Abstract：
Screening for colorectal cancer (CRC) with colonoscopy has improved patient outcomes; however, it remains the third leading cause of cancer-related mortality, novel strategies to improve screening are needed. Here, we propose an optical biopsy technique based on spectroscopic optical coherence tomography (OCT). Depth resolved OCT images are analyzed as a function of wavelength to measure optical tissue properties and used as input to machine learning algorithms. Previously, we used this approach to analyze mouse colon polyps. Here, we extend the approach to examine human biopsied colonic epithelial tissue samples ex vivo. Optical properties are used as input to a novel deep learning architecture, producing accuracy of up to 97.9% in discriminating tissue type. SOCT parameters are used to create false colored en face OCT images and deep learning classifications are used to enable visual classification by tissue type. This study advances SOCT toward clinical utility for analysis of colonic epithelium.
摘要：
通过结肠镜检查筛查结直肠癌（CRC）改善了患者的预后；然而，它仍然是癌症相关死亡率的第三大原因，需要新的策略来改善筛查。这里,我们提出了一种基于光谱光学相干断层扫描（OCT）的光学活检技术。深度分辨OCT图像作为波长的函数被分析以测量光学组织性质并且用作机器学习算法的输入。以前,我们用这种方法分析了小鼠结肠息肉.这里,我们将该方法扩展到体外检查人类活检的结肠上皮组织样本。光学特性被用作新型深度学习架构的输入，识别组织类型的准确率高达97.9%。SOCT参数用于创建假彩色人脸OCT图像，深度学习分类用于按组织类型进行视觉分类。这项研究将SOCT推向了结肠上皮分析的临床应用。
