superficial neoplastic lesions in the digestive tract. Endoscopy 2005; 37: 570-578
    Suche in Google Scholar
  • 18 Dixon MF. Gastrointestinal epithelial neoplasia:Vienna revisited. Gut 2002; 51: 130-131
    Suche in Google Scholar
  • 19 Pai RK, Makinen MJ, Rosty C. WHO classification of tumours. Digestive system tumours. 5. Lyon: IARC; 2019
    Suche in Google Scholar
  • 20 Kligman E, Li W, Eckert GJ. et al. Adenoma detection rate in asymptomatic patients with positive fecal immunochemical tests. Dig Dis Sci 2018; 63: 1167-1172
    CrossrefPubMedSuche in Google Scholar
  • 21 Cavicchi M, Tharsis G, Burtin P. et al. Difference in physician- and patient-dependent factors contributing to adenoma detection rate and serrated polyp detection rate. Dig Dis Sci 2019; 64: 3579-3588
    CrossrefPubMedSuche in Google Scholar
  • 22 Hilsden RJ, Bridges R, Dube C. et al. Defining benchmarks for adenoma detection rate and adenomas per colonoscopy in patients undergoing colonoscopy due to a positive fecal immunochemical test. Am J Gastroenterol 2016; 111: 1743-1749
    CrossrefPubMedSuche in Google Scholar
  • 23 Wong JCT, Chiu H, Kim H. et al. Adenoma detection rates in colonoscopies for positive fecal immunochemical tests versus direct screening colonoscopies. Gastrointest Endosc 2019; 89: 607-613
    CrossrefPubMedSuche in Google Scholar
  • 24 Glissen Brown JR, Mansour NM, Wang P. et al. Deep learning computer-aided polyp detection reduces adenoma miss rate: a United States multi-center randomized tandem colonoscopy study (CADeT-CS Trial). Clin Gastroenterol Hepatol 2022; 20: 1499-1507
    Suche in Google Scholar
  • 25 Hassan C, Senore C, Manes G. et al. Diagnostic yield and miss rate of EndoRings in an organized colorectal cancer screening program: the SMART (Study Methodology for ADR-Related Technology) trial. Gastrointest Endosc 2019; 89: 583-590
    CrossrefPubMedSuche in Google Scholar
  • 26 Thayalasekaran S, Frazzoni L, Antonelli G. et al. Endoscopic technological innovations for neoplasia detection in organized colorectal cancer screening programs: a systematic review and meta-analysis. Gastrointest Endosc 2020; 92: 840-847
    CrossrefPubMedSuche in Google Scholar
  • 27 Hassan C, Senore C, Radaelli F. et al. Full-spectrum (FUSE) versus standard forward-viewing colonoscopy in an organised colorectal cancer screening programme. Gut 2017; 66: 1949-1955
    PubMedSuche in Google Scholar
  • 28 Corley DA, Levin TR, Doubeni CA. Adenoma detection rate and risk of colorectal cancer and death. N Eng J Med 2014; 26: 2541
    PubMedSuche in Google Scholar
  • 29 Liem B, Gupta N. Adenoma detection rate: the perfect colonoscopy quality measure or is there more?. Transl Gastroenterol Hepatol 2018; 3: 19
    CrossrefPubMedSuche in Google Scholar
  • 30 Rex DK. Detection measures for colonoscopy: considerations on the adenoma detection rate, recommended detection thresholds, withdrawal times, and potential updates to measures. J Clin Gastroenterol 2020; 54: 130-135
    CrossrefPubMedSuche in Google Scholar
  • 31 Hassan C, Antonelli G, Dumonceau JM. et al. Post-polypectomy colonoscopy surveillance: European Society of Gastrointestinal Endoscopy (ESGE) Guideline – Update 2020. Endoscopy 2020; 52: 687-700
    Suche in Google Scholar
  • 32 Anderson J, Hisey W, Robinson C. et al. Mean adenomas per colonoscopy is inversely associated with post colonoscopy colorectal cancers: data from the new Hampshire colonoscopy registry validating this quality measure. Am J Gastroenterol 2021; 116: S249
    CrossrefPubMedSuche in Google Scholar
  • 33 Facciorusso A, Triantafyllou K, Murad MH. et al. Compared abilities of endoscopic techniques to increase colon adenoma detection rates: a network meta-analysis. Clin Gastroenterol Hepatol 2019; 17: 2439-2454
    CrossrefPubMedSuche in Google Scholar
  • 34 Lui TKL, Hui CKY, Tsui VWM. et al. New insights on missed colonic lesions during colonoscopy through artificial intelligence–assisted real-time detection. Gastrointest Endosc 2021; 93: 193-200
    CrossrefPubMedSuche in Google Scholar
  • 35 Wang P, Liu X, Berzin TM. et al. Effect of a deep-learning computer-aided detection system on adenoma detection during colonoscopy (CADe-DB trial): a double-blind randomised study. Lancet Gastroenterol Hepatol 2020; 4: 345-351
    PubMedSuche in Google Scholar
  • 36 Hassan C, Badalamenti M, Maselli R. et al. Computer-aided detection-assisted colonoscopy: classification and relevance of false positives. Gastrointest Endosc 2020; 92: 900-904
    Suche in Google Scholar
  • 37 Spadaccini M, Hassan C, Alfarone L. et al. Comparing number and relevance of false activations between two artificial intelligence CADe SystEms: the NOISE study. Gastrointest Endosc 2022; 95: 975-981
    CrossrefPubMedSuche in Google Scholar