AI-Assisted Microendoscopy for the Early Detection of Esophageal Cancer

翻译后摘要：食管癌是第6个最常见的原因，癌症相关的死亡率在世界范围内。而 食管鳞状细胞瘤（ESCN）是一个重大的全球负担，在某些服务不足的国家， 地理区域（南美洲、东非、伊朗东部、中国北方）的 由于缺乏内窥镜检查能力，发病率和死亡率很高。当内镜检查时， 色素内镜或“数字”色素内镜显示出筛查的高灵敏度（>95%），特异性是 由于混淆的炎症区域，不良（<60%）和假阳性结果比比皆是。因此，标准 护理内窥镜检查产生许多不必要的活检，增加了内窥镜筛查的风险和成本， 监视 在我们正在进行的R 01项目中，我们开发并验证了一种移动的高分辨率显微内窥镜（mHRME） 用于ESCN的筛查和监测。尽管COVID延迟了≥2年，这尤其影响了 在中国的研究中心，我们完成了：（1）一项随机对照临床试验（美国和中国; n=918）， 对接受食管鳞状细胞瘤筛查或监测的患者进行目视判读， (2)用于自动检测肿瘤图像的深度学习软件算法，以及（3）试点试验（n=41） 巴西的软件辅助mHRME。试验显示定性（目视） 监测组中专家解释而非新手解释（100%比19%，P<0.05）。在筛选组中， 诊断率（肿瘤活检/总活检）增加了3.6倍（8 - 29%）; 16%的患者 正确地避免了任何活检; 18%的人改变了临床计划。在一项单组初步研究中，我们还评估了 基于人工智能的移动的HRME（AI-mHRME）在41名巴西受试者。本研究（2022年1月） 证实了定量解释（AI-mHRME）使诊断率提高了一倍，改善了内镜医生的 具有显著的临床影响（64%的临床计划变更）。我们最初的深度学习算法 敏感性/特异性为100%/85%。参与的临床医生一致表示，他们倾向于人工智能指导 但他对这一做法的实施表示关切。 在这次竞争性更新中，我们将基于这些有价值的全球数据来优化AI-mHRME并评估 其在种族和社会经济多样化人群中的临床影响和实施潜力 美国和巴西。将采用一种由业主参与的方法来评估障碍、可接受性， 在ESCN管理中使用AI-mHRME的适当性和可行性，并确定环境因素 影响收养。所获得的数据将有助于实施和传播创新的、人工智能辅助的 在不同人群和其他癌症中的癌症筛查策略。

Abstract: Esophageal cancer is the 6th most common cause of cancer-related mortality worldwide. While esophageal squamous cell neoplasia (ESCN) carries a significant global burden, those in certain underserved geographic regions (South America, eastern Africa, eastern Iran, northern China) have particularly high incidence and mortality rates due to lack of endoscopic screening capacity. While endoscopy with Lugol’s chromoendoscopy or “digital” chromoendoscopy has shown high sensitivity (>95%) for screening, specificity is poor (<60%) and false-positive results abound due to confounding inflammatory areas. As a result, standard of care endoscopy produces many unnecessary biopsies, increasing risk and cost of endoscopic screening and surveillance. In our ongoing R01 project, we developed and validated a mobile, high-resolution microendoscope (mHRME) for screening and surveillance of ESCN. Despite ≥2 years of COVID delays, which especially impacted the Chinese sites, we completed: (1) a randomized, controlled clinical trial (USA and China; n=918) of mHRME with visual interpretation in patients undergoing screening or surveillance for esophageal squamous cell neoplasia, (2) deep-learning software algorithms for automated detection of neoplastic images, and (3) a pilot trial (n=41) of the software-assisted mHRME in Brazil. The trial revealed higher specificity for qualitative (visual) interpretation by experts but not novices, in surveillance arm (100% vs. 19%, p<0.05). In the screening arm, diagnostic yield (neoplastic biopsies/total biopsies) increased 3.6 times (8 to 29%); 16% of patients were correctly spared any biopsy; and 18% had a change in clinical plan. In a single-arm pilot study, we also evaluated an artificial intelligence-based mobile HRME (AI-mHRME) in 41 Brazilian subjects. This study (January 2022) confirmed that quantitative interpretation (AI-mHRME) doubled diagnostic yield, improved endoscopists’ confidence, and had significant clinical impact (change of clinical plan in 64%). Our initial deep-learning algorithm had a sensitivity/specificity of 100%/85%. Participating clinicians uniformly said they favored an AI-guided approach but expressed concerns about its implementation. In this competing renewal, we will build on this valuable global data to optimize an AI-mHRME and evaluate its clinical impact and implementation potential in ethnically and socioeconomically diverse populations in the USA and Brazil. A stakeholder-engaged approach will be used to evaluate barriers, acceptability, appropriateness, and feasibility of using AI-mHRME in ESCN management and to determine contextual factors influencing adoption. Data obtained will facilitate implementation and dissemination of innovative, AI-assisted cancer screening strategies in diverse populations and other cancers.