Wan Qi, Wei Ran, Yin Hongbo, Tang Jing, Deng Ying-Ping, Ma Ke
Department of Ophthalmology, West China Hospital of Sichuan University, Chengdu, Sichuan, China.
Front Immunol. 2025 Jul 9;16:1585097. doi: 10.3389/fimmu.2025.1585097. eCollection 2025.
Uveal melanoma (UVM) is the most common primary intraocular malignancy in adults, characterized by high mortality despite its relatively low incidence. This study aimed to utilize unsupervised learning techniques to identify a high immune infiltration subtype of UVM and improve patient stratification based on mortality risk.
A total of 70 hematoxylin and eosin (H&E) stained whole-slide images (WSIs) of UVM were collected from the Genomic Data Commons (GDC) data portal, along with genomic and clinical data. An additional validation cohort of 68 UVM patients from West China Hospital was included. Pathomic features were extracted using CellProfiler software, and deep learning models were constructed for classification and survival prediction. Unsupervised clustering was performed to identify critical regions for prognosis prediction and patient classification. The relationship between histopathological features and genomics was explored.
The study achieved accurate prediction and classification of UVM patients using deep learning models and machine learning techniques. A high immune infiltration subtype of UVM was identified, which showed prognostic relevance. Unsupervised clustering categorized UVM patients into three distinct subgroups. The developed deep learning model based on the Inception-V3 architecture demonstrated promising results in survival prediction.
This study demonstrates the potential of unsupervised learning and deep learning techniques in identifying a high immune infiltration subtype of UVM and improving patient stratification based on mortality risk. This research contributes to the field of computational pathology and highlights the potential of utilizing histopathological images, genomic data, and deep learning models in enhancing the management of UVM patients.
葡萄膜黑色素瘤（UVM）是成人中最常见的原发性眼内恶性肿瘤，尽管其发病率相对较低，但死亡率很高。本研究旨在利用无监督学习技术识别UVM的高免疫浸润亚型，并根据死亡风险改善患者分层。
从基因组数据共享库（GDC）数据门户收集了70张UVM苏木精和伊红（H&E）染色的全切片图像（WSIs），以及基因组和临床数据。纳入了来自华西医院的68例UVM患者的额外验证队列。使用CellProfiler软件提取病理特征，并构建深度学习模型进行分类和生存预测。进行无监督聚类以识别预后预测和患者分类的关键区域。探索了组织病理学特征与基因组学之间的关系。
该研究使用深度学习模型和机器学习技术实现了对UVM患者的准确预测和分类。识别出了UVM的高免疫浸润亚型，其具有预后相关性。无监督聚类将UVM患者分为三个不同的亚组。基于Inception-V3架构开发的深度学习模型在生存预测方面显示出有前景的结果。
本研究证明了无监督学习和深度学习技术在识别UVM的高免疫浸润亚型以及基于死亡风险改善患者分层方面的潜力。这项研究为计算病理学领域做出了贡献，并突出了利用组织病理学图像、基因组数据和深度学习模型在加强UVM患者管理方面的潜力。
