Ren Yong, Xia Wenqi, Wu Jiayun, Yang Zheng, Jiang Ye, Wen Ya, Guo Qiuquan, Gu Jieruo, Yang Jun, Luo Jun, Lv Qing
University of Electronic Science and Technology of China, Chengdu, 611731, China.
Department of Rheumatology, The Seventh Affiliated Hospital of Sun Yat-Sen University, Shenzhen, 518107, China.
Clin Rheumatol. 2025 Jun 5. doi: 10.1007/s10067-025-07518-5.
This study aimed to develop a deep learning-based model to predict the risk of high-risk extra-glandular organ involvement (HR-OI) in patients with Sjogren's syndrome (SS) using whole-slide images (WSI) from labial gland biopsies.
We collected WSI data from 221 SS patients. Pre-trained models, including ResNet50, InceptionV3, and EfficientNet-B5, were employed to extract image features. A classification model was constructed using multi-instance learning and ensemble learning techniques.
The ensemble model achieved high area under the receiver operating characteristic (ROC) curve values on both internal and external validation sets, indicating strong predictive performance. Moreover, the model was able to identify key pathological features associated with the risk of HR-OI.
This study demonstrates that a deep learning-based model can effectively predict the risk of HR-OI in SS patients, providing a novel basis for clinical decision-making. Key Points 1. What is already known on this topic? • Sjogren's syndrome (SS) is a chronic autoimmune disease affecting the salivary and lacrimal glands. • Accurate prediction of high-risk extra-glandular organ involvement (HR-OI) is crucial for timely intervention and improved patient outcomes in SS. • Traditional methods for HR-OI prediction rely on clinical data and lack objectivity. 2. What this study adds? • This study proposes a novel deep learning-based model using whole-slide images (WSI) from labial gland biopsies for predicting HR-OI in SS patients. • Our model utilizes pre-trained convolutional neural networks (CNNs) and a Vision Transformer (ViT) module to extract informative features from WSI data. • The ensemble model achieves high accuracy in predicting HR-OI, outperforming traditional methods. • The model can identify key pathological features in WSI data associated with HR-OI risk. 3. How this study might affect research, practice or policy? • This study provides a novel and objective approach for predicting HR-OI in SS patients, potentially leading to improved clinical decision-making and personalized treatment strategies. • Our findings encourage further investigation into the role of deep learning and WSI analysis in SS diagnosis and risk stratification. • The development of a non-invasive and objective diagnostic tool based on WSI analysis could benefit clinical practice and inform policy decisions regarding patient care for SS.The development of a non-invasive and objective diagnostic tool based on WSI analysis could benefit clinical practice and inform policy decisions regarding patient care for SS.
本研究旨在开发一种基于深度学习的模型，利用唇腺活检的全切片图像（WSI）预测干燥综合征（SS）患者发生高危腺外器官受累（HR-OI）的风险。
我们收集了221例SS患者的WSI数据。采用包括ResNet50、InceptionV3和EfficientNet-B5在内的预训练模型来提取图像特征。使用多实例学习和集成学习技术构建分类模型。
该集成模型在内部和外部验证集上均获得了较高的受试者操作特征（ROC）曲线下面积值，表明其具有较强的预测性能。此外，该模型能够识别与HR-OI风险相关的关键病理特征。
本研究表明，基于深度学习的模型可以有效预测SS患者发生HR-OI的风险，为临床决策提供了新的依据。要点1. 关于该主题已了解的内容有哪些？• 干燥综合征（SS）是一种影响唾液腺和泪腺的慢性自身免疫性疾病。• 准确预测高危腺外器官受累（HR-OI）对于SS患者的及时干预和改善预后至关重要。• 传统的HR-OI预测方法依赖临床数据，缺乏客观性。2. 本研究的新增内容是什么？• 本研究提出了一种基于深度学习的新型模型，利用唇腺活检的全切片图像（WSI）预测SS患者的HR-OI。• 我们的模型利用预训练的卷积神经网络（CNN）和视觉Transformer（ViT）模块从WSI数据中提取信息特征。• 该集成模型在预测HR-OI方面具有较高的准确性，优于传统方法。• 该模型可以识别WSI数据中与HR-OI风险相关的关键病理特征。3. 本研究可能如何影响研究、实践或政策？• 本研究为预测SS患者的HR-OI提供了一种新颖且客观的方法，可能会改善临床决策和个性化治疗策略。• 我们的研究结果鼓励进一步研究深度学习和WSI分析在SS诊断和风险分层中的作用。• 基于WSI分析开发一种非侵入性且客观的诊断工具可能会有益于临床实践，并为有关SS患者护理的政策决策提供参考。基于WSI分析开发一种非侵入性且客观的诊断工具可能会有益于临床实践，并为有关SS患者护理的政策决策提供参考。
