ArticleJournal of magnetic resonance imaging : JMRI2018
Characterization of active and infiltrative tumorous subregions from normal tissue in brain gliomas using multiparametric MRI.
Article in Journal of magnetic resonance imaging : JMRI, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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Who cites it
29 citing papers in PubMed.
- Article
- Supervoxel-based multimodal MRI biomarkers reveal tumor heterogeneity in high-grade glioma for prognostic stratification and therapy response prediction.NPJ precision oncology · 2026Article
- Advances in Artificial Intelligence for Glioblastoma Radiotherapy Planning and Treatment.Cancers · 2025Review
- Role of repulsive guidance signaling and GPR180 in pediatric low-grade glioma infiltration.NPJ precision oncology · 2025Article
- Inverse Problem Regularization for 3D Multi-Species Tumor Growth Models.International journal for numerical methods in biomedical engineering · 2025Article
- The radiogenomic and spatiogenomic landscapes of glioblastoma and their relationship to oncogenic drivers.Communications medicine · 2025Article
- Integrative Magnetic Resonance Imaging and Metabolomic Characterization of a Glioblastoma Rat Model.Brain sciences · 2024Article
- Differentiating tumour progression from pseudoprogression in glioblastoma patients: a monoexponential, biexponential, and stretched-exponential model-based DWI study.BMC medical imaging · 2023Article
- An initial study on the predictive value using multiple MRI characteristics for Ki-67 labeling index in glioma.Journal of translational medicine · 2023Article
- Clinical measures, radiomics, and genomics offer synergistic value in AI-based prediction of overall survival in patients with glioblastoma.Scientific reports · 2022Article
- The Concept of «Peritumoral Zone» in Diffuse Low-Grade Gliomas: Oncological and Functional Implications for a Connectome-Guided Therapeutic Attitude.Brain sciences · 2022Review
- A Magnetic Resonance-Relaxometry-Based Technique to Identify Blood Products in Brain Parenchyma: An Experimental Study on a Rabbit Model.Frontiers in veterinary science · 2022Article
- Decoding the Heterogeneity of Malignant Gliomas by PET and MRI for Spatial Habitat Analysis of Hypoxia, Perfusion, and Diffusion Imaging: A Preliminary Study.Frontiers in neuroscience · 2022Article
- Applications of Artificial Intelligence Based on Medical Imaging in Glioma: Current State and Future Challenges.Frontiers in oncology · 2022Review
- Construction of machine learning-based models for cancer outcomes in low and lower-middle income countries: A scoping review.Frontiers in oncology · 2022Article
- Machine Learning and Radiomic Features to Predict Overall Survival Time for Glioblastoma Patients.Journal of personalized medicine · 2021Article
- Applications of Radiomics and Radiogenomics in High-Grade Gliomas in the Era of Precision Medicine.Cancers · 2021Article
- Spatiotemporal habitats from multiparametric physiologic MRI distinguish tumor progression from treatment-related change in post-treatment glioblastoma.European radiology · 2021Article
- Artificial intelligence in tumor subregion analysis based on medical imaging: A review.Journal of applied clinical medical physics · 2021Review
- Characterization of hardware-related spatial distortions for IR-PETRA pulse sequence using a brain specific phantom.Magma (New York, N.Y.) · 2021Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
backgroundTargeted localized biopsies and treatments for diffuse gliomas rely on accurate identification of tissue subregions, for which current MRI techniques lack specificity. PURPOSE: To explore the complementary and competitive roles of a variety of conventional and quantitative MRI methods for distinguishing subregions of brain gliomas. STUDY TYPE: Prospective. POPULATION: Fifty-one tissue specimens were collected using image-guided localized biopsy surgery from 10 patients with newly diagnosed gliomas. FIELD STRENGTH/SEQUENCE: Conventional and quantitative MR images consisting of pre- and postcontrast T ASSESSMENT: Biopsy specimens were histopathologically attributed to glioma tissue subregion categories of active tumor (AT), infiltrative edema (IE), and normal tissue (NT) subregions. For each tissue sample, a feature vector comprising 15 MRI-based parameters was derived from preoperative images and assessed by a machine learning algorithm to determine the best multiparametric feature combination for characterizing the tissue subregions. STATISTICAL TESTS: For discrimination of AT, IE, and NT subregions, a one-way analysis of variance (ANOVA) test and for pairwise tissue subregion differentiation, Tukey honest significant difference, and Games-Howell tests were applied (P < 0.05). Cross-validated feature selection and classification methods were implemented for identification of accurate multiparametric MRI parameter combination.
resultsAfter exclusion of 17 tissue specimens, 34 samples (AT = 6, IE = 20, and NT = 8) were considered for analysis. Highest accuracies and statistically significant differences for discrimination of IE from NT and AT from NT were observed for diffusion-based parameters (AUCs >90%), and the perfusion-derived parameter as the most accurate feature in distinguishing IE from AT. A combination of "CBV, MD, T DATA
conclusionIntegration of a few quantitative along with conventional MRI parameters may provide a potential multiparametric imaging biomarker for predicting the histopathologically proven glioma tissue subregions. LEVEL OF EVIDENCE: 2 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2018;48:938-950.
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