ArticleFrontiers in bioengineering and biotechnology2022
Combining Radiology and Pathology for Automatic Glioma Classification.
Article in Frontiers in bioengineering and biotechnology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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14 citing papers in PubMed, 23 citations in OpenAlex.
- Oral squamous cell carcinoma diagnosis: an interpretable deep learning approach using multi-scale pathological images.BMC oral health · 2026Article
- Multimodal fusion of pathology and radiology foundation models for WHO 2021 glioma subtyping.NPJ precision oncology · 2026Article
- Investigation of Intraoperative and Permanent Diagnostic Consistency in Glial Tumors Considering Rater and Technical Variability.Medicina (Kaunas, Lithuania) · 2025Article
- Enhancing Interstitial Lung Disease Diagnoses Through Multimodal AI Integration of Histopathological and CT Image Data.Respirology (Carlton, Vic.) · 2025Article
- Prediction of Reactivation After Antivascular Endothelial Growth Factor Monotherapy for Retinopathy of Prematurity: Multimodal Machine Learning Model Study.Journal of medical Internet research · 2025Article
- Utilizing the amide proton transfer technique to characterize diffuse gliomas based on the WHO 2021 classification of CNS tumors.The neuroradiology journal · 2024Article
- Applications of artificial intelligence in the analysis of histopathology images of gliomas: a review.Npj imaging · 2024Review
- Article
- Advances in the Use of Deep Learning for the Analysis of Magnetic Resonance Image in Neuro-Oncology.Cancers · 2024Review
- Efficient diagnosis of IDH-mutant gliomas: 1p/19qNET assesses 1p/19q codeletion status using weakly-supervised learning.NPJ precision oncology · 2023Article
- Performance of deep learning algorithms to distinguish high-grade glioma from low-grade glioma: A systematic review and meta-analysis.iScience · 2023Article
- Multimodal data fusion for cancer biomarker discovery with deep learning.Nature machine intelligence · 2023Article
- The Pathological Activation of Microglia Is Modulated by Sexually Dimorphic Pathways.International journal of molecular sciences · 2023Review
- Multimodal analysis methods in predictive biomedicine.Computational and structural biotechnology journal · 2023Review
Corrections and comments
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Authors and funding
8 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Subtype classification is critical in the treatment of gliomas because different subtypes lead to different treatment options and postoperative care. Although many radiological- or histological-based glioma classification algorithms have been developed, most of them focus on single-modality data. In this paper, we propose an innovative two-stage model to classify gliomas into three subtypes (i.e., glioblastoma, oligodendroglioma, and astrocytoma) based on radiology and histology data. In the first stage, our model classifies each image as having glioblastoma or not. Based on the obtained non-glioblastoma images, the second stage aims to accurately distinguish astrocytoma and oligodendroglioma. The radiological images and histological images pass through the two-stage design with 3D and 2D models, respectively. Then, an ensemble classification network is designed to automatically integrate the features of the two modalities. We have verified our method by participating in the MICCAI 2020 CPM-RadPath Challenge and won 1st place. Our proposed model achieves high performance on the validation set with a balanced accuracy of 0.889, Cohen's Kappa of 0.903, and an F1-score of 0.943. Our model could advance multimodal-based glioma research and provide assistance to pathologists and neurologists in diagnosing glioma subtypes. The code has been publicly available online at https://github.com/Xiyue-Wang/1st-in-MICCAI2020-CPM.
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