Efficacy of Real-Time Computer-Aided Detection of Colorectal Neoplasia in a Randomized Trial.

By A. Repici, M. Badalamenti, R. Maselli, L. Correale, F. Radaelli, E. Rondonotti, E. Ferrara, M. Spadaccini, A. Alkandari, A. Fugazza and 14 more. Gastroenterology, 2020

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BACKGROUND & AIMS 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. METHODS We analyzed data from 685 subjects (61.32±10.2 years old; 337 women) undergoing screening colonoscopies for CRC, post-polypectomy surveillance, or work up due to positive results from a fecal immunochemical test or signs or symptoms of CRC, at three 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 min 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. RESULTS The ADR was significantly higher in the CADe group (54.8%) than in the control group (40.4%) (relative risk [RR], 1.30; 95% 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 6-9 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 sec 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). CONCLUSIONS 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.

Subfields: Artificial Intelligence, Oncology, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/8d84eacf8607dc7744621f54bfca725ac2760ccb

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From patterns to patients: Advances in clinical machine learning for cancer diagnosis, prognosis, and treatment. (2023, background): “GI Genius, an FDA-approved device for polyp detection in endoscopy videos, uses a CNN on individual video frames to produce bounding boxes over suspicious lesions.(93) Skin cancer is a promising yet challenging domain.”