Evidence map›Paper›PMID 34778086›Full record

ArticleFrontiers in oncology2021

Preoperative Magnetic Resonance Imaging Radiomics for Predicting Early Recurrence of Glioblastoma.

Jing Wang, Xiaoping Yi, Yan Fu, Peipei Pang, Huihuang Deng, Haiyun Tang, Zaide Han, Haiping Li, Jilin Nie, Guanghui Gong and 3 more

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed, 1 pooled it
1.8field-weighted citation impact, top 15% 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

15 citing papers in PubMed, 1 synthesis or guideline pooled it, 18 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

13 authors at 3 institutions in 2 countries.

Jing WangDepartment of Radiology, Xiangya Hospital, Central South University, Changsha, China.
Xiaoping YiDepartment of Radiology, Xiangya Hospital, Central South University, Changsha, China.
Yan FuDepartment of Radiology, Xiangya Hospital, Central South University, Changsha, China.
Peipei PangDepartment of Pharmaceuticals Diagnosis, GE Healthcare, Hangzhou, China.
Huihuang DengDepartment of Radiology, Xiangya Hospital, Central South University, Changsha, China.
Haiyun TangDepartment of Radiology, Xiangya Hospital, Central South University, Changsha, China.
Zaide HanDepartment of Radiology, Xiangya Hospital, Central South University, Changsha, China.
Haiping LiDepartment of Radiology, Xiangya Hospital, Central South University, Changsha, China.
Jilin NieDepartment of Radiology, Xiangya Hospital, Central South University, Changsha, China.
Guanghui GongDepartment of Pathology, Xiangya Hospital, Central South University, Changsha, China.
Zhongliang HuDepartment of Pathology, Xiangya Hospital, Central South University, Changsha, China.
Zeming TanDepartment of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China.
Bihong T ChenDepartment of Diagnostic Radiology, City of Hope National Medical Center, Duarte, CA, United States.
Central South University · CNXiangya Hospital Central South University · CNCity Of Hope National Medical Center · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeEarly recurrence of glioblastoma after standard treatment makes patient care challenging. This study aimed to assess preoperative magnetic resonance imaging (MRI) radiomics for predicting early recurrence of glioblastoma. PATIENTS AND

methodsA total of 122 patients (training cohort: n = 86; validation cohort: n = 36) with pathologically confirmed glioblastoma were included in this retrospective study. Preoperative brain MRI images were analyzed for both radiomics and the Visually Accessible Rembrandt Image (VASARI) features of glioblastoma. Models incorporating MRI radiomics, the VASARI parameters, and clinical variables were developed and presented in a nomogram. Performance was assessed based on calibration, discrimination, and clinical usefulness.

resultsThe nomogram consisting of the radiomic signatures, the VASARI parameters, and blood urea nitrogen (BUN) values showed good discrimination between the patients with early recurrence and those with later recurrence, with an area under the curve of 0.85 (95% CI, 0.77-0.94) in the training cohort and 0.84 [95% CI, 0.71-0.97] in the validation cohort. Decision curve analysis demonstrated favorable clinical application of the nomogram.

conclusionThis study showed the potential usefulness of preoperative brain MRI radiomics in predicting the early recurrence of glioblastoma, which should be helpful in personalized management of glioblastoma.

Indexed as

blood urea nitrogenglioblastomamagnetic resonance imagingnomogrampreoperativeradiomicsrecurrenceVisually Accessible Rembrandt Images (VASARI)

Identifiers

PMID34778086
PMCPMC8579096
OpenAlexW3210179307

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

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.