AI-Assisted Microendoscopy for the Early Detection of Esophageal Cancer

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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.