ArticleNeuro-oncology advances
Prognostic risk stratification of gliomas using deep learning in digital pathology images.
Article in Neuro-oncology advances. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed, 22 citations in OpenAlex.
- Survival risk prediction for patients with lung adenocarcinoma containing micropapillary components based on weakly supervised pathomics.Journal of thoracic disease · 2026Article
- Crosstalk in the brain tumor microenvironment: mechanisms, therapeutic strategies, and clinical advances.Military Medical Research · 2026Review
- 3D spatial sampling to quantify morphologic heterogeneity in isocitrate dehydrogenase-wildtype glioblastoma.Journal of neuropathology and experimental neurology · 2025Article
- Multimodal Explainable Artificial Intelligence for Prognostic Stratification of Patients With Glioblastoma.Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc · 2025Article
- IDH-mutant glioma risk stratification via whole slide images: Identifying pathological feature associations.iScience · 2025Article
- Deep learning and pathomics analyses predict prognosis of high-grade gliomas.Frontiers in neurology · 2025Article
- Review
- Applications of artificial intelligence in the analysis of histopathology images of gliomas: a review.Npj imaging · 2024Review
- Machine learning-based pathomics signature of histology slides as a novel prognostic indicator in primary central nervous system lymphoma.Journal of neuro-oncology · 2024Article
- Review
- A multicenter proof-of-concept study on deep learning-based intraoperative discrimination of primary central nervous system lymphoma.Nature communications · 2024Article
- Deep learning links localized digital pathology phenotypes with transcriptional subtype and patient outcome in glioblastoma.GigaScience · 2024Article
- Translation of tissue-based artificial intelligence into clinical practice: from discovery to adoption.Oncogene · 2023Review
- A deep attention LSTM embedded aggregation network for multiple histopathological images.PloS one · 2023Article
- Survivorship from pediatric and adult brain tumors: The 2024 Brain Tumor Epidemiology Consortium meeting report.Neuro-oncology advancesArticle
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
Background: Evaluation of tumor-tissue images stained with hematoxylin and eosin (H&E) is pivotal in diagnosis, yet only a fraction of the rich phenotypic information is considered for clinical care. Here, we propose a survival deep learning (SDL) framework to extract this information to predict glioma survival. Methods: Digitized whole slide images were downloaded from The Cancer Genome Atlas (TCGA) for 766 diffuse glioma patients, including isocitrate dehydrogenase (IDH)-mutant/1p19q-codeleted oligodendroglioma, IDH-mutant/1p19q-intact astrocytoma, and IDH-wildtype astrocytoma/glioblastoma. Our SDL framework employs a residual convolutional neural network with a survival model to predict patient risk from H&E-stained whole-slide images. We used statistical sampling techniques and randomized the transformation of images to address challenges in learning from histology images. The SDL risk score was evaluated in traditional and recursive partitioning (RPA) survival models. Results: The SDL risk score demonstrated substantial univariate prognostic power (median concordance index of 0.79 [ Conclusions: The present study highlights the independent prognostic power of the SDL risk score for objective and accurate prediction of glioma outcomes. Further, we show that the RPA delineation of patient-specific risk scores and clinical prognostic factors can successfully demarcate the OS of glioma patients.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.