ArticleNeuro-oncology2023
Combined molecular subtyping, grading, and segmentation of glioma using multi-task deep learning.
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99 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Deep Learning Models for Radiomics-Based Segmentation of Vestibular Schwannoma on Magnetic Resonance Imaging: A Systematic Review and Meta-analysis.Journal of imaging informatics in medicine · 2026Pooled it
- Diagnostic performance of deep learning for predicting glioma isocitrate dehydrogenase and 1p/19q co-deletion in MRI: a systematic review and meta-analysis.European radiology · 2026Pooled it
- Artificial intelligence and machine learning driven segmentation and quantification models for brain arteriovenous malformations: A systematic review.Neuroradiology · 2025Pooled it
- Radiomics and Machine Learning in Diagnostics of Glial Brain Tumors: a Systematic Review and Meta-Analysis.Sovremennye tekhnologii v meditsine · 2025Pooled it
- Robust deep learning for incomplete MRI sequences in glioma grading and IDH mutation status prediction: a large-scale multicenter study.European radiology · 2026Article
- Deep learning-based segmentation, grading and genotype classification of glioma: a literature analysis.Quantitative imaging in medicine and surgery · 2026Review
- Classification of histology and molecular subtypes of brain gliomas and glioneuronal and neuronal tumors using a deep learning approach.European radiology · 2026Article
- Automatic Segmentation and Classification of Glioblastoma and Solitary Brain Metastasis Using a Deep Learning Model on Multiparametric MRI.Journal of imaging informatics in medicine · 2026Article
- Chinese expert concern and consensus on applications of artificial intelligence in clinical cancer imaging.Insights into imaging · 2026Article
- A perioperative multi-modal fusion and deep learning-based prognostic system for upper tract urothelial carcinoma: a multi-institutional study.Insights into imaging · 2026Article
- Advancing precision risk stratification in adult diffuse gliomas through DSC-MRI-based habitat analysis.BMC medical imaging · 2026Article
- A transformer-based multimodal deep learning model for preoperative prediction of Ki-67 expression level in glioma.BMC medical imaging · 2026Article
- A comprehensive multi-task deep learning model for kidney cancer: histological subtyping, clinical staging, and anatomical complexity grading.European radiology · 2026Article
- A robust vision language model for molecular status prediction and radiology report generation in adult-type diffuse gliomas.NPJ digital medicine · 2026Article
- Multimodal fusion of pathology and radiology foundation models for WHO 2021 glioma subtyping.NPJ precision oncology · 2026Article
- 2.5 dimensional multimodal MRI improves convolutional neural networks performance in predicting the IDH mutation status of adult diffuse gliomas.Chinese medical journal · 2026Article
- Article
- Radiomics-Driven Hybrid Deep Learning for MRI-Based Prediction of Glioma Grade and 1p/19q Codeletion.Tomography (Ann Arbor, Mich.) · 2026Article
- The diagnostic value of enhanced CT radiomics and deep learning in differentiating pediatric peripheral neuroblastoma from ganglioneuroblastoma.BMC cancer · 2026Article
- Foundation Model Based on Routine Magnetic Resonance Imaging for Brain Tumor Molecular Profiling and Progression Prediction.JCO precision oncology · 2026Article
39 more citing papers are in PubMed but not listed here.
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17 authors.
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Abstract
backgroundAccurate characterization of glioma is crucial for clinical decision making. A delineation of the tumor is also desirable in the initial decision stages but is time-consuming. Previously, deep learning methods have been developed that can either non-invasively predict the genetic or histological features of glioma, or that can automatically delineate the tumor, but not both tasks at the same time. Here, we present our method that can predict the molecular subtype and grade, while simultaneously providing a delineation of the tumor.
methodsWe developed a single multi-task convolutional neural network that uses the full 3D, structural, preoperative MRI scans to predict the IDH mutation status, the 1p/19q co-deletion status, and the grade of a tumor, while simultaneously segmenting the tumor. We trained our method using a patient cohort containing 1508 glioma patients from 16 institutes. We tested our method on an independent dataset of 240 patients from 13 different institutes.
resultsIn the independent test set, we achieved an IDH-AUC of 0.90, an 1p/19q co-deletion AUC of 0.85, and a grade AUC of 0.81 (grade II/III/IV). For the tumor delineation, we achieved a mean whole tumor Dice score of 0.84.
conclusionsWe developed a method that non-invasively predicts multiple, clinically relevant features of glioma. Evaluation in an independent dataset shows that the method achieves a high performance and that it generalizes well to the broader clinical population. This first-of-its-kind method opens the door to more generalizable, instead of hyper-specialized, AI methods.
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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.