Computer-aided detection versus advanced imaging for detection of colorectal neoplasia: a systematic review and network meta-analysis

Computer-aided detection (CADe) techniques based on artificial intelligence algorithms can assist endoscopists in detecting colorectal neoplasia. CADe has been associated with an increased adenoma detection rate, a key quality indicator, but the utility of CADe compared with existing advanced imagin...

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Published inThe lancet. Gastroenterology & hepatology Vol. 6; no. 10; p. 793
Main Authors Spadaccini, Marco, Iannone, Andrea, Maselli, Roberta, Badalamenti, Matteo, Desai, Madhav, Chandrasekar, Viveksandeep Thoguluva, Patel, Harsh K, Fugazza, Alessandro, Pellegatta, Gaia, Galtieri, Piera Alessia, Lollo, Gianluca, Carrara, Silvia, Anderloni, Andrea, Rex, Douglas K, Savevski, Victor, Wallace, Michael B, Bhandari, Pradeep, Roesch, Thomas, Gralnek, Ian M, Sharma, Prateek, Hassan, Cesare, Repici, Alessandro
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Published Netherlands 01.10.2021
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Abstract Computer-aided detection (CADe) techniques based on artificial intelligence algorithms can assist endoscopists in detecting colorectal neoplasia. CADe has been associated with an increased adenoma detection rate, a key quality indicator, but the utility of CADe compared with existing advanced imaging techniques and distal attachment devices is unclear. For this systematic review and network meta-analysis, we did a comprehensive search of PubMed/Medline, Embase, and Scopus databases from inception to Nov 30, 2020, for randomised controlled trials investigating the effectiveness of the following endoscopic techniques in detecting colorectal neoplasia: CADe, high definition (HD) white-light endoscopy, chromoendoscopy, or add-on devices (ie, systems that increase mucosal visualisation, such as full spectrum endoscopy [FUSE] or G-EYE balloon endoscopy). We collected data on adenoma detection rates, sessile serrated lesion detection rates, the proportion of large adenomas detected per colonoscopy, and withdrawal times. A frequentist framework, random-effects network meta-analysis was done to compare artificial intelligence with chromoendoscopy, increased mucosal visualisation systems, and HD white-light endoscopy (the control group). We estimated odds ratios (ORs) for the adenoma detection rate, sessile serrated lesion detection rate, and proportion of large adenomas detected per colonoscopy, and calculated mean differences for withdrawal time, with 95% CIs. Risk of bias and certainty of evidence were assessed with the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach. 50 randomised controlled trials, comprising 34 445 participants, were included in our main analysis (six trials of CADe, 18 of chromoendoscopy, and 26 of increased mucosal visualisation systems). HD white-light endoscopy was the control technique in all 50 studies. Compared with the control technique, the adenoma detection rate was 7·4% higher with CADe (OR 1·78 [95% CI 1·44-2·18]), 4·4% higher with chromoendoscopy (1·22 [1·08-1·39]), and 4·1% higher with increased mucosal visualisation systems (1·16 [1·04-1·28]). CADe ranked as the superior technique for adenoma detection (with moderate confidence in hierarchical ranking); cross-comparisons of CADe with other imaging techniques showed a significant increase in the adenoma detection rate with CADe versus increased mucosal visualisation systems (OR 1·54 [95% CI 1·22-1·94]; low certainty of evidence) and with CADe versus chromoendoscopy (1·45 [1·14-1·85]; moderate certainty of evidence). When focusing on large adenomas (≥10 mm) there was a significant increase in the detection of large adenomas only with CADe (OR 1·69 [95% CI 1·10-2·60], moderate certainty of evidence) when compared to HD white-light endoscopy; CADe ranked as the superior strategy for detection of large adenomas. CADe also seemed to be the superior strategy for detection of sessile serrated lesions (with moderate confidence in hierarchical ranking), although no significant increase in the sessile serrated lesion detection rate was shown (OR 1·37 [95% CI 0·65-2·88]). No significant difference in withdrawal time was reported for CADe compared with the other techniques. Based on the published literature, detection rates of colorectal neoplasia are higher with CADe than with other techniques such as chromoendoscopy or tools that increase mucosal visualisation, supporting wider incorporation of CADe strategies into community endoscopy services. None.
