ArticleScientific reports2021
Predicting prognosis and IDH mutation status for patients with lower-grade gliomas using whole slide images.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 papers, 2 of them syntheses that pooled it.
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Who cites it
48 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- The Performance of Artificial Intelligence in Classifying Molecular Markers in Adult-Type Gliomas Using Histopathological Images: Systematic Review.Journal of medical Internet research · 2026Pooled it
- A Systematic Review on Machine Learning Techniques for Survival Analysis in Cancer.Cancer medicine · 2025Pooled it
- Contrastive multimodal deep learning for survival prediction in grade 2/3 gliomas.JNCI cancer spectrum · 2026Article
- Deep learning framework for predicting EGFR mutation status from H&E whole slide images in lung adenocarcinoma.BMC cancer · 2026Article
- Improving Colorectal Cancer Screening and Risk Assessment through Predictive Modeling on Medical Images and Records.The American journal of pathology · 2026Article
- AI-Powered Histology for Molecular Profiling in Brain Tumors: Toward Smart Diagnostics from Tissue.Cancers · 2025Review
- Deep learning for digital pathology: A critical overview of methodological framework.Journal of pathology informatics · 2025Review
- Estimating Health State Utilities for IDH-Mutant Diffuse Glioma.PharmacoEconomics - open · 2025Article
- Review
- Article
- The outcome prediction method of football matches by the quantum neural network based on deep learning.Scientific reports · 2025Article
- α-synuclein expression in glioblastoma restores tumor suppressor function and rescues temozolomide drug resistance.Cell death & disease · 2025Article
- Epidemiology and Outcome of Primary Central Nervous System Tumors Treated at King Hussein Cancer Center.Cancers · 2025Article
- Weakly supervised deep learning-based classification for histopathology of gliomas: a single center experience.Scientific reports · 2025Article
- Deep learning and pathomics analyses predict prognosis of high-grade gliomas.Frontiers in neurology · 2025Article
- Masked pre-training of transformers for histology image analysis.Journal of pathology informatics · 2024Article
- Digital profiling of gene expression from histology images with linearized attention.Nature communications · 2024Article
- Completely non-invasive prediction of IDH mutation status based on preoperative native CT images.Scientific reports · 2024Article
- Deep learning-based IDH1 gene mutation prediction using histopathological imaging and clinical data.Computers in biology and medicine · 2024Article
- Applications of artificial intelligence in the analysis of histopathology images of gliomas: a review.Npj imaging · 2024Review
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3 authors.
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Abstract
We developed end-to-end deep learning models using whole slide images of adults diagnosed with diffusely infiltrating, World Health Organization (WHO) grade 2 gliomas to predict prognosis and the mutation status of a somatic biomarker, isocitrate dehydrogenase (IDH) 1/2. The models, which utilize ResNet-18 as a backbone, were developed and validated on 296 patients from The Cancer Genome Atlas (TCGA) database. To account for the small sample size, repeated random train/test splits were performed for hyperparameter tuning, and the out-of-sample predictions were pooled for evaluation. Our models achieved a concordance- (C-) index of 0.715 (95% CI: 0.569, 0.830) for predicting prognosis and an area under the curve (AUC) of 0.667 (0.532, 0.784) for predicting IDH mutations. When combined with additional clinical information, the performance metrics increased to 0.784 (95% CI: 0.655, 0.880) and 0.739 (95% CI: 0.613, 0.856), respectively. When evaluated on the WHO grade 3 gliomas from the TCGA dataset, which were not used for training, our models predicted survival with a C-index of 0.654 (95% CI: 0.537, 0.768) and IDH mutations with an AUC of 0.814 (95% CI: 0.721, 0.897). If validated in a prospective study, our method could potentially assist clinicians in managing and treating patients with diffusely infiltrating gliomas.
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