Support System of Cystoscopic Diagnosis for Bladder Cancer Based on Artificial Intelligence
Support System of Cystoscopic Diagnosis for Bladder Cancer Based on Artificial Intelligence
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DOI：
10.1089/end.2019.0509
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发表时间：
2020-01-14
影响因子：
2.7
通讯作者：
Nishiyama, Hiroyuki
中科院分区：
文献类型：
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作者：
Ikeda, Atsushi;Nosato, Hirokazu;Nishiyama, Hiroyuki
简介：非肌层浸润性膀胱癌尽管采用常规治疗方法，术后复发率仍相对较高。膀胱镜检查是诊断和监测膀胱癌必不可少的，但病变被忽视，而使用白光成像。使用膀胱镜检查，直径小的肿瘤;扁平肿瘤，如原位癌;以及与隆起病变相关的扁平病变的范围很难识别。此外，使用膀胱镜检查的诊断和治疗的准确性根据医生的技能和经验而变化。因此，为了提高膀胱癌诊断的质量，我们的目标是使用人工智能（AI）支持膀胱癌的膀胱镜诊断。材料与方法：总共2102个膀胱镜图像，包括1671个正常组织图像和431个肿瘤病变图像，用于创建训练图像和测试图像比例为8：2的数据集。我们构建了一个基于卷积神经网络（CNN）的肿瘤分类器。使用测试数据评估训练的分类器的性能。将阈值改变时的真阳性率和假阳性率绘制为受试者工作特征曲线（ROC）。结果如下：在测试数据中（肿瘤图像：87，正常图像：335），78幅图像为真阳性，315幅为真阴性，20幅为假阳性，9幅为假阴性。ROC曲线下面积为0.98，最大约登指数为0.837，敏感性为89.7%，特异性为94.0%。结论：通过CNN客观评价膀胱镜图像，可以对图像进行分类，包括肿瘤病变和正常。使用AI对膀胱镜图像进行客观评价，有望有助于提高膀胱癌诊断和治疗的准确性。
Introduction: Nonmuscle-invasive bladder cancer has a relatively high postoperative recurrence rate despite the implementation of conventional treatment methods. Cystoscopy is essential for diagnosing and monitoring bladder cancer, but lesions are overlooked while using white-light imaging. Using cystoscopy, tumors with a small diameter; flat tumors, such as carcinoma in situ; and the extent of flat lesions associated with the elevated lesions are difficult to identify. In addition, the accuracy of diagnosis and treatment using cystoscopy varies according to the skill and experience of physicians. Therefore, to improve the quality of bladder cancer diagnosis, we aimed to support the cystoscopic diagnosis of bladder cancer using artificial intelligence (AI). Materials and Methods: A total of 2102 cystoscopic images, consisting of 1671 images of normal tissue and 431 images of tumor lesions, were used to create a dataset with an 8:2 ratio of training and test images. We constructed a tumor classifier based on a convolutional neural network (CNN). The performance of the trained classifier was evaluated using test data. True-positive rate and false-positive rate were plotted when the threshold was changed as the receiver operating characteristic (ROC) curve. Results: In the test data (tumor image: 87, normal image: 335), 78 images were true positive, 315 true negative, 20 false positive, and 9 false negative. The area under the ROC curve was 0.98, with a maximum Youden index of 0.837, sensitivity of 89.7%, and specificity of 94.0%. Conclusion: By objectively evaluating the cystoscopic image with CNN, it was possible to classify the image, including tumor lesions and normality. The objective evaluation of cystoscopic images using AI is expected to contribute to improvement in the accuracy of the diagnosis and treatment of bladder cancer.