Cesare Hassan1, Marco Spadaccini2, Andrea Iannone3, Roberta Maselli4, Manol Jovani5, Viveksandeep Thoguluva Chandrasekar6, Giulio Antonelli1, Honggang Yu7, Miguel Areia8, Mario Dinis-Ribeiro9, Pradeep Bhandari10, Prateek Sharma6, Douglas K Rex11, Thomas Rösch12, Michael Wallace13, Alessandro Repici2. 1. Digestive Endoscopy Unit, Nuovo Regina Margherita Hospital, Rome, Italy. 2. Endoscopy Unit, Humanitas Clinical and Research Center-IRCCS, Rozzano, Italy; Department of Biomedical Sciences, Humanitas University, Rozzano, Italy. 3. Section of Gastroenterology, Department of Emergency and Organ Transplantation, University of Bari, Bari, Italy. 4. Endoscopy Unit, Humanitas Clinical and Research Center-IRCCS, Rozzano, Italy. 5. Division of Gastroenterology and Hepatology, Johns Hopkins Hospital, Baltimore, Maryland, USA; Division of Gastroenterology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA. 6. Gastroenterology and Hepatology, Kansas City VA Medical Center, Kansas City, Missouri, USA. 7. Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China. 8. Department of Gastroenterology, Portuguese Oncology Institute of Coimbra, Coimbra, Portugal. 9. MEDCIDS-Department of Community Medicine, Information and Decision in Health, Faculty of Porto, University of Medicine, Porto, Portugal. 10. Department of Gastroenterology, Queen Alexandra Hospital, Portsmouth, UK. 11. Division of Gastroenterology/Hepatology, Indiana University School of Medicine, Indianapolis, Indiana, USA. 12. Department of Interdisciplinary Endoscopy, University Hospital Hamburg-Eppendorf, Hamburg, Germany. 13. Department of Gastroenterology, Mayo Clinic, Jacksonville, Florida, USA.
Abstract
BACKGROUND AND AIMS: One-fourth of colorectal neoplasia are missed at screening colonoscopy, representing the main cause of interval colorectal cancer. Deep learning systems with real-time computer-aided polyp detection (CADe) showed high accuracy in artificial settings, and preliminary randomized controlled trials (RCTs) reported favorable outcomes in the clinical setting. The aim of this meta-analysis was to summarize available RCTs on the performance of CADe systems in colorectal neoplasia detection. METHODS: We searched MEDLINE, EMBASE, and Cochrane Central databases until March 2020 for RCTs reporting diagnostic accuracy of CADe systems in the detection of colorectal neoplasia. The primary outcome was pooled adenoma detection rate (ADR), and secondary outcomes were adenoma per colonoscopy (APC) according to size, morphology, and location; advanced APC; polyp detection rate; polyps per colonoscopy; and sessile serrated lesions per colonoscopy. We calculated risk ratios (RRs), performed subgroup and sensitivity analyses, and assessed heterogeneity and publication bias. RESULTS: Overall, 5 randomized controlled trials (4354 patients) were included in the final analysis. Pooled ADR was significantly higher in the CADe group than in the control group (791/2163 [36.6%] vs 558/2191 [25.2%]; RR, 1.44; 95% confidence interval [CI], 1.27-1.62; P < .01; I2 = 42%). APC was also higher in the CADe group compared with control (1249/2163 [.58] vs 779/2191 [.36]; RR, 1.70; 95% CI, 1.53-1.89; P < .01; I2 = 33%). APC was higher for ≤5-mm (RR, 1.69; 95% CI, 1.48-1.84), 6- to 9-mm (RR, 1.44; 95% CI, 1.19-1.75), and ≥10-mm adenomas (RR, 1.46; 95% CI, 1.04-2.06) and for proximal (RR, 1.59; 95% CI, 1.34-1.88), distal (RR, 1.68; 95% CI, 1.50-1.88), flat (RR, 1.78; 95% CI, 1.47-2.15), and polypoid morphology (RR, 1.54; 95% CI, 1.40-1.68). Regarding histology, CADe resulted in a higher sessile serrated lesion per colonoscopy (RR, 1.52; 95% CI, 1.14-2.02), whereas a nonsignificant trend for advanced ADR was found (RR, 1.35; 95% CI, .74-2.47; P = .33; I2 = 69%). Level of evidence for RCTs was graded as moderate. CONCLUSIONS: According to available evidence, the incorporation of artificial intelligence as aid for detection of colorectal neoplasia results in a significant increase in the detection of colorectal neoplasia, and such effect is independent from main adenoma characteristics.
BACKGROUND AND AIMS: One-fourth of colorectal neoplasia are missed at screening colonoscopy, representing the main cause of interval colorectal cancer. Deep learning systems with real-time computer-aided polyp detection (CADe) showed high accuracy in artificial settings, and preliminary randomized controlled trials (RCTs) reported favorable outcomes in the clinical setting. The aim of this meta-analysis was to summarize available RCTs on the performance of CADe systems in colorectal neoplasia detection. METHODS: We searched MEDLINE, EMBASE, and Cochrane Central databases until March 2020 for RCTs reporting diagnostic accuracy of CADe systems in the detection of colorectal neoplasia. The primary outcome was pooled adenoma detection rate (ADR), and secondary outcomes were adenoma per colonoscopy (APC) according to size, morphology, and location; advanced APC; polyp detection rate; polyps per colonoscopy; and sessile serrated lesions per colonoscopy. We calculated risk ratios (RRs), performed subgroup and sensitivity analyses, and assessed heterogeneity and publication bias. RESULTS: Overall, 5 randomized controlled trials (4354 patients) were included in the final analysis. Pooled ADR was significantly higher in the CADe group than in the control group (791/2163 [36.6%] vs 558/2191 [25.2%]; RR, 1.44; 95% confidence interval [CI], 1.27-1.62; P < .01; I2 = 42%). APC was also higher in the CADe group compared with control (1249/2163 [.58] vs 779/2191 [.36]; RR, 1.70; 95% CI, 1.53-1.89; P < .01; I2 = 33%). APC was higher for ≤5-mm (RR, 1.69; 95% CI, 1.48-1.84), 6- to 9-mm (RR, 1.44; 95% CI, 1.19-1.75), and ≥10-mm adenomas (RR, 1.46; 95% CI, 1.04-2.06) and for proximal (RR, 1.59; 95% CI, 1.34-1.88), distal (RR, 1.68; 95% CI, 1.50-1.88), flat (RR, 1.78; 95% CI, 1.47-2.15), and polypoid morphology (RR, 1.54; 95% CI, 1.40-1.68). Regarding histology, CADe resulted in a higher sessile serrated lesion per colonoscopy (RR, 1.52; 95% CI, 1.14-2.02), whereas a nonsignificant trend for advanced ADR was found (RR, 1.35; 95% CI, .74-2.47; P = .33; I2 = 69%). Level of evidence for RCTs was graded as moderate. CONCLUSIONS: According to available evidence, the incorporation of artificial intelligence as aid for detection of colorectal neoplasia results in a significant increase in the detection of colorectal neoplasia, and such effect is independent from main adenoma characteristics.
Authors: R B den Boer; C de Jongh; W T E Huijbers; T J M Jaspers; J P W Pluim; R van Hillegersberg; M Van Eijnatten; J P Ruurda Journal: Surg Endosc Date: 2022-08-04 Impact factor: 3.453
Authors: Fei Kuang; Juan Du; Mengjia Zhou; Xiangdong Liu; Xinchen Luo; Yong Tang; Bo Li; Song Su Journal: Front Oncol Date: 2022-06-10 Impact factor: 5.738