Efficacy of Real-Time Computer-Aided Detection of Colorectal Neoplasia in a Randomized Trial
One-fourth of colorectal neoplasias are missed during screening colonoscopies; these can develop into colorectal cancer (CRC). Deep learning systems allow for real-time computer-aided detection (CADe) of polyps with high accuracy. We performed a multicenter, randomized trial to assess the safety and...
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| Vydáno v: | Gastroenterology (New York, N.Y. 1943) Ročník 159; číslo 2; s. 512 |
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| Hlavní autoři: | , , , , , , , , , , , , , , , , , , , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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United States
01.08.2020
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| ISSN: | 1528-0012, 1528-0012 |
| On-line přístup: | Zjistit podrobnosti o přístupu |
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| Abstract | One-fourth of colorectal neoplasias are missed during screening colonoscopies; these can develop into colorectal cancer (CRC). Deep learning systems allow for real-time computer-aided detection (CADe) of polyps with high accuracy. We performed a multicenter, randomized trial to assess the safety and efficacy of a CADe system in detection of colorectal neoplasias during real-time colonoscopy.
We analyzed data from 685 subjects (61.32 ± 10.2 years old; 337 men) undergoing screening colonoscopies for CRC, post-polypectomy surveillance, or workup due to positive results from a fecal immunochemical test or signs or symptoms of CRC, at 3 centers in Italy from September through November 2019. Patients were randomly assigned (1:1) to groups who underwent high-definition colonoscopies with the CADe system or without (controls). The CADe system included an artificial intelligence-based medical device (GI-Genius, Medtronic) trained to process colonoscopy images and superimpose them, in real time, on the endoscopy display a green box over suspected lesions. A minimum withdrawal time of 6 minutes was required. Lesions were collected and histopathology findings were used as the reference standard. The primary outcome was adenoma detection rate (ADR, the percentage of patients with at least 1 histologically proven adenoma or carcinoma). Secondary outcomes were adenomas detected per colonoscopy, non-neoplastic resection rate, and withdrawal time.
The ADR was significantly higher in the CADe group (54.8%) than in the control group (40.4%) (relative risk [RR], 1.30; 95% confidence interval [CI], 1.14-1.45). Adenomas detected per colonoscopy were significantly higher in the CADe group (mean, 1.07 ±1.54) than in the control group (mean 0.71 ± 1.20) (incidence rate ratio, 1.46; 95% CI, 1.15-1.86). Adenomas 5 mm or smaller were detected in a significantly higher proportion of subjects in the CADe group (33.7%) than in the control group (26.5%; RR, 1.26; 95% CI, 1.01-1.52), as were adenomas of 6 to 9 mm (detected in 10.6% of subjects in the CADe group vs 5.8% in the control group; RR, 1.78; 95% CI, 1.09-2.86), regardless of morphology or location. There was no significant difference between groups in withdrawal time (417 ± 101 seconds for the CADe group vs 435 ± 149 for controls; P = .1) or proportion of subjects with resection of non-neoplastic lesions (26.0% in the CADe group vs 28.7% of controls; RR, 1.00; 95% CI, 0.90-1.12).
In a multicenter, randomized trial, we found that including CADe in real-time colonoscopy significantly increases ADR and adenomas detected per colonoscopy without increasing withdrawal time. ClinicalTrials.gov no: 04079478. |
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| AbstractList | One-fourth of colorectal neoplasias are missed during screening colonoscopies; these can develop into colorectal cancer (CRC). Deep learning systems allow for real-time computer-aided detection (CADe) of polyps with high accuracy. We performed a multicenter, randomized trial to assess the safety and efficacy of a CADe system in detection of colorectal neoplasias during real-time colonoscopy.
