Evidence map›Paper›PMID 37376821›Full record

ArticleCancer medicine2023

Magnetic resonance imaging findings of intracranial extraventricular ependymoma: A retrospective multi-center cohort study of 114 cases.

Liyan Li, Yan Fu, Yinping Zhang, Yipu Mao, Deyou Huang, Xiaoping Yi, Jing Wang, Zeming Tan, Muliang Jiang, Bihong T Chen

Open access · goldAbstract readMulticenter Study
In one paragraph

Article in Cancer medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
2.9field-weighted citation impact, top 9% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed, 10 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors at 5 institutions in 2 countries.

Liyan LiDepartment of Radiology, First Affiliated Hospital of Guangxi Medical University, Nanning, P. R. China.ORCID 0000-0003-0318-3909
Yan FuDepartment of Radiology, Xiangya Hospital, Central South University, Changsha, P. R. China.
Yinping ZhangDepartment of Radiology, Xiangya Hospital, Central South University, Changsha, P. R. China.
Yipu MaoDepartment of Radiology, Nanning First People's Hospital, Nanning, P. R. China.
Deyou HuangDepartment of Radiology, Affiliated Hospital of Youjiang Medical University for Nationalities, Baise, P. R. China.
Xiaoping YiDepartment of Radiology, Xiangya Hospital, Central South University, Changsha, P. R. China.ORCID 0000-0002-4220-1981
Jing WangDepartment of Neurology, Xiangya Hospital, Central South University, Changsha, P. R. China.
Zeming TanDepartment of Neurosurgery, Xiangya Hospital, Central South University, Changsha, P. R. China.
Muliang JiangDepartment of Radiology, First Affiliated Hospital of Guangxi Medical University, Nanning, P. R. China.
Bihong T ChenDepartment of Diagnostic Radiology, City of Hope National Medical Center, Duarte, California, USA.
Central South University · CNGuangxi Medical University · CNAffiliated Hospital of Youjiang Medical University for Nationalities · CNCity Of Hope National Medical Center · USFirst People's Hospital of Nanning · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIntracranial extraventricular ependymoma (IEE) is an ependymoma located in the brain parenchyma outside the ventricles. IEE has overlapping clinical and imaging characteristics with glioblastoma multiforme (GBM) but different treatment strategy and prognosis. Therefore, an accurate preoperative diagnosis is necessary for optimizing therapy for IEE.

methodsA retrospective multicenter cohort of IEE and GBM was identified. MR imaging characteristics assessed with the Visually Accessible Rembrandt Images (VASARI) feature set and clinicopathological findings were recorded. Independent predictors for IEE were identified using multivariate logistic regression, which was used to construct a diagnostic score for differentiating IEE from GBM.

resultsCompared to GBM, IEE tended to occur in younger patients. Multivariate logistic regression analysis identified seven independent predictors for IEE. Among them, 3 predictors including tumor necrosis rate (F7), age, and tumor-enhancing margin thickness (F11), demonstrated higher diagnostic performance with an Area Under Curve (AUC) of more than 70% in distinguishing IEE from GBM. The AUC was 0.85, 0.78, and 0.70, with sensitivity of 92.98%, 72.81%, and 96.49%, and specificity of 65.50%, 73.64%, and 43.41%, for F7, age, and F11, respectively.

conclusionWe identified specific MR imaging features such as tumor necrosis and thickness of enhancing tumor margins that could help to differentiate IEE from GBM. Our study results should be helpful to assist in diagnosis and clinical management of this rare brain tumor.

Indexed as

Brain NeoplasmsEpendymomaGlioblastomaCohort StudiesHumansMagnetic Resonance ImagingNecrosisRetrospective Studiesglioblastoma multiforme (GBM)intracranial extraventricular ependymoma (IEE)magnetic resonance imaging (MRI)visually AcceSAble Rembrandt images (VASARI)

Identifiers

PMID37376821
PMCPMC10469843
OpenAlexW4382344229

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Registered trials

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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.