Deep learning classification of uveal melanoma based on histopathological images and identification of a novel indicator for prognosis of patients

Biological Procedures Online

Research

Shali Yue¹  Ming Zhang¹  Lixiang Wang¹  Ke Ma¹  Ying-ping Deng¹  Ran Wei¹  Jing Tang¹  Qi Wan¹  Hongbo Yin¹  Xiang Ren¹ 

[1] Department of Ophthalmology, West China Hospital, Sichuan University, Chengdu City, Sichuan Province, China;

关键词: Deep learning;    Histopathological images;    Prognosis;    Uveal melanoma;    Subtype;   

DOI  :  10.1186/s12575-023-00207-0

 received in 2023-03-07, accepted in 2023-05-15,  发布年份 2023

来源: Springer

【 摘 要 】

BackgroundDeep learning has been extensively used in digital histopathology. The purpose of this study was to test deep learning (DL) algorithms for predicting the vital status of whole-slide image (WSI) of uveal melanoma (UM).MethodsWe developed a deep learning model (Google-net) to predict the vital status of UM patients from histopathological images in TCGA-UVM cohort and validated it in an internal cohort. The histopathological DL features extracted from the model and then were applied to classify UM patients into two subtypes. The differences between two subtypes in clinical outcomes, tumor mutation, and microenvironment, and probability of drug therapeutic response were investigated further.ResultsWe observed that the developed DL model can achieve a high accuracy of >  = 90% for patches and WSIs prediction. Using 14 histopathological DL features, we successfully classified UM patients into Cluster1 and Cluster2 subtypes. Compared to Cluster2, patients in the Cluster1 subtype have a poor survival outcome, increased expression levels of immune-checkpoint genes, higher immune-infiltration of CD8 + T cell and CD4 + T cells, and more sensitivity to anti-PD-1 therapy. Besides, we established and verified prognostic histopathological DL-signature and gene-signature which outperformed the traditional clinical features. Finally, a well-performed nomogram combining the DL-signature and gene-signature was constructed to predict the mortality of UM patients.ConclusionsOur findings suggest that DL model can accurately predict vital status in UM patents just using histopathological images. We found out two subgroups based on histopathological DL features, which may in favor of immunotherapy and chemotherapy. Finally, a well-performing nomogram that combines DL-signature and gene-signature was constructed to give a more straightforward and reliable prognosis for UM patients in treatment and management.

【 授权许可】

CC BY   
© The Author(s) 2023

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