基于快速区域卷积神经网络的胰腺癌人工智能诊断系统的建立与应用。
Establishment and application of an artificial intelligence diagnosis system for pancreatic cancer with a faster region-based convolutional neural network.
机构信息
Department of General Surgery, Affiliated Hospital of Qingdao University, Qingdao, Shandong 266003, China.
State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing 100191, China.
出版信息
Chin Med J (Engl). 2019 Dec 5;132(23):2795-2803. doi: 10.1097/CM9.0000000000000544.
BACKGROUND
Early diagnosis and accurate staging are important to improve the cure rate and prognosis for pancreatic cancer. This study was performed to develop an automatic and accurate imaging processing technique system, allowing this system to read computed tomography (CT) images correctly and make diagnosis of pancreatic cancer faster.
METHODS
The establishment of the artificial intelligence (AI) system for pancreatic cancer diagnosis based on sequential contrast-enhanced CT images were composed of two processes: training and verification. During training process, our study used all 4385 CT images from 238 pancreatic cancer patients in the database as the training data set. Additionally, we used VGG16, which was pre-trained in ImageNet and contained 13 convolutional layers and three fully connected layers, to initialize the feature extraction network. In the verification experiment, we used sequential clinical CT images from 238 pancreatic cancer patients as our experimental data and input these data into the faster region-based convolution network (Faster R-CNN) model that had completed training. Totally, 1699 images from 100 pancreatic cancer patients were included for clinical verification.
RESULTS
A total of 338 patients with pancreatic cancer were included in the study. The clinical characteristics (sex, age, tumor location, differentiation grade, and tumor-node-metastasis stage) between the two training and verification groups were insignificant. The mean average precision was 0.7664, indicating a good training effect of the Faster R-CNN. Sequential contrast-enhanced CT images of 100 pancreatic cancer patients were used for clinical verification. The area under the receiver operating characteristic curve calculated according to the trapezoidal rule was 0.9632. It took approximately 0.2 s for the Faster R-CNN AI to automatically process one CT image, which is much faster than the time required for diagnosis by an imaging specialist.
CONCLUSIONS
Faster R-CNN AI is an effective and objective method with high accuracy for the diagnosis of pancreatic cancer.
TRIAL REGISTRATION
ChiCTR1800017542; http://www.chictr.org.cn.
背景
早期诊断和准确分期对于提高胰腺癌的治愈率和预后至关重要。本研究旨在开发一种自动且准确的影像处理技术系统，使该系统能够正确读取计算机断层扫描（CT）图像并更快地做出胰腺癌诊断。
方法
基于序贯增强 CT 图像的胰腺癌诊断人工智能（AI）系统的建立包括两个过程：训练和验证。在训练过程中，我们的研究使用数据库中 238 例胰腺癌患者的 4385 张 CT 图像作为训练数据集。此外，我们使用在 ImageNet 中预先训练的包含 13 个卷积层和 3 个全连接层的 VGG16 来初始化特征提取网络。在验证实验中，我们使用来自 238 例胰腺癌患者的序贯临床 CT 图像作为实验数据，并将这些数据输入到已完成训练的更快区域卷积网络（Faster R-CNN）模型中。共有 100 例胰腺癌患者的 1699 张图像用于临床验证。
结果
共有 338 例胰腺癌患者纳入研究。两组训练和验证组的临床特征（性别、年龄、肿瘤位置、分化程度和肿瘤-淋巴结-转移分期）无显著差异。Faster R-CNN 的平均准确率为 0.7664，表明其训练效果良好。对 100 例胰腺癌患者的序贯增强 CT 图像进行了临床验证。根据梯形规则计算的受试者工作特征曲线下面积为 0.9632。Faster R-CNN AI 自动处理一张 CT 图像大约需要 0.2 秒，比影像专家诊断所需的时间快得多。
结论
Faster R-CNN AI 是一种有效且客观的方法，对胰腺癌的诊断具有较高的准确性。
试验注册
ChiCTR1800017542；http://www.chictr.org.cn。
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