The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Survival and low-grade glioma: the emergence of genetic information
Open accessAbstract
Significant gaps exist in our understanding of the causes and clinical management of glioma. One of the biggest gaps is how best to manage low-grade (World Health Organization [WHO] Grade II) glioma. Low-grade glioma (LGG) is a uniformly fatal disease of young adults (mean age 41 years), with survival averaging approximately 7 years. Although LGG patients have better survival than patients with high-grade (WHO Grade III or IV) glioma, all LGGs eventually progress to high-grade glioma and death. Data from the Surveillance, Epidemiology and End Results (SEER) program of the National Cancer Institute suggest that for the majority of LGG patients, overall survival has not significantly improved over the past 3 decades, highlighting the need for intensified study of this tumor. Recently published research suggests that historically used clinical variables are not sufficient (and are likely inferior) prognostic and predictive indicators relative to information provided by recently discovered tumor markers (e.g., 1p/19q deletion and IDH1 or IDH2 mutation status), tumor expression profiles (e.g., the proneural profile) and/or constitutive genotype (e.g., rs55705857 on 8q24.21). Discovery of such tumor and constitutive variation may identify variables needed to improve randomization in clinical trials as well as identify patients more sensitive to current treatments and targets for improved treatment in the future. This article reports on survival trends for patients diagnosed with LGG within the United States from 1973 through 2011 and reviews the emerging role of tumor and constitutive genetics in refining risk stratification, defining targeted therapy, and improving survival for this group of relatively young patients.
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
Cites
Comprehensive genomic characterization defines human glioblastoma genes and core pathways
The Cancer Genome Atlas Research Network (2008) · Nature · doi:10.1038/nature07385 · Cited by 7,735
Verhaak, R. G. W., Hoadley, K. A., Purdom, E., Wang, V., Qi, Y., Wilkerson, M. D., Miller, C. R., Ding, L., Golub, T., Mesirov, J. P., et al. (2010) · Cancer Cell · doi:10.1016/j.ccr.2009.12.020 · Cited by 7,575
An Integrated Genomic Analysis of Human Glioblastoma Multiforme
Parsons, D. W., Jones, S., Zhang, X., Lin, J. C.-H., Leary, R. J., Angenendt, P., Mankoo, P., Carter, H., Siu, I.-M., Gallia, G. L., et al. (2008) · Science · doi:10.1126/science.1164382 · Cited by 5,857
IDH1 and IDH2 Mutations in Gliomas
Yan, H., Parsons, D. W., Jin, G., McLendon, R., Rasheed, B. A., Yuan, W., Kos, I., Batinic-Haberle, I., Jones, S., Riggins, G. J., et al. (2009) · New England Journal of Medicine · doi:10.1056/nejmoa0808710 · Cited by 5,847
IDH mutation impairs histone demethylation and results in a block to cell differentiation
Lu, C., Ward, P. S., Kapoor, G. S., Rohle, D., Turcan, S., Abdel-Wahab, O., Edwards, C. R., Khanin, R., Figueroa, M. E., Melnick, A., et al. (2012) · Nature · doi:10.1038/nature10860 · Cited by 1,985
IDH1 mutation is sufficient to establish the glioma hypermethylator phenotype
Turcan, S., Rohle, D., Goenka, A., Walsh, L. A., Fang, F., Yilmaz, E., Campos, C., Fabius, A. W. M., Lu, C., Ward, P. S., et al. (2012) · Nature · doi:10.1038/nature10866 · Cited by 1,974
Karim, A. B. M. F., Maat, B., Hatlevoll, R., Menten, J., Rutten, E. H. J. M., Thomas, D. G. T., Mascarenhas, F., Horiot, J. C., Parvinen, L. M., van Reijn, M., et al. (1996) · International Journal of Radiation Oncology*Biology*Physics · doi:10.1016/s0360-3016(96)00352-5 · Cited by 694 · cited 1×
“Randomized clinical trials suggest that radiation therapy pro-longs time to recurrence but not overall survival 36,38,62,72 and may be associated with reduction in quality of life and cognition, 1,16,38,45 while the impact of TMZ, the primary single chemotherapeutic agent now used to treat LGG, has…”
Background
Cited by
Liu, H., Karsidag, M., Chhatwal, K., Wang, P., & Tang, T. (2025) · PLOS ONE · doi:10.1371/journal.pone.0314745 · Cited by 48
Yuan, Y., Xiang, W., Yanhui, L., Ruofei, L., Yunhe, M., Jiewen, L., & Qing, M. (2016) · Epilepsy Research · doi:10.1016/j.eplepsyres.2016.08.005 · Cited by 17
Villeneuve, P. J., Momoli, F., Parent, M.-É., Siemiatycki, J., Turner, M. C., & Krewski, D. (2021) · Environmental Research · doi:10.1016/j.envres.2021.111283 · Cited by 16
Prabhudesai, S., Wang, N. C., Ahluwalia, V., Huan, X., Bapuraj, J. R., Banovic, N., & Rao, A. (2021) · Frontiers in Neuroscience · doi:10.3389/fnins.2021.740353 · Cited by 13
Rimmer, B., Dutton, L., Lewis, J., Burns, R., Gallagher, P., Williams, S., Araujo-Soares, V., Finch, T., & Sharp, L. (2020) · BMJ Open · doi:10.1136/bmjopen-2020-041465 · Cited by 12
Metadata
- Authors
- Claus, E. B., Walsh, K. M., Wiencke, J. K., Molinaro, A. M., Wiemels, J. L., Schildkraut, J. M., Bondy, M. L., Berger, M., Jenkins, R., & Wrensch, M.
- Venue
- Neurosurgical Focus
- Published
- 2015-01
- Volume
- 38
- Issue
- 1
- Pages
- E6
- ISSN
- 1092-0684 (ISSN-L)
- OpenAlex
- W2010573862
- Cited by
- 445
- References
- 7
- FWCI
- 10.11
- Type
- journal article
- Language
- en
- Open access
- bronze
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
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.