We analyzed data from 685 subjects (61.32 ± 10.2 years old; 337 men) undergoing screening colonoscopies for CRC, post-polypectomy surveillance, or workup due to positive results from a fecal immunochemical test or signs or symptoms of CRC, at 3 centers in Italy from September through November 2019. Patients were randomly assigned (1:1) to groups who underwent high-definition colonoscopies with the CADe system or without (controls). The CADe system included an artificial intelligence-based medical device (GI-Genius, Medtronic) trained to process colonoscopy images and superimpose them, in real time, on the endoscopy display a green box over suspected lesions. A minimum withdrawal time of 6 minutes was required. Lesions were collected and histopathology findings were used as the reference standard. The primary outcome was adenoma detection rate (ADR, the percentage of patients with at least 1 histologically proven adenoma or carcinoma). Secondary outcomes were adenomas detected per colonoscopy, non-neoplastic resection rate, and withdrawal time.
The ADR was significantly higher in the CADe group (54.8%) than in the control group (40.4%) (relative risk [RR], 1.30; 95% confidence interval [CI], 1.14-1.45). Adenomas detected per colonoscopy were significantly higher in the CADe group (mean, 1.07 ±1.54) than in the control group (mean 0.71 ± 1.20) (incidence rate ratio, 1.46; 95% CI, 1.15-1.86). Adenomas 5 mm or smaller were detected in a significantly higher proportion of subjects in the CADe group (33.7%) than in the control group (26.5%; RR, 1.26; 95% CI, 1.01-1.52), as were adenomas of 6 to 9 mm (detected in 10.6% of subjects in the CADe group vs 5.8% in the control group; RR, 1.78; 95% CI, 1.09-2.86), regardless of morphology or location. There was no significant difference between groups in withdrawal time (417 ± 101 seconds for the CADe group vs 435 ± 149 for controls; P = .1) or proportion of subjects with resection of non-neoplastic lesions (26.0% in the CADe group vs 28.7% of controls; RR, 1.00; 95% CI, 0.90-1.12).
In a multicenter, randomized trial, we found that including CADe in real-time colonoscopy significantly increases ADR and adenomas detected per colonoscopy without increasing withdrawal time. ClinicalTrials.gov no: 04079478. One-fourth of colorectal neoplasias are missed during screening colonoscopies; these can develop into colorectal cancer (CRC). Deep learning systems allow for real-time computer-aided detection (CADe) of polyps with high accuracy. We performed a multicenter, randomized trial to assess the safety and efficacy of a CADe system in detection of colorectal neoplasias during real-time colonoscopy.BACKGROUND & AIMSOne-fourth of colorectal neoplasias are missed during screening colonoscopies; these can develop into colorectal cancer (CRC). Deep learning systems allow for real-time computer-aided detection (CADe) of polyps with high accuracy. We performed a multicenter, randomized trial to assess the safety and efficacy of a CADe system in detection of colorectal neoplasias during real-time colonoscopy.We analyzed data from 685 subjects (61.32 ± 10.2 years old; 337 men) undergoing screening colonoscopies for CRC, post-polypectomy surveillance, or workup due to positive results from a fecal immunochemical test or signs or symptoms of CRC, at 3 centers in Italy from September through November 2019. Patients were randomly assigned (1:1) to groups who underwent high-definition colonoscopies with the CADe system or without (controls). The CADe system included an artificial intelligence-based medical device (GI-Genius, Medtronic) trained to process colonoscopy images and superimpose them, in real time, on the endoscopy display a green box over suspected lesions. A minimum withdrawal time of 6 minutes was required. Lesions were collected and histopathology findings were used as the reference standard. The primary outcome was adenoma detection rate (ADR, the percentage of patients with at least 1 histologically proven adenoma or carcinoma). Secondary outcomes were adenomas detected per colonoscopy, non-neoplastic resection rate, and withdrawal time.METHODSWe