School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China.
Department of Oral Medicine, Hospital of Stomatology, Sun Yat-sen University, Guangzhou, China.
J Transl Med. 2024 Aug 8;22(1):748. doi: 10.1186/s12967-024-05550-8.
Sjögren's Syndrome (SS) is a rare chronic autoimmune disorder primarily affecting adult females, characterized by chronic inflammation and salivary and lacrimal gland dysfunction. It is often associated with systemic lupus erythematosus, rheumatoid arthritis and kidney disease, which can lead to increased mortality. Early diagnosis is critical, but traditional methods for diagnosing SS, mainly through histopathological evaluation of salivary gland tissue, have limitations.
The study used 100 labial gland biopsy, creating whole-slide images (WSIs) for analysis. The proposed model, named Cell-tissue-graph-based pathological image analysis model (CTG-PAM) and based on graph theory, characterizes single-cell feature, cell-cell feature, and cell-tissue feature. Building upon these features, CTG-PAM achieves cellular-level classification, enabling lymphocyte recognition. Furthermore, it leverages connected component analysis techniques in the cell graph structure to perform SS diagnosis based on lymphocyte counts.
CTG-PAM outperforms traditional deep learning methods in diagnosing SS. Its area under the receiver operating characteristic curve (AUC) is 1.0 for the internal validation dataset and 0.8035 for the external test dataset. This indicates high accuracy. The sensitivity of CTG-PAM for the external dataset is 98.21%, while the accuracy is 93.75%. In comparison, the sensitivity and accuracy for traditional deep learning methods (ResNet-50) are lower. The study also shows that CTG-PAM's diagnostic accuracy is closer to skilled pathologists compared to beginners.
Our findings indicate that CTG-PAM is a reliable method for diagnosing SS. Additionally, CTG-PAM shows promise in enhancing the prognosis of SS patients and holds significant potential for the differential diagnosis of both non-neoplastic and neoplastic diseases. The AI model potentially extends its application to diagnosing immune cells in tumor microenvironments.
干燥综合征（SS）是一种罕见的慢性自身免疫性疾病，主要影响成年女性，其特征是慢性炎症和唾液腺及泪腺功能障碍。它常与系统性红斑狼疮、类风湿关节炎和肾脏疾病相关，这可能导致死亡率增加。早期诊断至关重要，但 SS 的传统诊断方法主要通过唾液腺组织的组织病理学评估，存在局限性。
本研究使用了 100 例唇腺活检，创建了用于分析的全切片图像（WSI）。所提出的模型命名为基于细胞-组织-图的病理图像分析模型（CTG-PAM），它基于图论，对单细胞特征、细胞间特征和细胞组织特征进行了描述。基于这些特征，CTG-PAM 实现了细胞水平的分类，能够识别淋巴细胞。此外，它利用细胞图结构中的连通分量分析技术，根据淋巴细胞计数进行 SS 诊断。
CTG-PAM 在 SS 诊断中优于传统的深度学习方法。其内部验证数据集的受试者工作特征曲线下面积（AUC）为 1.0，外部测试数据集的 AUC 为 0.8035，表明具有较高的准确性。CTG-PAM 对外部数据集的敏感性为 98.21%，准确性为 93.75%。相比之下，传统深度学习方法（ResNet-50）的敏感性和准确性较低。研究还表明，与初学者相比，CTG-PAM 的诊断准确性更接近熟练的病理学家。
我们的研究结果表明，CTG-PAM 是一种可靠的 SS 诊断方法。此外，CTG-PAM 有望改善 SS 患者的预后，并在非肿瘤和肿瘤性疾病的鉴别诊断方面具有重要的应用潜力。该人工智能模型可能将其应用扩展到肿瘤微环境中免疫细胞的诊断。
