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人工智能辅助系统对食管浅表鳞状细胞癌及癌前病变内镜诊断的影响:一项多中心、串联、双盲、随机对照研究。

Effect of an artificial intelligence-assisted system on endoscopic diagnosis of superficial oesophageal squamous cell carcinoma and precancerous lesions: a multicentre, tandem, double-blind, randomised controlled trial.

机构信息

Department of Gastroenterology and Hepatology, West China Hospital, Sichuan University, Chengdu, China.

Department of Gastroenterology, Meishan People's Hospital, Meishan, China.

出版信息

Lancet Gastroenterol Hepatol. 2024 Jan;9(1):34-44. doi: 10.1016/S2468-1253(23)00276-5. Epub 2023 Nov 10.

Abstract

BACKGROUND

Despite the usefulness of white light endoscopy (WLE) and non-magnified narrow-band imaging (NBI) for screening for superficial oesophageal squamous cell carcinoma and precancerous lesions, these lesions might be missed due to their subtle features and interpretation variations among endoscopists. Our team has developed an artificial intelligence (AI) system to detect superficial oesophageal squamous cell carcinoma and precancerous lesions using WLE and non-magnified NBI. We aimed to evaluate the auxiliary diagnostic performance of the AI system in a real clinical setting.

METHODS

We did a multicentre, tandem, double-blind, randomised controlled trial at 12 hospitals in China. Eligible patients were aged 18 years or older and underwent sedated upper gastrointestinal endoscopy for screening, investigation of gastrointestinal symptoms, or surveillance. Patients were randomly assigned (1:1) to either the AI-first group or the routine-first group using a computerised random number generator. Patients, pathologists, and statistical analysts were masked to group assignment, whereas endoscopists and research assistants were not. The same endoscopist at each centre did tandem upper gastrointestinal endoscopy for each eligible patient on the same day. In the AI-first group, the endoscopist did the first examination with the assistance of the AI system and the second examination without it. In the routine-first group, the order of examinations was reversed. The primary outcome was the miss rate of superficial oesophageal squamous cell carcinoma and precancerous lesions, calculated on a per-lesion and per-patient basis. All analyses were done on a per-protocol basis. This trial is registered with the Chinese Clinical Trial Registry (ChiCTR2100052116) and is completed.

FINDINGS

Between Oct 19, 2021, and June 8, 2022, 5934 patients were randomly assigned to the AI-first group and 5912 to the routine-first group, of whom 5865 and 5850 were eligible for analysis. Per-lesion miss rates were 1·7% (2/118; 95% CI 0·0-4·0) in the AI-first group versus 6·7% (6/90; 1·5-11·8) in the routine-first group (risk ratio 0·25, 95% CI 0·06-1·08; p=0·079). Per-patient miss rates were 1·9% (2/106; 0·0-4·5) in AI-first group versus 5·1% (4/79; 0·2-9·9) in the routine-first group (0·37, 0·08-1·71; p=0·40). Bleeding after biopsy of oesophageal lesions was observed in 13 (0·2%) patients in the AI-first group and 11 (0·2%) patients in the routine-first group. No serious adverse events were reported by patients in either group.

INTERPRETATION

The observed effect of AI-assisted endoscopy on the per-lesion and per-patient miss rates of superficial oesophageal squamous cell carcinoma and precancerous lesions under WLE and non-magnified NBI was consistent with substantial benefit through to a neutral or small negative effect. The effectiveness and cost-benefit of this AI system in real-world clinical settings remain to be further assessed.

FUNDING

National Natural Science Foundation of China, 1·3·5 project for disciplines of excellence, West China Hospital, Sichuan University, and Chengdu Science and Technology Project.

TRANSLATION

For the Chinese translation of the abstract see Supplementary Materials section.

摘要

背景

尽管白光内镜(WLE)和非放大窄带成像(NBI)在筛查食管浅表鳞状细胞癌和癌前病变方面具有一定的作用,但由于这些病变的细微特征以及内镜医生之间的解读差异,这些病变可能会被漏诊。我们的团队开发了一种人工智能(AI)系统,用于使用 WLE 和非放大 NBI 检测食管浅表鳞状细胞癌和癌前病变。我们旨在评估该 AI 系统在真实临床环境中的辅助诊断性能。

方法

我们在中国的 12 家医院进行了一项多中心、串联、双盲、随机对照试验。符合条件的患者年龄在 18 岁及以上,接受镇静上消化道内镜检查用于筛查、胃肠道症状调查或监测。患者使用计算机生成的随机数发生器以 1:1 的比例随机分配到 AI 优先组或常规优先组。患者、病理学家和统计分析人员对分组情况进行了盲法,而内镜医生和研究助理则没有。每个中心的同一位内镜医生在同一天对每个符合条件的患者进行串联上消化道内镜检查。在 AI 优先组中,内镜医生首先在 AI 系统的协助下进行第一次检查,然后在没有 AI 系统协助的情况下进行第二次检查。在常规优先组中,检查顺序相反。主要结局是根据病变和患者计算的食管浅表鳞状细胞癌和癌前病变的漏诊率。所有分析均基于方案进行。该试验在中国临床试验注册中心(ChiCTR2100052116)注册,并已完成。

结果

2021 年 10 月 19 日至 2022 年 6 月 8 日,5934 名患者被随机分配到 AI 优先组,5912 名患者被随机分配到常规优先组,其中 5865 名和 5850 名患者符合分析条件。AI 优先组的病变漏诊率为 1.7%(2/118;95%CI,0.0-4.0),常规优先组为 6.7%(6/90;1.5-11.8)(风险比 0.25,95%CI 0.06-1.08;p=0.079)。AI 优先组的患者漏诊率为 1.9%(2/106;0.0-4.5),常规优先组为 5.1%(4/79;0.2-9.9)(0.37,0.08-1.71;p=0.40)。AI 优先组的 13 名(0.2%)患者和常规优先组的 11 名(0.2%)患者在食管病变活检后出现出血。两组患者均未报告严重不良事件。

结论

在 WLE 和非放大 NBI 下,观察到 AI 辅助内镜检查对食管浅表鳞状细胞癌和癌前病变的病变和患者漏诊率的影响与实质性获益一致,直至中性或小的负性影响。该 AI 系统在真实临床环境中的有效性和成本效益仍有待进一步评估。

资助

国家自然科学基金,1·3·5 学科卓越计划,四川大学华西医院和成都科技项目。

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