Evidence map›Paper›PMID 29178031›Full record

ArticleEuropean radiology2018

A predictive model for distinguishing radiation necrosis from tumour progression after gamma knife radiosurgery based on radiomic features from MR images.

Zijian Zhang, Jinzhong Yang, Angela Ho, Wen Jiang, Jennifer Logan, Xin Wang, Paul D Brown, Susan L McGovern, Nandita Guha-Thakurta, Sherise D Ferguson and 5 more

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

Article in European radiology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 93 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
93citing papers in PubMed, 4 pooled it
9.8field-weighted citation impact, top 1% 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

93 citing papers in PubMed, 4 syntheses or guidelines pooled it, 161 citations in OpenAlex.

  1. Pooled it
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  3. Delta radiomics: a systematic review.La Radiologia medica · 2021
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  10. Incidence of radiation necrosis following different radiotherapy fractionation schedules for intracranial meningiomas.Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al] · 2026
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33 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

15 authors at 1 institution in 2 countries.

Zijian ZhangCentral South University Xiangya Hospital, Changsha, Hunan, China.
Jinzhong YangDepartment of Radiation Physics, The University of Texas MD Anderson Cancer Center, Unit 1420, 1515 Holcombe Blvd, Houston, TX, 77030, USA. jyang4@mdanderson.org.ORCID http://orcid.org/0000-0002-9254-4501
Angela HoDepartment of Radiation Physics, The University of Texas MD Anderson Cancer Center, Unit 1420, 1515 Holcombe Blvd, Houston, TX, 77030, USA.
Wen JiangDepartment of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Unit 1420, 1515 Holcombe Blvd, Houston, TX, 77030, USA.
Jennifer LoganDepartment of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Unit 1420, 1515 Holcombe Blvd, Houston, TX, 77030, USA.
Xin WangDepartment of Radiation Physics, The University of Texas MD Anderson Cancer Center, Unit 1420, 1515 Holcombe Blvd, Houston, TX, 77030, USA.
Paul D BrownDepartment of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Unit 1420, 1515 Holcombe Blvd, Houston, TX, 77030, USA.
Susan L McGovernDepartment of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Unit 1420, 1515 Holcombe Blvd, Houston, TX, 77030, USA.
Nandita Guha-ThakurtaDepartment of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, Unit 1420, 1515 Holcombe Blvd, Houston, TX, 77030, USA.
Sherise D FergusonDepartment of Neurosurgery, The University of Texas MD Anderson Cancer Center, Unit 1420, 1515 Holcombe Blvd, Houston, TX, 77030, USA.
Xenia FaveDepartment of Radiation Physics, The University of Texas MD Anderson Cancer Center, Unit 1420, 1515 Holcombe Blvd, Houston, TX, 77030, USA.
Lifei ZhangDepartment of Radiation Physics, The University of Texas MD Anderson Cancer Center, Unit 1420, 1515 Holcombe Blvd, Houston, TX, 77030, USA.
Dennis MackinDepartment of Radiation Physics, The University of Texas MD Anderson Cancer Center, Unit 1420, 1515 Holcombe Blvd, Houston, TX, 77030, USA.
Laurence E CourtDepartment of Radiation Physics, The University of Texas MD Anderson Cancer Center, Unit 1420, 1515 Holcombe Blvd, Houston, TX, 77030, USA.
Jing LiDepartment of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Unit 1420, 1515 Holcombe Blvd, Houston, TX, 77030, USA.
The University of Texas MD Anderson Cancer Center · US

Funding

Tumor Evolution and Metastasis ProgramP30CA016672 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI Kathrin Milbury · 1985 to 2026
$290.8M
National Cancer Institute CA016672NCI NIH HHS P30 CA016672
6 · The paper itself

Abstract

objectivesTo develop a model using radiomic features extracted from MR images to distinguish radiation necrosis from tumour progression in brain metastases after Gamma Knife radiosurgery.

methodsWe retrospectively identified 87 patients with pathologically confirmed necrosis (24 lesions) or progression (73 lesions) and calculated 285 radiomic features from four MR sequences (T1, T1 post-contrast, T2, and fluid-attenuated inversion recovery) obtained at two follow-up time points per lesion per patient. Reproducibility of each feature between the two time points was calculated within each group to identify a subset of features with distinct reproducible values between two groups. Changes in radiomic features from one time point to the next (delta radiomics) were used to build a model to classify necrosis and progression lesions.

resultsA combination of five radiomic features from both T1 post-contrast and T2 MR images were found to be useful in distinguishing necrosis from progression lesions. Delta radiomic features with a RUSBoost ensemble classifier had an overall predictive accuracy of 73.2% and an area under the curve value of 0.73 in leave-one-out cross-validation.

conclusionsDelta radiomic features extracted from MR images have potential for distinguishing radiation necrosis from tumour progression after radiosurgery for brain metastases. KEY POINTS: • Some radiomic features showed better reproducibility for progressive lesions than necrotic ones • Delta radiomic features can help to distinguish radiation necrosis from tumour progression • Delta radiomic features had better predictive value than did traditional radiomic features.

Indexed as

AdultAgedBrainBrain NeoplasmsDiagnosis, DifferentialDisease ProgressionFemaleHumansImage Interpretation, Computer-AssistedMagnetic Resonance ImagingMaleMiddle AgedNecrosisNeoplasm Recurrence, LocalPredictive Value of TestsRadiation InjuriesBrain metastasesDelta radiomic featuresGamma Knife radiosurgeryMRIRadiation necrosis

Identifiers

PMID29178031
PMCPMC6036915
OpenAlexW2770305510

What OpenQuestion holds

Textmetadata
LicenceTDM
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.