ArticleScientific reports2021
Machine learning based differentiation of glioblastoma from brain metastasis using MRI derived radiomics.
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 27 papers, 1 of them a synthesis that pooled it.
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Who cites it
27 citing papers in PubMed, 1 synthesis or guideline pooled it, 40 citations in OpenAlex.
- Predicting IDH and ATRX mutations in gliomas from radiomic features with machine learning: a systematic review and meta-analysis.Frontiers in radiology · 2024Pooled it
- Chronic osteomyelitis diagnosis using MRI: optimising radiomics models through multi-level region of interest extensions.Frontiers in bioengineering and biotechnology · 2026Article
- Electron Density and Effective Atomic Number as Quantitative Biomarkers for Differentiating Malignant Brain Tumors: An Exploratory Study with Machine Learning.Tomography (Ann Arbor, Mich.) · 2025Article
- Hybrid Radiomics and Machine Learning for Brain Tumors Multi-Task Classification: An Exploratory Study on Integrating GLCM and Curvelet-Based Features for Enhanced Accuracy.Health science reports · 2025Article
- Multiple deep learning models based on MRI images in discriminating glioblastoma from solitary brain metastases: a multicentre study.BMC medical imaging · 2025Article
- Radiomics-Based Differentiation of Glioblastoma and Metastatic Disease: Impact of Different T1-Contrast-Enhanced Sequences on Radiomics Features and Model Performance.AJNR. American journal of neuroradiology · 2025Article
- Artificial intelligence in the task of segmentation and classification of brain metastases images: current challenges and future opportunities.Frontiers in neurology · 2025Review
- Prediction of prognosis in glioblastoma with radiomics features extracted by synthetic MRI images using cycle-consistent GAN.Physical and engineering sciences in medicine · 2024Article
- Optimizing diagnosis and surgical decisions for chronic osteomyelitis through radiomics in the precision medicine era.Frontiers in bioengineering and biotechnology · 2024Article
- An Explainable MRI-Radiomic Quantum Neural Network to Differentiate Between Large Brain Metastases and High-Grade Glioma Using Quantum Annealing for Feature Selection.Journal of digital imaging · 2023Article
- Magnetic resonance-based imaging biopsy with signatures including topological Betti number features for prediction of primary brain metastatic sites.Physical and engineering sciences in medicine · 2023Article
- One Step Forward-The Current Role of Artificial Intelligence in Glioblastoma Imaging.Life (Basel, Switzerland) · 2023Review
- Radiomics characterization of tissues in an animal brain tumor model imaged using dynamic contrast enhanced (DCE) MRI.Scientific reports · 2023Article
- Biomaterial-basedCancer pathogenesis and therapy · 2023Review
- Evaluating Autoencoders for Dimensionality Reduction of MRI-derived Radiomics and Classification of Malignant Brain Tumors.Scientific and statistical database management : International Conference, SSDBM ... : proceedings. International Conference on Scientific and Statistical Database Management · 2023Article
- A brain tumor computer-aided diagnosis method with automatic lesion segmentation and ensemble decision strategy.Frontiers in medicine · 2023Article
- Whole-brain dynamical modelling for classification of Parkinson's disease.Brain communications · 2023Article
- Differentiation of Intracerebral Tumor Entities with Quantitative Contrast Attenuation and Iodine Mapping in Dual-Layer Computed Tomography.Diagnostics (Basel, Switzerland) · 2022Article
- Machine learning for the micropeptide encoded by LINC02381 regulates ferroptosis through the glucose transporter SLC2A10 in glioblastoma.BMC cancer · 2022Article
- A Systematic Review of the Current Status and Quality of Radiomics for Glioma Differential Diagnosis.Cancers · 2022Review
Corrections and comments
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Authors and funding
10 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Few studies have addressed radiomics based differentiation of Glioblastoma (GBM) and intracranial metastatic disease (IMD). However, the effect of different tumor masks, comparison of single versus multiparametric MRI (mp-MRI) or select combination of sequences remains undefined. We cross-compared multiple radiomics based machine learning (ML) models using mp-MRI to determine optimized configurations. Our retrospective study included 60 GBM and 60 IMD patients. Forty-five combinations of ML models and feature reduction strategies were assessed for features extracted from whole tumor and edema masks using mp-MRI [T1W, T2W, T1-contrast enhanced (T1-CE), ADC, FLAIR], individual MRI sequences and combined T1-CE and FLAIR sequences. Model performance was assessed using receiver operating characteristic curve. For mp-MRI, the best model was LASSO model fit using full feature set (AUC 0.953). FLAIR was the best individual sequence (LASSO-full feature set, AUC 0.951). For combined T1-CE/FLAIR sequence, adaBoost-full feature set was the best performer (AUC 0.951). No significant difference was seen between top models across all scenarios, including models using FLAIR only, mp-MRI and combined T1-CE/FLAIR sequence. Top features were extracted from both the whole tumor and edema masks. Shape sphericity is an important discriminating feature.
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