Efficacy of Real-Time Computer-Aided Detection of Colorectal Neoplasia in a Randomized Trial
Efficacy of Real-Time Computer-Aided Detection of Colorectal Neoplasia in a Randomized Trial
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DOI：
10.1053/j.gastro.2020.04.062
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发表时间：
2020-08-01
期刊：
影响因子：
29.4
通讯作者：
Hassan, Cesare
中科院分区：
文献类型：
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作者：
Repici, Alessandro;Badalamenti, Matteo;Hassan, Cesare
背景与目的：四分之一的结直肠肿瘤在结肠镜检查中被漏诊，这些肿瘤可能发展为结直肠癌（CRC）。深度学习系统允许以高精度实时计算机辅助检测（CADe）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。方法：我们分析了2019年9月至11月在意大利的3个中心进行CRC筛查结肠镜检查、息肉切除术后监测或由于粪便免疫化学检测阳性结果或CRC体征或症状而进行检查的685例受试者（61.32 ± 10.2岁; 337例男性）的数据。患者被随机分配（1：1）到使用CADe系统或不使用（对照）进行高清结肠镜检查的组。CADe系统包括一个基于人工智能的医疗设备（GI-Genius，Medtronic），该设备经过培训可以处理结肠镜检查图像，并在内窥镜检查显示器上真实的实时显示这些图像，可疑病变上方有一个绿色框。需要至少6分钟的停药时间。收集病变，并将组织病理学结果用作参考标准。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）。次要结局是结肠镜检查发现的腺瘤、非肿瘤切除率和停药时间。研究结果：CADe组的ADR（54.8%）显著高于对照组（40.4%）（相对风险[RR]，1.30; 95%置信区间[CI]，1.14-1.45）。CADe组结肠镜检查发现的腺瘤（平均值为1.07 ± 1.54）显著高于对照组（平均值为0.71 ± 1.20）（发病率比为1.46; 95%CI为1.15-1.86）。在CADe组的受试者中检测到腺瘤5 mm或更小的比例显著更高（33.7%）高于对照组（26.5%; RR，1.26; 95% CI，1.01-1.52），6 - 9 mm腺瘤也是如此（在CADe组10.6%的受试者中检测到，对照组5.8%; RR，1.78; 95% CI，1.09-2.86），无论形态或位置如何。两组间停药时间（CADe组为417 +/- 101秒，对照组为435 +/- 149秒; P = .1）或切除非肿瘤性病变的受试者比例（CADe组为26.0%，对照组为28.7%; RR，1.00; 95% CI，0.90-1.12）无显著差异。结论：在一项多中心随机试验中，我们发现在实时结肠镜检查中包括CADe显著增加了每次结肠镜检查检测到的ADR和腺瘤，而没有增加停药时间。
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 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. 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% 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). 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.