Evidence map›Paper›PMID 32193547›Full record

ArticleNeuro-oncology2020

Multiparametric MRI for early identification of therapeutic response in recurrent glioblastoma treated with immune checkpoint inhibitors.

Joseph Song, Priyanka Kadaba, Amanda Kravitz, Adilia Hormigo, Joshua Friedman, Puneet Belani, Constantinos Hadjipanayis, Benjamin M Ellingson, Kambiz Nael

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

Article in Neuro-oncology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.

0numbers the graph read from it
0cells of the map it votes in
29citing papers in PubMed
3.5field-weighted citation impact, top 7% 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

29 citing papers in PubMed, 44 citations in OpenAlex.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

9 authors at 2 institutions in 1 country.

Joseph SongIcahn School of Medicine at Mount Sinai, Department of Radiology (Neuroimaging Advanced and Exploratory Lab), New York, New York.
Priyanka KadabaIcahn School of Medicine at Mount Sinai, Department of Radiology (Neuroimaging Advanced and Exploratory Lab), New York, New York.
Amanda KravitzIcahn School of Medicine at Mount Sinai, Department of Radiology (Neuroimaging Advanced and Exploratory Lab), New York, New York.
Adilia HormigoDepartment of Neurology, Medicine (Div Hem Onc), The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, New York.
Joshua FriedmanDepartment of Neurology, Medicine (Div Hem Onc), The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, New York.
Puneet BelaniIcahn School of Medicine at Mount Sinai, Department of Radiology (Neuroimaging Advanced and Exploratory Lab), New York, New York.
Constantinos HadjipanayisDepartment of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, New York.
Benjamin M EllingsonUCLA Brain Tumor Imaging Laboratory, Center for Computer Vision and Imaging Biomarkers, Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California.
Kambiz NaelUCLA Brain Tumor Imaging Laboratory, Center for Computer Vision and Imaging Biomarkers, Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California.
Icahn School of Medicine at Mount Sinai · USUniversity of California, Los Angeles · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPhysiologic changes quantified by diffusion and perfusion MRI have shown utility in predicting treatment response in glioblastoma (GBM) patients treated with cytotoxic therapies. We aimed to investigate whether quantitative changes in diffusion and perfusion after treatment by immune checkpoint inhibitors (ICIs) would determine 6-month progression-free survival (PFS6) in patients with recurrent GBM.

methodsInclusion criteria for this retrospective study were: (i) diagnosis of recurrent GBM treated with ICIs and (ii) availability of diffusion and perfusion in pre and post ICI MRI (iii) at ≥6 months follow-up from treatment. After co-registration, mean values of the relative apparent diffusion coefficient (rADC), Ktrans (volume transfer constant), Ve (extravascular extracellular space volume) and Vp (plasma volume), and relative cerebral blood volume (rCBV) were calculated from a volume-of-interest of the enhancing tumor. Final assignment of stable/improved versus progressive disease was determined on 6-month follow-up using modified Response Assessment in Neuro-Oncology criteria.

resultsOut of 19 patients who met inclusion criteria and follow-up (mean ± SD: 7.8 ± 1.4 mo), 12 were determined to have tumor progression, while 7 had treatment response after 6 months of ICI treatment. Only interval change of rADC was suggestive of treatment response. Patients with treatment response (6/7: 86%) had interval increased rADC, while 11/12 (92%) with tumor progression had decreased rADC (P = 0.001). Interval change in rCBV, Ktrans, Vp, and Ve were not indicative of treatment response within 6 months.

conclusionsIn patients with recurrent GBM, interval change in rADC is promising in assessing treatment response versus progression within the first 6 months following ICI treatment. KEY POINTS: • In recurrent GBM treated with ICIs, interval change in rADC suggests early treatment response.• Interval change in rADC can be used as an imaging biomarker to determine PFS6.• Interval change in MR perfusion and permeability measures do not suggest ICI treatment response.

Indexed as

Brain NeoplasmsGlioblastomaFemaleHumansImmune Checkpoint InhibitorsMagnetic Resonance ImagingMaleMiddle AgedMultiparametric Magnetic Resonance ImagingRetrospective StudiesImmune Checkpoint InhibitorsglioblastomaimmunotherapyMR diffusionMR perfusiontreatment response

Identifiers

PMID32193547
PMCPMC7846197
OpenAlexW3010730408

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.