Evidence map›Paper›PMID 25611736›Full record

ArticleRadiology2015

Pseudoprogression in Patients with Glioblastoma: Assessment by Using Volume-weighted Voxel-based Multiparametric Clustering of MR Imaging Data in an Independent Test Set.

Ji Eun Park, Ho Sung Kim, Myeong Ju Goh, Sang Joon Kim, Jeong Hoon Kim

Registry-linked trialAbstract readComparative Study
PubMed Publisher
In one paragraph

Article in Radiology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT02613988 (Early Response Assessment Using on 3T Advanced MR Imaging as Predictor of Long-term Treatment Response in Newly Diagnosed Glioblastomas), which is not on this map. Cited by 35 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
35citing papers in PubMed, 3 pooled it
–field-weighted citation impact
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.

NCT02613988 naunknown statusnot on this mapstarted 2020, after this paper: background citation

Early Response Assessment Using on 3T Advanced MR Imaging as Predictor of Long-term Treatment Response in Newly Diagnosed Glioblastomas

TypeinterventionalSponsorAsan Medical CenterRan2020 to 2025Enrolled100ConditionsAdult GlioblastomaArms3 Tesla magnetic resonance imaging, Chemical exchange saturation transfer MRI, Diffusion weighted MRI, Dynamic susceptibility contrast MRI
3 · Its place in the literature

Who cites it

35 citing papers in PubMed, 3 syntheses or guidelines pooled it.

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  20. Differentiating between Glioblastoma and Primary CNS Lymphoma Using Combined Whole-tumor Histogram Analysis of the Normalized Cerebral Blood Volume and the Apparent Diffusion Coefficient.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2019
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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

5 authors.

Ji Eun ParkFrom the Department of Radiology and Research Institute of Radiology (J.E.P., H.S.K., M.J.G., S.J.K.) and Department of Neurosurgery (J.H.K.), University of Ulsan College of Medicine, Asan Medical Center, 86 Asanbyeongwon-Gil, Songpa-Gu, Seoul 138-736, Korea.
Ho Sung Kim
Myeong Ju Goh
Sang Joon Kim
Jeong Hoon Kim

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeTo validate a volume-weighted voxel-based multiparametric clustering (VVMC) method for magnetic resonance imaging data that is designed to differentiate between pseudoprogression and early tumor progression (ETP) in patients with glioblastoma in an independent test set. MATERIALS AND

methodsThis retrospective study was approved by the local institutional review board, with waiver of the need to obtain informed consent. The study patients were grouped chronologically into a training set (108 patients) and a test set (54 patients). The reference standard was pathologic findings or subsequent clinical-radiologic study results. By using the optimal cutoff determined in the training set, the diagnostic performance of VVMC was subsequently tested in the test set and was compared with that of single-parameter measurements (apparent diffusion coefficient [ADC], normalized cerebral blood volume [nCBV], and initial area under the time-signal intensity curve).

resultsInterreader agreement was highest for VVMC (intraclass correlation coefficient, 0.87-0.89). Receiver operating characteristic curve analysis revealed that VVMC performed the best as a classifier, although statistical significance was not demonstrated with respect to the nCBV in the training set. In the test set, the diagnostic accuracy of VVMC was higher than that of any single-parameter measurements, but this trend reached significance only for the ADC. When the entire population was considered, VVMC had significantly better diagnostic accuracy than did any single parameter (P = .003-.046 for reader 1; P = .002-.016 for reader 2). Results of fivefold cross validation confirmed the trends in both the training set and the test set.

conclusionVVMC is a superior and more reproducible imaging biomarker than single-parameter measurements for differentiating between pseudoprogression and ETP in patients with glioblastoma. Online supplemental material is available for this article.

Indexed as

Brain NeoplasmsDisease ProgressionFemaleGlioblastomaHumansMagnetic Resonance ImagingMaleMiddle AgedRetrospective Studies

Identifiers

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Registered trials

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.