Department of Gastroenterology and Hepatology, Academic Medical Center, Amsterdam, the Netherlands.
Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands.
Gastrointest Endosc. 2017 Nov;86(5):839-846. doi: 10.1016/j.gie.2017.03.011. Epub 2017 Mar 16.
Volumetric laser endomicroscopy (VLE) is an advanced imaging system that provides a near-microscopic resolution scan of the esophageal wall layers up to 3-mm deep. VLE has the potential to improve detection of early neoplasia in Barrett's esophagus (BE). However, interpretation of VLE images is complex because of the large amount of data that need to be interpreted in real time. The aim of this study was to investigate the feasibility of a computer algorithm to identify early BE neoplasia on ex vivo VLE images.
We used 60 VLE images from a database of high-quality ex vivo VLE-histology correlations, obtained from BE patients ± neoplasia (30 nondysplastic BE [NDBE] and 30 high-grade dysplasia/early adenocarcinoma images). VLE features from a recently developed clinical VLE prediction score for BE neoplasia served as input for the algorithm: (1) higher VLE surface than subsurface signal and (2) lack of layering. With this input, novel clinically inspired algorithm features were developed, based on signal intensity statistics and grayscale correlations. For comparison, generic image analysis methods were examined for their performance to detect neoplasia. For classification of the images in the NDBE or neoplastic group, several machine learning methods were evaluated. Leave-1-out cross-validation was used for algorithm validation.
Three novel clinically inspired algorithm features were developed. The feature "layering and signal decay statistics" showed the optimal performance compared with the other clinically features ("layering" and "signal intensity distribution") and generic image analyses methods, with an area under the receiver operating characteristic curve (AUC) of .95. Corresponding sensitivity and specificity were 90% and 93%, respectively. In addition, the algorithm showed a better performance than the clinical VLE prediction score (AUC .81).
This is the first study in which a computer algorithm for BE neoplasia was developed based on VLE images with direct histologic correlates. The algorithm showed good performance to detect BE neoplasia in ex vivo VLE images compared with the performance of a recently developed clinical VLE prediction score. This study suggests that an automatic detection algorithm has the potential to assist endoscopists in detecting early neoplasia on VLE. Future studies on in vivo VLE scans are needed to further validate the algorithm.
容积激光内镜检查（VLE）是一种先进的成像系统，可对食管壁层进行近微观分辨率扫描，深度可达 3 毫米。VLE 有可能提高对巴雷特食管（BE）早期肿瘤的检测。然而，由于需要实时解释大量数据，因此 VLE 图像的解释非常复杂。本研究旨在探讨一种计算机算法在离体 VLE 图像上识别早期 BE 肿瘤的可行性。
我们使用了来自 BE 患者（伴或不伴肿瘤）高质量离体 VLE-组织学相关性数据库中的 60 个 VLE 图像（30 个非异型增生 BE [NDBE]和 30 个高级别异型增生/早期腺癌图像）。该算法的输入是最近开发的用于预测 BE 肿瘤的临床 VLE 预测评分中的 VLE 特征：（1）较高的 VLE 表面信号与较深的信号强度比和（2）缺乏分层。基于此输入，我们开发了新的基于信号强度统计和灰度相关性的临床启发式算法特征。为了比较，我们还检查了通用图像分析方法在检测肿瘤方面的性能。为了对 NDBE 或肿瘤组中的图像进行分类，我们评估了几种机器学习方法。采用留一法交叉验证对算法进行验证。
开发了三个新的临床启发式算法特征。与其他临床特征（“分层”和“信号强度分布”）和通用图像分析方法相比，“分层和信号衰减统计”特征的表现最佳，其受试者工作特征曲线下面积（AUC）为.95。相应的灵敏度和特异性分别为 90%和 93%。此外，该算法的性能优于最近开发的临床 VLE 预测评分（AUC.81）。
这是第一项基于具有直接组织学相关性的离体 VLE 图像开发用于 BE 肿瘤的计算机算法的研究。与最近开发的临床 VLE 预测评分相比，该算法在检测离体 VLE 图像中的 BE 肿瘤方面表现出良好的性能。本研究表明，自动检测算法有可能帮助内镜医生在 VLE 上检测早期肿瘤。需要进一步的体内 VLE 扫描研究来验证该算法。
