The DOI matches its Crossref record.
Gliomas: classification with MR imaging.
Abstract
The findings at magnetic resonance (MR) imaging in a group of 36 pathologically verified supratentorial gliomas were analyzed and compared with the biopsy diagnoses (a) to determine whether MR imaging could be used to classify astrocytic-series tumors into a three-tiered system of low-grade astrocytoma, anaplastic astrocytoma, and glioblastoma multiforme; and (b) to evaluate MR imaging features that may aid in this classification. The MR characteristics evaluated were crossing of the midline, edema, tumor signal heterogeneity, hemorrhage, border definition, cyst formation or necrosis, and mass effect. The statistically significant MR characteristics (positive predictors) were mass effect (P = .0000) and cyst formation or necrosis (P = .0512). The MR accuracy rate approached that of neuropathologic diagnosis, which is subject to sampling errors. MR imaging may serve as an adjunct in case management when the clinical course and MR findings appear to be at odds with the neuropathologic diagnosis.
Overview
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
Verification
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.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
Gliomas: diffusion kurtosis MR imaging in grading
Van Cauter, S., Veraart, J., Sijbers, J., Peeters, R. R., Himmelreich, U., De Keyzer, F., Van Gool, S. W., Van Calenbergh, F., De Vleeschouwer, S., Van Hecke, W., et al. (2012) · Radiology · doi:10.1148/radiol.12110927 · Cited by 340
Magnetic resonance imaging and histopathology of cerebral gliomas
Watanabe, M., Tanaka, R., & Takeda, N. (1992) · Neuroradiology · doi:10.1007/bf00598951 · Cited by 329
Noerholm, M., Balaj, L., Limperg, T., Salehi, A., Zhu, L. D., Hochberg, F. H., Breakefield, X. O., Carter, B. S., & Skog, J. (2012) · BMC Cancer · doi:10.1186/1471-2407-12-22 · Cited by 190
Metadata
- Authors
- Dean, B. L., Drayer, B. P., Bird, C. R., Flom, R. A., Hodak, J. A., Coons, S. W., & Carey, R. G.
- Venue
- Radiology
- Published
- 1990-02
- Volume
- 174
- Issue
- 2
- Pages
- 411-415
- ISSN
- 0033-8419 (ISSN-L)
- 1527-1315
- OpenAlex
- W2050922989
- Cited by
- 260
- References
- 0
- FWCI
- 4.80
- Type
- journal article
- Language
- en
- Open access
- closed
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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Gliomas: classification with MR imaging. https://app.cochat.ai/papers/10.1148/radiology.174.2.2153310
- The paper
- Dean, B. L., Drayer, B. P., Bird, C. R., Flom, R. A., Hodak, J. A., Coons, S. W., & Carey, R. G. (1990). Gliomas: classification with MR imaging. Radiology. https://doi.org/10.1148/radiology.174.2.2153310
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.