AbstractList Computer-aided detection (CADe) techniques based on artificial intelligence algorithms can assist endoscopists in detecting colorectal neoplasia. CADe has been associated with an increased adenoma detection rate, a key quality indicator, but the utility of CADe compared with existing advanced imaging techniques and distal attachment devices is unclear. For this systematic review and network meta-analysis, we did a comprehensive search of PubMed/Medline, Embase, and Scopus databases from inception to Nov 30, 2020, for randomised controlled trials investigating the effectiveness of the following endoscopic techniques in detecting colorectal neoplasia: CADe, high definition (HD) white-light endoscopy, chromoendoscopy, or add-on devices (ie, systems that increase mucosal visualisation, such as full spectrum endoscopy [FUSE] or G-EYE balloon endoscopy). We collected data on adenoma detection rates, sessile serrated lesion detection rates, the proportion of large adenomas detected per colonoscopy, and withdrawal times. A frequentist framework, random-effects network meta-analysis was done to compare artificial intelligence with chromoendoscopy, increased mucosal visualisation systems, and HD white-light endoscopy (the control group). We estimated odds ratios (ORs) for the adenoma detection rate, sessile serrated lesion detection rate, and proportion of large adenomas detected per colonoscopy, and calculated mean differences for withdrawal time, with 95% CIs. Risk of bias and certainty of evidence were assessed with the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach. 50 randomised controlled trials, comprising 34 445 participants, were included in our main analysis (six trials of CADe, 18 of chromoendoscopy, and 26 of increased mucosal visualisation systems). HD white-light endoscopy was the control technique in all 50 studies. Compared with the control technique, the adenoma detection rate was 7·4% higher with CADe (OR 1·78 [95% CI 1·44-2·18]), 4·4% higher with chromoendoscopy (1·22 [1·08-1·39]), and 4·1% higher with increased mucosal visualisation systems (1·16 [1·04-1·28]). CADe ranked as the superior technique for adenoma detection (with moderate confidence in hierarchical ranking); cross-comparisons of CADe with other imaging techniques showed a significant increase in the adenoma detection rate with CADe versus increased mucosal visualisation systems (OR 1·54 [95% CI 1·22-1·94]; low certainty of evidence) and with CADe versus chromoendoscopy (1·45 [1·14-1·85]; moderate certainty of evidence). When focusing on large adenomas (≥10 mm) there was a significant increase in the detection of large adenomas only with CADe (OR 1·69 [95% CI 1·10-2·60], moderate certainty of evidence) when compared to HD white-light endoscopy; CADe ranked as the superior strategy for detection of large adenomas. CADe also seemed to be the superior strategy for detection of sessile serrated lesions (with moderate confidence in hierarchical ranking), although no significant increase in the sessile serrated lesion detection rate was shown (OR 1·37 [95% CI 0·65-2·88]). No significant difference in withdrawal time was reported for CADe compared with the other techniques. Based on the published literature, detection rates of colorectal neoplasia are higher with CADe than with other techniques such as chromoendoscopy or tools that increase mucosal visualisation, supporting wider incorporation of CADe strategies into community endoscopy services. None.
Author Maselli, Roberta
Gralnek, Ian M
Roesch, Thomas
Chandrasekar, Viveksandeep Thoguluva
Sharma, Prateek
Bhandari, Pradeep
Iannone, Andrea
Patel, Harsh K
Desai, Madhav
Pellegatta, Gaia
Lollo, Gianluca
Fugazza, Alessandro
Anderloni, Andrea
Carrara, Silvia
Badalamenti, Matteo
Wallace, Michael B
Rex, Douglas K
Savevski, Victor
Spadaccini, Marco
Galtieri, Piera Alessia
Hassan, Cesare
Repici, Alessandro
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  organization: Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy; Endoscopy Unit, IRCCS Humanitas Clinical and Research Center, Rozzano, Milan, Italy. Electronic address: marcospadaccini9@gmail.com
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  organization: Endoscopy Unit, Ochsner Clinic Foundation, New Orleans, LA, USA
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  organization: Endoscopy Unit, IRCCS Humanitas Clinical and Research Center, Rozzano, Milan, Italy
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  givenname: Piera Alessia
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  organization: Endoscopy Unit, IRCCS Humanitas Clinical and Research Center, Rozzano, Milan, Italy
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  givenname: Gianluca
  surname: Lollo
  fullname: Lollo, Gianluca
  organization: Department of Gastroenterology and Hepatology, Università della Svizzera Italiana, Lugano, Switzerland
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  surname: Carrara
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  organization: Endoscopy Unit, IRCCS Humanitas Clinical and Research Center, Rozzano, Milan, Italy
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  organization: Endoscopy Unit, IRCCS Humanitas Clinical and Research Center, Rozzano, Milan, Italy
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  organization: Division of Gastroenterology/Hepatology, Indiana University School of Medicine, Indianapolis, IN, USA
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  organization: Artificial Intelligence Research, IRCCS Humanitas Clinical and Research Center, Rozzano, Milan, Italy
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  surname: Wallace
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  organization: Endoscopy Unit, Sheikh Shakhbout Medical City, Abu Dhabi, United Arab Emirates
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  givenname: Pradeep
  surname: Bhandari
  fullname: Bhandari, Pradeep
  organization: Department of Gastroenterology, Queen Alexandra Hospital, Portsmouth, UK
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  surname: Roesch
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  organization: Department of Interdisciplinary Endoscopy, University Hospital Hamburg-Eppendorf, Hamburg, Germany
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  surname: Gralnek
  fullname: Gralnek, Ian M
  organization: Institute of Gastroenterology and Liver Diseases, Ha'Emek Medical Center, Afula, Israel
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  surname: Hassan
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  organization: Digestive Endoscopy Unit, Nuovo Regina Margherita Hospital, Rome, Italy
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  surname: Repici
  fullname: Repici, Alessandro
  organization: Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy; Endoscopy Unit, IRCCS Humanitas Clinical and Research Center, Rozzano, Milan, Italy
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References 34774156 - Lancet Gastroenterol Hepatol. 2021 Dec;6(12):984-985. doi: 10.1016/S2468-1253(21)00379-4.
34774155 - Lancet Gastroenterol Hepatol. 2021 Dec;6(12):984. doi: 10.1016/S2468-1253(21)00349-6.
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Snippet Computer-aided detection (CADe) techniques based on artificial intelligence algorithms can assist endoscopists in detecting colorectal neoplasia. CADe has been...
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StartPage 793
SubjectTerms Adenoma - diagnosis
Adenoma - pathology
Artificial Intelligence
Colonoscopy - methods
Colorectal Neoplasms - diagnostic imaging
Colorectal Neoplasms - pathology
Diagnostic Imaging - statistics & numerical data
Diagnostic Imaging - trends
Endoscopy, Digestive System - methods
Female
Humans
Image Processing, Computer-Assisted - instrumentation
Image Processing, Computer-Assisted - statistics & numerical data
Male
Randomized Controlled Trials as Topic
Title Computer-aided detection versus advanced imaging for detection of colorectal neoplasia: a systematic review and network meta-analysis
URI https://www.ncbi.nlm.nih.gov/pubmed/34363763
Volume 6
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