Evidence map›Paper›PMID 34460616›Full record

ArticleJournal of imaging2021

The Potential Use of Radiomics with Pre-Radiation Therapy MR Imaging in Predicting Risk of Pseudoprogression in Glioblastoma Patients.

Michael Baine, Justin Burr, Qian Du, Chi Zhang, Xiaoying Liang, Luke Krajewski, Laura Zima, Gerard Rux, Chi Zhang, Dandan Zheng

Abstract read
In one paragraph

Article in Journal of imaging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed, 1 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.

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

13 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
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  12. Radiomics for precision medicine in glioblastoma.Journal of neuro-oncology · 2022
    Review
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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.

Michael BaineDepartment of Radiation Oncology, University of Nebraska Medical Center, Omaha, NE 68198, USA.ORCID 0000-0002-1163-7539
Justin BurrDepartment of Radiation Oncology, University of Nebraska Medical Center, Omaha, NE 68198, USA.
Qian DuDepartment of Biological Science, University of Nebraska Lincoln, Lincoln, NE 68588, USA.
Chi ZhangDepartment of Biological Science, University of Nebraska Lincoln, Lincoln, NE 68588, USA.
Xiaoying LiangDepartment of Radiation Oncology, University of Florida Proton Institute, Jacksonville, FL 32206, USA.
Luke KrajewskiDepartment of Radiation Oncology, University of Nebraska Medical Center, Omaha, NE 68198, USA.
Laura ZimaDepartment of Radiation Oncology, University of Nebraska Medical Center, Omaha, NE 68198, USA.
Gerard RuxDepartment of Radiation Oncology, University of Nebraska Medical Center, Omaha, NE 68198, USA.
Chi ZhangDepartment of Radiation Oncology, University of Nebraska Medical Center, Omaha, NE 68198, USA.
Dandan ZhengDepartment of Radiation Oncology, University of Nebraska Medical Center, Omaha, NE 68198, USA.ORCID 0000-0003-2259-1633

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Glioblastoma (GBM) is the most common adult glioma. Differentiating post-treatment effects such as pseudoprogression from true progression is paramount for treatment. Radiomics has been shown to predict overall survival and MGMT (methylguanine-DNA methyltransferase) promoter status in those with GBM. A potential application of radiomics is predicting pseudoprogression on pre-radiotherapy (RT) scans for patients with GBM. A retrospective review was performed with radiomic data analyzed using pre-RT MRI scans. Pseudoprogression was defined as post-treatment findings on imaging that resolved with steroids or spontaneously on subsequent imaging. Of the 72 patients identified for the study, 35 were able to be assessed for pseudoprogression, and 8 (22.9%) had pseudoprogression. A total of 841 radiomic features were examined along with clinical features. Receiver operating characteristic (ROC) analyses were performed to determine the AUC (area under ROC curve) of models of clinical features, radiomic features, and combining clinical and radiomic features. Two radiomic features were identified to be the optimal model combination. The ROC analysis found that the predictive ability of this combination was higher than using clinical features alone (mean AUC: 0.82 vs. 0.62). Additionally, combining the radiomic features with clinical factors did not improve predictive ability. Our results indicate that radiomics is potentially capable of predicting future development of pseudoprogression in patients with GBM using pre-RT MRIs.

Indexed as

GBMglioblastomapseudoprogressionradiationradiomics

Identifiers

PMID34460616
PMCPMC8321255

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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.