AutoLNMNet：利用金字塔视觉变换器和多光子显微镜数据的自动淋巴结转移估算网络

AutoLNMNet: Automated Network for Estimating Lymph-Node Metastasis in EGC Using a Pyramid Vision Transformer and Data Derived From Multiphoton Microscopy

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MICROSCOPY RESEARCH AND TECHNIQUE

影响因子:2.1

分区:工程技术3区 / 显微镜技术2区解剖学与形态学3区生物学4区

发表日期:2025 Jan

作者: Lin Gao, Wenju Liu, Bingzi Kang, Han Wu, Jiajia He, Xiaolu Li, Gangqin Xi, Shuangmu Zhuo

DOI: 10.1002/jemt.24705

摘要

淋巴结状态在早期胃癌（EGC）治疗中的决策制定中具有重要意义。目前，内镜黏膜下剥离术是EGC的主要治疗方式，但即使是经验丰富的内镜医师也难以准确诊断和治疗EGC。多光子显微镜能够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态。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上的准确性。然后，将表现最佳的模型——PVTv2的特征与手动及临床特征结合，开发出一种新型模型AutoLNMNet。AutoLNMNet对无转移（Ly0）和淋巴结转移（Ly1）阶段的预测准确率达到0.92，比PVTv2高0.3%。AutoLNMNet在区分Ly0和Ly1阶段的受试者工作特征曲线（ROC）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，为早期诊断和治疗EGC提供了重要工具。

Abstract

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.