淋巴结状态对于早期胃癌 (EGC) 治疗期间的决策非常重要。目前，内镜黏膜下剥离术是EGC的主流治疗方法。然而，即使是经验丰富的内窥镜医师也很难准确诊断和治疗 EGC。多光子显微镜可以提取组织中胶原纤维的形态特征。胶原纤维的特性可用于评估EGC患者的淋巴结转移状况。首先，我们比较了四种深度学习模型（VGG16、ResNet34、MobileNetV2 和 PVTv2）在训练预处理图像和测试数据集时的准确性。接下来，我们将性能最好的模型 PVTv2 的特征与手动和临床特征相结合，开发了一种名为 AutoLNMNet 的新颖模型。 AutoLNMNet对无转移（Ly0）和淋巴结转移（Ly1）分期的预测准确率达到0.92，比PVTv2提高0.3%。 AutoLNMNet 在量化 Ly0 和 Ly1 阶段的接收器操作特性分别为 0.97 和 0.97。因此，AutoLNMNet 在检测淋巴结转移方面具有高度可靠性和准确性，为 EGC 的早期诊断和治疗提供了重要工具。© 2024 Wiley periodicals LLC。

Lymph-node status is important in decision-making during early gastric cancer (EGC) treatment. Currently, endoscopic submucosal dissection is the mainstream treatment for EGC. However, it is challenging for even experienced endoscopists to accurately diagnose and treat EGC. Multiphoton microscopy can extract the morphological features of collagen fibers from tissues. The characteristics of collagen fibers can be used to assess the lymph-node metastasis status in patients with EGC. First, we compared the accuracy of four deep learning models (VGG16, ResNet34, MobileNetV2, and PVTv2) in training preprocessed images and test datasets. Next, we integrated the features of the best-performing model, which was PVTv2, with manual and clinical features to develop a novel model called AutoLNMNet. The prediction accuracy of AutoLNMNet for the no metastasis (Ly0) and metastasis in lymph nodes (Ly1) stages reached 0.92, which was 0.3% higher than that of PVTv2. The receiver operating characteristics of AutoLNMNet in quantifying Ly0 and Ly1 stages were 0.97 and 0.97, respectively. Therefore, AutoLNMNet is highly reliable and accurate in detecting lymph-node metastasis, providing an important tool for the early diagnosis and treatment of EGC.© 2024 Wiley Periodicals LLC.