Accuracy of diagnosing invasive colorectal cancer using computer-aided endocytoscopy
Accuracy of diagnosing invasive colorectal cancer using computer-aided endocytoscopy
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DOI：
10.1055/s-0043-105486
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发表时间：
2017-08-01
期刊：
影响因子：
9.3
通讯作者：
Mori, Kensaku
中科院分区：
文献类型：
--
作者：
Takeda, Kenichi;Kudo, Shin-ei;Mori, Kensaku
背景和研究目的侵袭性癌具有转移的风险，因此，区分侵袭性癌病变和非侵袭性癌病变的能力是重要的。我们评估了一种使用超高倍(约400倍)细胞内窥镜(EC-CAD)的计算机辅助诊断系统。患者和方法我们从连续的375个病变的5843张细胞内窥镜图像中生成图像数据库。为了构建诊断算法，从数据库中随机提取了来自238个病变的5543张内窥镜图像，用于机器学习。我们将所得到的算法应用于200幅内窥镜图像，并计算了用于诊断浸润性癌的测试特征。我们将高置信度诊断定义为正确概率为90%。结果在200张测试图像中，188张(94.0%)可用EC-CAD系统进行评估。其敏感性为89.4%，特异性为98.9%，准确性为94.1%，阳性预测值为98.8%，阴性预测值为90.1%。高信度诊断的敏感度、特异度、准确度、PPV和NPV分别为98.1%、100%、99.3%、100%和98.8%。结论：EC-CAD是诊断浸润性结直肠癌的有用工具。
Background and study aims 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 x 400) magnification endocytoscopy (EC-CAD).Patients and methods 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.Results Of the 200 test images, 188 (94.0%) were assessable with the EC-CADsystem. 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.Conclusion: EC-CADmay be a useful tool in diagnosing invasive colorectal cancer.