ArticleNeuroradiology2014
Multimodal MR imaging model to predict tumor infiltration in patients with gliomas.
Article in Neuroradiology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it, 30 citations in OpenAlex.
- Optimal differentiation of high- and low-grade glioma and metastasis: a meta-analysis of perfusion, diffusion, and spectroscopy metrics.Neuroradiology · 2016Pooled it
- Imaging-Based Algorithm for the Local Grading of Glioma.AJNR. American journal of neuroradiology · 2020Trial
- Guiding the first biopsy in glioma patients using estimated Ki-67 maps derived from MRI: conventional versus advanced imaging.Neuro-oncology · 2019Trial
- Predicting glioblastoma recurrence using multiparametric MR imaging of non-enhancing peritumoral regions at baseline.Heliyon · 2024Article
- Integrative Magnetic Resonance Imaging and Metabolomic Characterization of a Glioblastoma Rat Model.Brain sciences · 2024Article
- The Concept of «Peritumoral Zone» in Diffuse Low-Grade Gliomas: Oncological and Functional Implications for a Connectome-Guided Therapeutic Attitude.Brain sciences · 2022Review
- Visualizing Glioma Infiltration by the Combination of Multimodality Imaging and Artificial Intelligence, a Systematic Review of the Literature.Diagnostics (Basel, Switzerland) · 2021Review
- Estimating Local Cellular Density in Glioma Using MR Imaging Data.AJNR. American journal of neuroradiology · 2021Article
- Improved detection of diffuse glioma infiltration with imaging combinations: a diagnostic accuracy study.Neuro-oncology · 2020Article
- Accurate Patient-Specific Machine Learning Models of Glioblastoma Invasion Using Transfer Learning.AJNR. American journal of neuroradiology · 2019Article
- Characterization of active and infiltrative tumorous subregions from normal tissue in brain gliomas using multiparametric MRI.Journal of magnetic resonance imaging : JMRI · 2018Article
- A combined diffusion tensor imaging and Ki-67 labeling index study for evaluating the extent of tumor infiltration using the F98 rat glioma model.Journal of neuro-oncology · 2018Article
- Multimodal Imaging of Patients With Gliomas ConfirmsMolecular imaging · 2017Article
- Diffusion and perfusion weighted magnetic resonance imaging for tumor volume definition in radiotherapy of brain tumors.Radiation oncology (London, England) · 2016Review
- Multimodal neuroimaging computing: the workflows, methods, and platforms.Brain informatics · 2015Review
- Multimodal neuroimaging computing: a review of the applications in neuropsychiatric disorders.Brain informatics · 2015Review
- State-of-the-art MRI techniques in neuroradiology: principles, pitfalls, and clinical applications.Neuroradiology · 2015Review
- Optimal Symmetric Multimodal Templates and Concatenated Random Forests for Supervised Brain Tumor Segmentation (Simplified) with ANTsR.Neuroinformatics · 2015Article
Corrections and comments
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Authors and funding
14 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionGliomas remain difficult to treat, in part, due to our inability to accurately delineate the margins of the tumor. The goal of our study was to evaluate if a combination of advanced MR imaging techniques and a multimodal imaging model could be used to predict tumor infiltration in patients with diffuse gliomas.
methodsInstitutional review board approval and written consent were obtained. This prospective pilot study enrolled patients undergoing stereotactic biopsy for a suspected de novo glioma. Stereotactic biopsy coordinates were coregistered with multiple standard and advanced neuroimaging sequences in 10 patients. Objective imaging values were assigned to the biopsy sites for each of the imaging sequences. A principal component analysis was performed to reduce the dimensionality of the imaging dataset without losing important information. A univariate analysis was performed to identify the statistically relevant principal components. Finally, a multivariate analysis was used to build the final model describing nuclear density.
resultsA univariate analysis identified three principal components as being linearly associated with the observed nuclear density (p values 0.021, 0.016, and 0.046, respectively). These three principal component composite scores are predominantly comprised of DTI (mean diffusivity or average diffusion coefficient and fractional anisotropy) and PWI data (rMTT, Ktrans). The p value of the model was <0.001. The correlation between the predicted and observed nuclear density was 0.75.
conclusionA multi-input, single output imaging model may predict the extent of glioma invasion with significant correlation with histopathology.
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Registered trials
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.