Advanced magnetic resonance imaging in glioblastoma: a review
Open accessAbstract
Glioblastoma, the most common and most rapidly progressing primary malignant tumor of the central nervous system, continues to portend a dismal prognosis, despite improvements in diagnostic and therapeutic strategies over the last 20 years. The standard of care radiographic characterization of glioblastoma is magnetic resonance imaging (MRI), which is a widely utilized examination in the diagnosis and post-treatment management of patients with glioblastoma. Basic MRI modalities available from any clinical scanner, including native T1-weighted (T1w) and contrast-enhanced (T1CE), T2-weighted (T2w), and T2-fluid-attenuated inversion recovery (T2-FLAIR) sequences, provide critical clinical information about various processes in the tumor environment. In the last decade, advanced MRI modalities are increasingly utilized to further characterize glioblastomas more comprehensively. These include multi-parametric MRI sequences, such as dynamic susceptibility contrast (DSC), dynamic contrast enhancement (DCE), higher order diffusion techniques such as diffusion tensor imaging (DTI), and MR spectroscopy (MRS). Significant efforts are ongoing to implement these advanced imaging modalities into improved clinical workflows and personalized therapy approaches. Functional MRI (fMRI) and tractography are increasingly being used to identify eloquent cortices and important tracts to minimize postsurgical neuro-deficits. A contemporary review of the application of standard and advanced MRI in clinical neuro-oncologic practice is presented here.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The data considered in this study described patient populations with adult-type diffuse glioma, specifically displaying the radiological features of glioblastoma, scanned with multiparametric MRI.
“The data considered in this study described patient populations with adult-type diffuse glioma30, and specifically displaying the radiological features of glioblastoma, scanned with mpMRI to characterize the anatomical tissue structure25.”
From Federated learning enables big data for rare cancer boundary detection (2022) · cites this paper as [25]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
The citation lists haven’t been fetched yet
Cited by 213
- Authors
- Shukla, G., Alexander, G. S., Bakas, S., Nikam, R., Talekar, K., Palmer, J. D., & Shi, W.
- Affiliations
- Thomas Jefferson University (US)
- University of Pennsylvania (US)
- The Ohio State University Wexner Medical Center (US)
- Corresponding author
- Shukla, Gaurav
- Venue
- Chinese Clinical Oncology
- Publisher
- AME Publishing Company
- Published
- 2017-08
- Volume
- 6
- Issue
- 4
- Pages
- 40-40
- ISSN
- 2304-3865 (ISSN-L)
- 2304-3873
- OpenAlex
- W2743924567
- Cited by
- 213
- References
- 0
- Citations by year
- 2026: 18 · 2025: 32 · 2024: 35 · 2023: 36 · 2022: 37 · 2021: 25 · 2020: 20 · 2019: 9 · 2018: 1
- FWCI
- 4.71
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- cc-by-nc-nd
- Locations
- Chinese Clinical Oncology (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Genetics › Glioma Diagnosis and Treatment
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › MRI in cancer diagnosis
- Keywords
- glioblastoma, advanced MRI techniques, dynamic susceptibility contrast MRI, dynamic contrast enhancement, diffusion tensor imaging, MR spectroscopy, functional MRI, tractography, eloquent cortex, tumor characterization, central nervous system tumors
- MeSH terms
- Brain Neoplasms, Glioblastoma, Humans, Magnetic Resonance Imaging, Magnetic Resonance Spectroscopy, Prognosis, Sensitivity and Specificity, Diffusion Magnetic Resonance Imaging, Diffusion Tensor Imaging
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review