Background and Aims

According to guidelines, endoscopic resection should only be performed for patients whose early gastric cancer invasion depth is within the mucosa or submucosa of the stomach regardless of lymph node involvement. The accurate prediction of invasion depth based on endoscopic images is crucial for screening patients for endoscopic resection. We constructed a convolutional neural network computer-aided detection (CNN-CAD) system based on endoscopic images to determine invasion depth and screen patients for endoscopic resection.

Methods

Endoscopic images of gastric cancer tumors were obtained from the Endoscopy Center of Zhongshan Hospital. An artificial intelligence–based CNN-CAD system was developed through transfer learning leveraging a state-of-the-art pretrained CNN architecture, ResNet50. A total of 790 images served as a development dataset and another 203 images as a test dataset. We used the CNN-CAD system to determine the invasion depth of gastric cancer and evaluated the system’s classification accuracy by calculating its sensitivity, specificity, and area under the receiver operating characteristic curve.

Results

The area under the receiver operating characteristic curve for the CNN-CAD system was .94 (95% confidence interval [CI], .90-.97). At a threshold value of .5, sensitivity was 76.47%, and specificity 95.56%. Overall accuracy was 89.16%. Positive and negative predictive values were 89.66% and 88.97%, respectively. The CNN-CAD system achieved significantly higher accuracy (by 17.25%; 95% CI, 11.63-22.59) and specificity (by 32.21%; 95% CI, 26.78-37.44) than human endoscopists.

Conclusions

We constructed a CNN-CAD system to determine the invasion depth of gastric cancer with high accuracy and specificity. This system distinguished early gastric cancer from deeper submucosal invasion and minimized overestimation of invasion depth, which could reduce unnecessary gastrectomy.

中文翻译：

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卷积神经网络在常规内镜胃癌浸润深度诊断中的应用

背景和目标

根据指南，早期胃癌浸润深度在胃粘膜或粘膜下层以内的患者，无论淋巴结是否受累，均应行内镜下切除术。基于内窥镜图像准确预测浸润深度对于筛选患者进行内窥镜切除至关重要。我们构建了一个基于内窥镜图像的卷积神经网络计算机辅助检测 (CNN-CAD) 系统，以确定浸润深度并筛选患者进行内窥镜切除。

方法

胃癌肿瘤的内镜图像来自中山医院内镜中心。基于人工智能的 CNN-CAD 系统是通过迁移学习利用最先进的预训练 CNN 架构 ResNet50 开发的。共有 790 张图像作为开发数据集，另外 203 张图像作为测试数据集。我们使用CNN-CAD系统确定胃癌的浸润深度，并通过计算其敏感性、特异性和受试者工作特征曲线下的面积来评估系统的分类准确性。

结果

CNN-CAD 系统的受试者工作特征曲线下面积为 0.94（95% 置信区间 [CI]，0.90-0.97）。在 0.5 的阈值下，灵敏度为 76.47%，特异性为 95.56%。总体准确率为89.16%。阳性和阴性预测值分别为 89.66% 和 88.97%。与人类内窥镜医师相比，CNN-CAD 系统的准确性（17.25%；95% CI，11.63-22.59）和特异性（32.21%；95% CI，26.78-37.44）显着提高。

结论

我们构建了一个 CNN-CAD 系统来以高精度和特异性确定胃癌的浸润深度。该系统将早期胃癌与更深的粘膜下浸润区分开来，并最大限度地减少对浸润深度的高估，从而减少不必要的胃切除术。