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Cited by 405

The DOI matches its Crossref record; 16 of the 16 references it cites were found in a registry or an index.

Prediction of survival in glioma patients by means of positron emission tomography

Patronas, Nicholas J., Chiro, Giovanni Di, Kufta, Conrad, Bairamian, Dikran, Kornblith, Paul L., Simon, Richard, Larson, Steven M.·1985·Journal of Neurosurgery

Abstract

✓ The aim of this study was to determine whether positron emission tomography (PET) with fluorine-18 (18 F)-2-deoxyglucose (FDG) can be used as a prognostic test in patients with high-grade cerebral gliomas, regardless of the treatment given. Forty-five patients with astrocytoma Grade III or IV were included in this analysis. The mean survival time of patients with tumors exhibiting high glucose utilization as determined by PET-FDG was 5 months, whereas patients with gliomas showing lower glucose utilization had a mean survival period of 19 months. It is postulated that PET-FDG scans reflect the biological behavior of high-grade astrocytomas and may be used to predict the survival time of patients harboring such neoplasms.

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Crossref · 10.3171/jns.1985.62.6.0816

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Metadata

Authors
Patronas, N. J., Chiro, G. D., Kufta, C., Bairamian, D., Kornblith, P. L., Simon, R., & Larson, S. M.
Venue
Journal of Neurosurgery
Published
1985-06
Volume
62
Issue
6
Pages
816-822
ISSN
0022-3085 (ISSN-L)
OpenAlex
W2003991156
Cited by
405
References
16
FWCI
6.15
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Prediction of survival in glioma patients by means of positron emission tomography. https://app.cochat.ai/papers/10.3171/jns.1985.62.6.0816
The paper
Patronas, N. J., Chiro, G. D., Kufta, C., Bairamian, D., Kornblith, P. L., Simon, R., & Larson, S. M. (1985). Prediction of survival in glioma patients by means of positron emission tomography. Journal of Neurosurgery. https://doi.org/10.3171/jns.1985.62.6.0816

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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.