Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan.
Digestive Disease Center, Showa University Koto Toyosu Hospital, Tokyo, Japan.
Endoscopy. 2017 Aug;49(8):798-802. doi: 10.1055/s-0043-105486. Epub 2017 May 4.
Invasive cancer carries the risk of metastasis, and therefore, the ability to distinguish between invasive cancerous lesions and less-aggressive lesions is important. We evaluated a computer-aided diagnosis system that uses ultra-high (approximately × 400) magnification endocytoscopy (EC-CAD). We generated an image database from a consecutive series of 5843 endocytoscopy images of 375 lesions. For construction of a diagnostic algorithm, 5543 endocytoscopy images from 238 lesions were randomly extracted from the database for machine learning. We applied the obtained algorithm to 200 endocytoscopy images and calculated test characteristics for the diagnosis of invasive cancer. We defined a high-confidence diagnosis as having a ≥ 90 % probability of being correct. Of the 200 test images, 188 (94.0 %) were assessable with the EC-CAD system. Sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) were 89.4 %, 98.9 %, 94.1 %, 98.8 %, and 90.1 %, respectively. High-confidence diagnosis had a sensitivity, specificity, accuracy, PPV, and NPV of 98.1 %, 100 %, 99.3 %, 100 %, and 98.8 %, respectively. EC-CAD may be a useful tool in diagnosing invasive colorectal cancer.
侵袭性癌症有转移的风险，因此，能够区分侵袭性癌性病变和侵袭性较低的病变非常重要。我们评估了一种使用超高（约×400）放大倍率的内镜下细胞学检查（EC-CAD）的计算机辅助诊断系统。我们从 375 个病变的 5843 个内镜下细胞学图像的连续系列中生成了一个图像数据库。为了构建诊断算法，我们从数据库中随机提取了 238 个病变的 5543 个内镜下细胞学图像用于机器学习。我们将获得的算法应用于 200 个内镜下细胞学图像，并计算了诊断侵袭性癌症的测试特征。我们将高置信度诊断定义为正确的概率≥90%。在 200 个测试图像中，有 188 个（94.0%）可通过 EC-CAD 系统进行评估。敏感性、特异性、准确性、阳性预测值（PPV）和阴性预测值（NPV）分别为 89.4%、98.9%、94.1%、98.8%和 90.1%。高置信度诊断的敏感性、特异性、准确性、PPV 和 NPV 分别为 98.1%、100%、99.3%、100%和 98.8%。EC-CAD 可能是诊断结直肠侵袭性癌症的有用工具。