analyzed data from 685 subjects (61.32 ± 10.2 years old; 337 men) undergoing screening colonoscopies for CRC, post-polypectomy surveillance, or workup due to positive results from a fecal immunochemical test or signs or symptoms of CRC, at 3 centers in Italy from September through November 2019. Patients were randomly assigned (1:1) to groups who underwent high-definition colonoscopies with the CADe system or without (controls). The CADe system included an artificial intelligence-based medical device (GI-Genius, Medtronic) trained to process colonoscopy images and superimpose them, in real time, on the endoscopy display a green box over suspected lesions. A minimum withdrawal time of 6 minutes was required. Lesions were collected and histopathology findings were used as the reference standard. The primary outcome was adenoma detection rate (ADR, the percentage of patients with at least 1 histologically proven adenoma or carcinoma). Secondary outcomes were adenomas detected per colonoscopy, non-neoplastic resection rate, and withdrawal time.The ADR was significantly higher in the CADe group (54.8%) than in the control group (40.4%) (relative risk [RR], 1.30; 95% confidence interval [CI], 1.14-1.45). Adenomas detected per colonoscopy were significantly higher in the CADe group (mean, 1.07 ±1.54) than in the control group (mean 0.71 ± 1.20) (incidence rate ratio, 1.46; 95% CI, 1.15-1.86). Adenomas 5 mm or smaller were detected in a significantly higher proportion of subjects in the CADe group (33.7%) than in the control group (26.5%; RR, 1.26; 95% CI, 1.01-1.52), as were adenomas of 6 to 9 mm (detected in 10.6% of subjects in the CADe group vs 5.8% in the control group; RR, 1.78; 95% CI, 1.09-2.86), regardless of morphology or location. There was no significant difference between groups in withdrawal time (417 ± 101 seconds for the CADe group vs 435 ± 149 for controls; P = .1) or proportion of subjects with resection of non-neoplastic lesions (26.0% in the CADe group vs 28.7% of controls; RR, 1.00; 95% CI, 0.90-1.12).RESULTSThe ADR was significantly higher in the CADe group (54.8%) than in the control group (40.4%) (relative risk [RR], 1.30; 95% confidence interval [CI], 1.14-1.45). Adenomas detected per colonoscopy were significantly higher in the CADe group (mean, 1.07 ±1.54) than in the control group (mean 0.71 ± 1.20) (incidence rate ratio, 1.46; 95% CI, 1.15-1.86). Adenomas 5 mm or smaller were detected in a significantly higher proportion of subjects in the CADe group (33.7%) than in the control group (26.5%; RR, 1.26; 95% CI, 1.01-1.52), as were adenomas of 6 to 9 mm (detected in 10.6% of subjects in the CADe group vs 5.8% in the control group; RR, 1.78; 95% CI, 1.09-2.86), regardless of morphology or location. There was no significant difference between groups in withdrawal time (417 ± 101 seconds for the CADe group vs 435 ± 149 for controls; P = .1) or proportion of subjects with resection of non-neoplastic lesions (26.0% in the CADe group vs 28.7% of controls; RR, 1.00; 95% CI, 0.90-1.12).In a multicenter, randomized trial, we found that including CADe in real-time colonoscopy significantly increases ADR and adenomas detected per colonoscopy without increasing withdrawal time. ClinicalTrials.gov no: 04079478.CONCLUSIONSIn a multicenter, randomized trial, we found that including CADe in real-time colonoscopy significantly increases ADR and adenomas detected per colonoscopy without increasing withdrawal time. ClinicalTrials.gov no: 04079478. |
| Author | Maselli, Roberta Andrealli, Alida Correale, Loredana Lorenzetti, Roberto Sharma, Prateek Radaelli, Franco Craviotto, Vincenzo Antonelli, Giulio Pellegatta, Gaia Fugazza, Alessandro Anderloni, Andrea Alkandari, Asma Carrara, Silvia Rondonotti, Emanuele Badalamenti, Matteo Di Leo, Milena Lamonaca, Laura Wallace, Michael Spadaccini, Marco Galtieri, Piera Alessia Rosch, Thomas Hassan, Cesare Ferrara, Elisa Repici, Alessandro |
