探讨基于超声内镜(EUS)深度学习、影像组学及其联合模型在鉴别胃间质瘤(GIST)与平滑肌瘤中的应用价值。

回顾性分析2010年1月至2025年4月解放军总医院第一医学中心895例患者(共4 802张EUS图像)的临床及影像资料，以病变为单位按8∶2比例随机划分为训练集与测试集。分别提取EUS图像的影像组学特征与基于Transformer架构的细粒度深度学习特征，构建单一模态及联合模态的机器学习分类模型(支持向量机、极端梯度提升、随机森林)。通过受试者操作特征(ROC)曲线、DeLong检验、校准曲线及决策曲线分析综合评估模型性能与临床效用。

在测试集中，深度学习模型与联合模型的诊断效能均显著优于传统影像组学模型，其中支持向量机的联合模型效能最佳，曲线下面积(AUC)、准确度、灵敏度、特异度分别为0.852、0.792、0.831、0.707。

基于EUS的深度学习特征是鉴别胃GIST与平滑肌瘤的核心驱动力，其诊断效能显著优于传统影像组学特征。

To investigate the value of endoscopic ultrasound (EUS)-based deep learning, radiomics, and their combined model in differentiating gastric gastrointestinal stromal tumors (GIST) from leiomyomas.

Clinical and imaging data from 895 patients (totaling 4, 802 EUS images) at the First Medical Center of the Chinese PLA General Hospital between January 2010 and April 2025 were retrospectively analyzed.Cases were randomly divided into training and test sets at an 8∶2 ratio based on lesions.Radiomic features and fine-grained deep learning features based on the Transformer architecture were extracted from EUS images to construct single-modality and combined-modality machine learning classification models (support vector machine, extreme gradient boosting, random forest).Model performance and clinical utility were comprehensively evaluated using receiver operating characteristic (ROC) curves, the DeLong test, calibration curves, and decision curve analysis.

In the test set, both the deep learning model and the combined model demonstrated significantly better diagnostic performance than the traditional radiomics model. Among these, the combined model using the support vector machine achieved the best performance, with an area under the curve (AUC), accuracy, sensitivity, and specificity of 0.852, 0.792, 0.831, and 0.707, respectively.

EUS-based deep learning features serve as the primary driver for differentiating gastric GIST from leiomyomas, with diagnostic performance significantly superior to that of traditional radiomic features.