Evidence map›Paper›PMID 32365185›Full record

ArticleNeurosurgery2020

Diffusion Magnetic Resonance Imaging Phenotypes Predict Overall Survival Benefit From Bevacizumab or Surgery in Recurrent Glioblastoma With Large Tumor Burden.

Kunal S Patel, Richard G Everson, Jingwen Yao, Catalina Raymond, Jodi Goldman, Jacob Schlossman, Joseph Tsung, Caleb Tan, Whitney B Pope, Matthew S Ji and 6 more

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

Article in Neurosurgery, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed, 2 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

18 citing papers in PubMed, 2 syntheses or guidelines pooled it, 22 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

16 authors at 1 institution in 1 country.

Kunal S PatelUCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California.
Richard G EversonDepartment of Neurosurgery, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California.
Jingwen YaoUCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California.
Catalina RaymondUCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California.
Jodi GoldmanUCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California.
Jacob SchlossmanUCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California.
Joseph TsungUCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California.
Caleb TanUCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California.
Whitney B PopeDepartment of Radiological Science, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California.
Matthew S JiDepartment of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California.
Nhung T NguyenDepartment of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California.
Albert LaiDepartment of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California.
Phioanh L NghiemphuDepartment of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California.
Linda M LiauDepartment of Neurosurgery, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California.
Timothy F CloughesyDepartment of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California.
Benjamin M EllingsonUCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California.
University of California, Los Angeles · US

Funding

Women's CancersP30CA016042 · NCI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI Samuel Wheeler French · 1985 to 2026
$134.5M
UCLA SPORE in Brain CancerP50CA211015 · NCI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI David A. Nathanson · 2017 to 2026
$25.2M
Metabolic MRI for quantifying aerobic glycolysis and immune function in gliomasR21CA223757 · NCI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI ELLINGSON, BENJAMIN M. · 2018 to 2019
$366k
EPA EP-C-15-003NCI NIH HHS P30 CA016042NCI NIH HHS P50 CA211015NCI NIH HHS R21 CA223757
6 · The paper itself

Abstract

backgroundDiffusion magnetic resonance (MR) characteristics are a predictive imaging biomarker for survival benefit in recurrent glioblastoma treated with anti-vascular endothelial growth factor (VEGF) therapy; however, its use in large volume recurrence has not been evaluated.

objectiveTo determine if diffusion MR characteristics can predict survival outcomes in patients with large volume recurrent glioblastoma treated with bevacizumab or repeat resection.

methodsA total of 32 patients with large volume (>20 cc or > 3.4 cm diameter) recurrent glioblastoma treated with bevacizumab and 35 patients treated with repeat surgery were included. Pretreatment tumor volume and apparent diffusion coefficient (ADC) histogram analysis were used to phenotype patients as having high (>1.24 μm2/ms) or low (<1.24 μm2/ms) ADCL, the mean value of the lower peak in a double Gaussian model of the ADC histogram within the contrast enhancing tumor.

resultsIn bevacizumab and surgical cohorts, volume was correlated with overall survival (Bevacizumab: P = .009, HR = 1.02; Surgical: P = .006, HR = 0.96). ADCL was an independent predictor of survival in the bevacizumab cohort (P = .049, HR = 0.44), but not the surgical cohort (P = .273, HR = 0.67). There was a survival advantage of surgery over bevacizumab in patients with low ADCL (P = .036, HR = 0.43) but not in patients with high ADCL (P = .284, HR = 0.69).

conclusionPretreatment diffusion MR imaging is an independent predictive biomarker for overall survival in recurrent glioblastoma with a large tumor burden. Large tumors with low ADCL have a survival benefit when treated with surgical resection, whereas large tumors with high ADCL may be best managed with bevacizumab.

Indexed as

AdultAgedAntineoplastic Agents, ImmunologicalBevacizumabBrain NeoplasmsCohort StudiesDiffusion Magnetic Resonance ImagingFemaleGlioblastomaHumansImage Interpretation, Computer-AssistedMaleMiddle AgedNeoplasm Recurrence, LocalNeurosurgical ProceduresPhenotypeAntineoplastic Agents, ImmunologicalBevacizumabADC histogram analysisBevacizumabDiffusion MRIRecurrent glioblastomaT1 subtraction

Identifiers

PMID32365185
PMCPMC7566341
OpenAlexW3021360974

What OpenQuestion holds

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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.