| Author_xml | – sequence: 1 givenname: Alessandro surname: Repici fullname: Repici, Alessandro email: alessandro.repici@hunimed.eu organization: Department of Gastroenterology, Humanitas Research Hospital, Milano, Italy. Electronic address: alessandro.repici@hunimed.eu – sequence: 2 givenname: Matteo surname: Badalamenti fullname: Badalamenti, Matteo organization: Department of Gastroenterology, Humanitas Research Hospital, Milano, Italy – sequence: 3 givenname: Roberta surname: Maselli fullname: Maselli, Roberta organization: Department of Gastroenterology, Humanitas Research Hospital, Milano, Italy – sequence: 4 givenname: Loredana surname: Correale fullname: Correale, Loredana organization: Department of Gastroenterology, Humanitas Research Hospital, Milano, Italy – sequence: 5 givenname: Franco surname: Radaelli fullname: Radaelli, Franco organization: Gastroenterology Department, Valduce Hospital, Como, Italy – sequence: 6 givenname: Emanuele surname: Rondonotti fullname: Rondonotti, Emanuele organization: Gastroenterology Department, Valduce Hospital, Como, Italy – sequence: 7 givenname: Elisa surname: Ferrara fullname: Ferrara, Elisa organization: Department of Gastroenterology, Humanitas Research Hospital, Milano, Italy – sequence: 8 givenname: Marco surname: Spadaccini fullname: Spadaccini, Marco organization: Department of Gastroenterology, Humanitas Research Hospital, Milano, Italy – sequence: 9 givenname: Asma surname: Alkandari fullname: Alkandari, Asma organization: Thanyan Alghanim Center for Gastroenterology and Hepatology, Alamiri Hospital, Kuwait, Kuwait – sequence: 10 givenname: Alessandro surname: Fugazza fullname: Fugazza, Alessandro organization: Department of Gastroenterology, Humanitas Research Hospital, Milano, Italy – sequence: 11 givenname: Andrea surname: Anderloni fullname: Anderloni, Andrea organization: Department of Gastroenterology, Humanitas Research Hospital, Milano, Italy – sequence: 12 givenname: Piera Alessia surname: Galtieri fullname: Galtieri, Piera Alessia organization: Department of Gastroenterology, Humanitas Research Hospital, Milano, Italy – sequence: 13 givenname: Gaia surname: Pellegatta fullname: Pellegatta, Gaia organization: Department of Gastroenterology, Humanitas Research Hospital, Milano, Italy – sequence: 14 givenname: Silvia surname: Carrara fullname: Carrara, Silvia organization: Department of Gastroenterology, Humanitas Research Hospital, Milano, Italy – sequence: 15 givenname: Milena surname: Di Leo fullname: Di Leo, Milena organization: Department of Gastroenterology, Humanitas Research Hospital, Milano, Italy – sequence: 16 givenname: Vincenzo surname: Craviotto fullname: Craviotto, Vincenzo organization: Department of Gastroenterology, Humanitas Research Hospital, Milano, Italy – sequence: 17 givenname: Laura surname: Lamonaca fullname: Lamonaca, Laura organization: Department of Gastroenterology, Humanitas Research Hospital, Milano, Italy – sequence: 18 givenname: Roberto surname: Lorenzetti fullname: Lorenzetti, Roberto organization: Digestive Endoscopy, Nuovo Regina Margherita Hospital, Rome, Italy – sequence: 19 givenname: Alida surname: Andrealli fullname: Andrealli, Alida organization: Gastroenterology Department, Valduce Hospital, Como, Italy – sequence: 20 givenname: Giulio surname: Antonelli fullname: Antonelli, Giulio organization: Digestive Endoscopy, Nuovo Regina Margherita Hospital, Rome, Italy – sequence: 21 givenname: Michael surname: Wallace fullname: Wallace, Michael organization: Mayo Clinic, Jacksonville, Florida – sequence: 22 givenname: Prateek surname: Sharma fullname: Sharma, Prateek organization: Kansas City Veterans Affairs Hospital, Kansas City, Missouri – sequence: 23 givenname: Thomas surname: Rosch fullname: Rosch, Thomas organization: Department of Interdisciplinary Endoscopy, University Hospital Hamburg-Eppendorf, Hamburg, Germany – sequence: 24 givenname: Cesare surname: Hassan fullname: Hassan, Cesare organization: Digestive Endoscopy, Nuovo Regina Margherita Hospital, Rome, Italy |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/32371116$$D View this record in MEDLINE/PubMed |
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| Title | Efficacy of Real-Time Computer-Aided Detection of Colorectal Neoplasia in a Randomized Trial |
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