Department of Gastroenterology, Humanitas Research Hospital, Milano, Italy.
Department of Gastroenterology, Humanitas Research Hospital, Milano, Italy.
Gastroenterology. 2020 Aug;159(2):512-520.e7. doi: 10.1053/j.gastro.2020.04.062. Epub 2020 May 1.
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.
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.
四分之一的结直肠肿瘤在筛查结肠镜检查中漏诊;这些肿瘤可能发展为结直肠癌(CRC)。深度学习系统允许实时计算机辅助检测(CADe)具有高精度的息肉。我们进行了一项多中心、随机试验,以评估 CADe 系统在实时结肠镜检查中检测结直肠肿瘤的安全性和有效性。
我们分析了 2019 年 9 月至 11 月在意大利 3 个中心接受 CRC、息肉切除后监测或因粪便免疫化学检测阳性或 CRC 体征或症状而接受检查的 685 例(61.32±10.2 岁;337 例男性)接受筛查结肠镜检查的患者的数据。患者随机(1:1)分为接受带有 CADe 系统的高清结肠镜检查或无 CADe 系统(对照组)的两组。CADe 系统包括一个基于人工智能的医疗设备(GI-Genius,Medtronic),用于处理结肠镜图像并实时将其叠加到内窥镜显示器上,可疑病变上叠加一个绿色方框。需要至少 6 分钟的退出时间。收集病变并使用组织病理学发现作为参考标准。主要结局是腺瘤检出率(ADR,至少有 1 例组织学证实的腺瘤或癌的患者百分比)。次要结局是每例结肠镜检查检测到的腺瘤、非肿瘤性切除率和退出时间。
CADe 组的 ADR 显著高于对照组(54.8%比 40.4%)(相对风险 [RR],1.30;95%置信区间 [CI],1.14-1.45)。CADe 组每例结肠镜检查检测到的腺瘤明显多于对照组(平均 1.07±1.54 比 0.71±1.20)(发病率比,1.46;95%CI,1.15-1.86)。CADe 组中 5mm 或更小的腺瘤的检出比例明显高于对照组(33.7%比 26.5%)(RR,1.26;95%CI,1.01-1.52),6-9mm 的腺瘤也是如此(CADe 组中 10.6%的患者检出,对照组中 5.8%;RR,1.78;95%CI,1.09-2.86),无论形态或位置如何。两组退出时间(CADe 组为 417±101 秒,对照组为 435±149 秒;P=0.1)或非肿瘤性病变切除比例(CADe 组为 26.0%,对照组为 28.7%)无显著差异RR,1.00;95%CI,0.90-1.12)。
在一项多中心、随机试验中,我们发现实时结肠镜检查中包含 CADe 可显著提高 ADR 和每例结肠镜检查检测到的腺瘤,而不会增加退出时间。ClinicalTrials.gov 编号:04079478。