ArticleBMC medical informatics and decision making2020
Machine learning assisted DSC-MRI radiomics as a tool for glioma classification by grade and mutation status.
Article in BMC medical informatics and decision making, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers, 3 of them syntheses that pooled it.
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Who cites it
37 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Diagnostic accuracy of a machine learning-based radiomics approach of MR in predicting IDH mutations in glioma patients: a systematic review and meta-analysis.Frontiers in oncology · 2024Pooled it
- Accuracy of Radiomics in PredictingRadiology. Artificial intelligence · 2024Pooled it
- The role of DSC MR perfusion in predicting IDH mutation and 1p19q codeletion status in gliomas: meta-analysis and technical considerations.Neuroradiology · 2023Pooled it
- Radiomics in glioblastoma recurrence: advances in prediction, localization, and differentiation from treatment-related effects.Journal of translational medicine · 2026Review
- Review
- ASLNet: an explainable deep learning framework for glioma grading and survival prediction.Frontiers in oncology · 2026Article
- Radiomics-based machine learning for glioma grade classification: a multicenter study with SHapley Additive exPlanations interpretability analysis.Translational cancer research · 2025Article
- Artificial Intelligence in the Diagnosis and Treatment of Brain Gliomas.Biomedicines · 2025Review
- Multi-site retrospective analysis of diffusion and perfusion magnetic resonance imaging correlates to glioma characteristics derived from radio-pathomic maps.Neuro-oncology · 2025Article
- A Combined Approach Using T2*-Weighted Dynamic Susceptibility Contrast MRI Perfusion Parameters and Radiomics to Differentiate Between Radionecrosis and Glioma Progression: A Proof-of-Concept Study.Life (Basel, Switzerland) · 2025Article
- Brain tumor intelligent diagnosis based on Auto-Encoder and U-Net feature extraction.PloS one · 2025Article
- Customized SAM-Med3D with multi-view adapter and T2-FLAIR mismatch features for glioma IDH genotyping and grading.Frontiers in behavioral neuroscience · 2025Article
- [An MRI multi-sequence feature imputation and fusion mutual-aid model based on sequence deletion for differentiation of high-grade from low-grade glioma].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2024Article
- Structural- and DTI- MRI enable automated prediction of IDH Mutation Status in CNS WHO Grade 2-4 glioma patients: a deep Radiomics Approach.BMC medical imaging · 2024Article
- Predicting the Response of Patients Treated withJournal of medical signals and sensors · 2024Article
- Coordinatized lesion location analysis empowering ROI-based radiomics diagnosis on brain gliomas.European radiology · 2023Article
- Radiomics and Machine Learning in Brain Tumors and Their Habitat: A Systematic Review.Cancers · 2023Review
- Whole-tumor histogram analysis of diffusion and perfusion metrics for noninvasive pediatric glioma grading.Neuroradiology · 2023Article
- The peritumoral brain zone in glioblastoma: where we are and where we are going.Journal of neuroscience research · 2023Review
- An image-based modeling framework for predicting spatiotemporal brain cancer biology within individual patients.Frontiers in oncology · 2023Article
Corrections and comments
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Authors and funding
29 authors.
Funding
Abstract
backgroundCombining MRI techniques with machine learning methodology is rapidly gaining attention as a promising method for staging of brain gliomas. This study assesses the diagnostic value of such a framework applied to dynamic susceptibility contrast (DSC)-MRI in classifying treatment-naïve gliomas from a multi-center patients into WHO grades II-IV and across their isocitrate dehydrogenase (IDH) mutation status.
methodsThree hundred thirty-three patients from 6 tertiary centres, diagnosed histologically and molecularly with primary gliomas (IDH-mutant = 151 or IDH-wildtype = 182) were retrospectively identified. Raw DSC-MRI data was post-processed for normalised leakage-corrected relative cerebral blood volume (rCBV) maps. Shape, intensity distribution (histogram) and rotational invariant Haralick texture features over the tumour mask were extracted. Differences in extracted features across glioma grades and mutation status were tested using the Wilcoxon two-sample test. A random-forest algorithm was employed (2-fold cross-validation, 250 repeats) to predict grades or mutation status using the extracted features.
resultsShape, distribution and texture features showed significant differences across mutation status. WHO grade II-III differentiation was mostly driven by shape features while texture and intensity feature were more relevant for the III-IV separation. Increased number of features became significant when differentiating grades further apart from one another. Gliomas were correctly stratified by mutation status in 71% and by grade in 53% of the cases (87% of the gliomas grades predicted with distance less than 1).
conclusionsDespite large heterogeneity in the multi-center dataset, machine learning assisted DSC-MRI radiomics hold potential to address the inherent variability and presents a promising approach for non-invasive glioma molecular subtyping and grading.
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