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  • Title: [Clinical application of convolutional neural network in pathological diagnosis of metastatic lymph nodes of gastric cancer].
    Author: Wang SZ, Wang JG, Lu Y, Zhang YJ, Xin FJ, Liu SL, Zhang XX, Liu GW, Li S, Sui D, Wang DS.
    Journal: Zhonghua Wai Ke Za Zhi; 2019 Dec 01; 57(12):934-938. PubMed ID: 31826599.
    Abstract:
    Objective: To examine the value and clinical application of convolutional neural network in pathological diagnosis of metastatic lymph nodes of gastric cancer. Methods: Totally 124 patients with advanced gastric cancer who underwent radical gastrectomy plus D2 lymphadenectomy at Affiliated Hospital of Qingdao University from July 2016 to December 2018 were selected in the study. According to the chronological order, the first 80 cases were served as learning group. The remaining 44 cases were served as verification group. There were 45 males and 35 females in the study group, with average age of 57.6 years. There were 29 males and 15 females in the validation group, with average age of 9.2 years. The pre-training convolutional neural network architecture Resnet50 was trained and fine-tuned by 21 352 patches with cancer areas and 14 997 patches without cancer areas in the training group. A total of 78 whole-slide image served as a test dataset including positive (n=38) and negative (n=40) lymph nodes. The convolutional neural network computer-aided detection (CNN-CAD) system was used to analyze the ability of convolutional neural network system to screen metastatic lymph nodes at the level of slice by setting threshold, and evaluate the system's classification accuracy by calculating its sensitivity, specificity, positive predictive value, negative predictive value and area under the receiver operating characteristic curve (AUC). Results: The classification accuracy of CNN-CAD system at slice level was 100%.The AUC for the CNN-CAD system was 0.89. The sensitivity was 0.778, specificity was 0.995, overall accuracy was 0.989. Positive and negative predictive values were 0.822 and 0.994, respectively. The CNN-CAD system achieved the same classification results as pathologists. Conclusions: The CNN-CAD system has been constructed to distinguished benign and malignant lymph node slides with high accuracy and specificity. It could achieve the similar classification results as pathologists. 目的： 探讨卷积神经网络在胃癌转移淋巴结病理学诊断中的临床应用价值。 方法： 选取2016年7月至2018年12月在青岛大学附属医院因进展期胃癌接受根治性胃癌切除术+D2淋巴结清扫术的患者124例，按照入院时间顺序前80例作为训练组，后44例作为测试组。训练组男性45例，女性35例，平均年龄57.6岁；测试组男性29例，女性15例，平均年龄59.2岁。利用训练组中21 352个带有癌区的图像区块和14 997个没有癌区的区块对预训练的卷积神经网络架构Resnet50进行训练和微调，建立卷积神经网络计算机辅助系统（CNN-CAD）。对测试组患者的38张转移淋巴结和40张非转移淋巴结的全扫描切片进行测试，运用设定阈值的方法分析CNN-CAD系统在整体切片水平筛选转移淋巴结的能力，运用受试者工作特征曲线评估CNN-CAD系统转移区块级别识别的灵敏度、特异度、阳性预测值、阴性预测值，并计算曲线下面积。 结果： CNN-CAD系统在切片水平分类准确性为100%，识别区块级别转移正确率为0.989，灵敏度为0.778，特异度为0.995，阳性预测值为0.822，阴性预测值为0.994，曲线下面积为0.89，结果与病理科医师相当。 结论： 课题组构建的CNN-CAD系统对胃癌转移淋巴结病理学切片具有较高的分类能力，可以辅助病理科医师对转移淋巴结切片进行初步筛选。.
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