ORIGINAL RESEARCH article
Front. Immunol.
Sec. Cancer Immunity and Immunotherapy
Volume 16 - 2025 | doi: 10.3389/fimmu.2025.1585097
This article is part of the Research TopicPrecision Oncology in Checkpoint Immunotherapy: Leveraging Predictive Biomarkers for Personalized TreatmentView all 17 articles
Integrating Pathomics and Deep Learning for Subtyping Uveal Melanoma: Identifying High-Risk Immune Infiltration Profiles
Provisionally accepted
- West China Hospital, Sichuan University, Chengdu, Sichuan Province, China
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Purpose: 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. Methods: 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.Results: 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.Conclusion: 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.
Keywords: unsupervised learning, Uveal Melanoma, Immune infiltration, Pathomics features, deep learning
Received: 28 Feb 2025; Accepted: 24 Jun 2025.
Copyright: © 2025 Wan, WEI, Yin, Tang, Deng and Ma. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Qi Wan, West China Hospital, Sichuan University, Chengdu, Sichuan Province, China
Ke Ma, West China Hospital, Sichuan University, Chengdu, Sichuan Province, China
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