Department of Physics and Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, United Kingdom.
Department of Gastroenterology, Hepatology, and Clinical Oncology, Medical Center for Postgraduate Education, Warsaw, Poland.
Cancer Res. 2021 Jun 15;81(12):3415-3425. doi: 10.1158/0008-5472.CAN-21-0474. Epub 2021 May 26.
Early detection of esophageal neoplasia enables curative endoscopic therapy, but the current diagnostic standard of care has low sensitivity because early neoplasia is often inconspicuous with conventional white-light endoscopy. Here, we hypothesized that spectral endoscopy could enhance contrast for neoplasia in surveillance of patients with Barrett's esophagus. A custom spectral endoscope was deployed in a pilot clinical study of 20 patients to capture 715 tissue spectra matched with gold standard diagnosis from histopathology. Spectral endoscopy was sensitive to changes in neovascularization during the progression of disease; both non-dysplastic and neoplastic Barrett's esophagus showed higher blood volume relative to healthy squamous tissue ( = 0.001 and 0.02, respectively), and vessel radius appeared larger in neoplasia relative to non-dysplastic Barrett's esophagus ( = 0.06). We further developed a deep learning algorithm capable of classifying spectra of neoplasia versus non-dysplastic Barrett's esophagus with high accuracy (84.8% accuracy, 83.7% sensitivity, 85.5% specificity, 78.3% positive predictive value, and 89.4% negative predictive value). Exploiting the newly acquired library of labeled spectra to model custom color filter sets identified a potential 12-fold enhancement in contrast between neoplasia and non-dysplastic Barrett's esophagus using application-specific color filters compared with standard-of-care white-light imaging (perceptible color difference = 32.4 and 2.7, respectively). This work demonstrates the potential of endoscopic spectral imaging to extract vascular properties in Barrett's esophagus, to classify disease stages using deep learning, and to enable high-contrast endoscopy. SIGNIFICANCE: The results of this pilot first-in-human clinical trial demonstrate the potential of spectral endoscopy to reveal disease-associated vascular changes and to provide high-contrast delineation of neoplasia in the esophagus. GRAPHICAL ABSTRACT: http://cancerres.aacrjournals.org/content/canres/81/12/3415/F1.large.jpg.
早期发现食管肿瘤可进行治愈性内镜治疗，但目前的标准护理诊断方法敏感性较低，因为早期肿瘤在常规白光内镜下通常不明显。在这里，我们假设光谱内镜可以增强对巴雷特食管患者监测中的肿瘤的对比度。在一项针对 20 名患者的试点临床研究中部署了一种定制的光谱内镜，以捕获与组织病理学的金标准诊断相匹配的 715 个组织光谱。光谱内镜对疾病进展过程中新生血管变化敏感；非异型增生和异型增生的巴雷特食管均显示出相对健康的鳞状组织更高的血容量（= 0.001 和 0.02），并且肿瘤中的血管半径相对非异型增生的巴雷特食管更大（= 0.06）。我们进一步开发了一种深度学习算法，能够以高精度对肿瘤与非异型增生的巴雷特食管的光谱进行分类（84.8%的准确率、83.7%的敏感性、85.5%的特异性、78.3%的阳性预测值和 89.4%的阴性预测值）。利用新获得的标记光谱库来构建自定义颜色滤光片集，与标准护理白光成像相比，使用特定于应用的颜色滤光片可以使肿瘤与非异型增生的巴雷特食管之间的对比度提高 12 倍（可感知的颜色差异分别为 32.4 和 2.7）。这项工作证明了内镜光谱成像在巴雷特食管中提取血管特性的潜力，使用深度学习对疾病阶段进行分类，以及实现高对比度内镜的潜力。意义：这项首次人体临床试验的结果表明，光谱内镜有可能揭示与疾病相关的血管变化，并提供食管肿瘤的高对比度描绘。
