Evidence map›Paper›PMID 29721688›Full record

ArticleEuropean radiology2018

Radiogenomics correlation between MR imaging features and major genetic profiles in glioblastoma.

Eun Kyoung Hong, Seung Hong Choi, Dong Jae Shin, Sang Won Jo, Roh-Eul Yoo, Koung Mi Kang, Tae Jin Yun, Ji-Hoon Kim, Chul-Ho Sohn, Sung-Hye Park and 5 more

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Article in European radiology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
44citing 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

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

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  10. Artificial intelligence-based MRI radiomics and radiogenomics in glioma.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024
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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

15 authors.

Eun Kyoung HongDepartment of Radiology, Seoul National University Hospital, Seoul, Republic of Korea.
Seung Hong ChoiDepartment of Radiology, Seoul National University Hospital, Seoul, Republic of Korea. verocay@snuh.org.ORCID http://orcid.org/0000-0001-8985-8473
Dong Jae ShinDepartment of Radiology, Seoul National University Hospital, Seoul, Republic of Korea.
Sang Won JoDepartment of Radiology, Seoul National University Hospital, Seoul, Republic of Korea.
Roh-Eul YooDepartment of Radiology, Seoul National University Hospital, Seoul, Republic of Korea.
Koung Mi KangDepartment of Radiology, Seoul National University Hospital, Seoul, Republic of Korea.
Tae Jin YunDepartment of Radiology, Seoul National University Hospital, Seoul, Republic of Korea.
Ji-Hoon KimDepartment of Radiology, Seoul National University Hospital, Seoul, Republic of Korea.
Chul-Ho SohnDepartment of Radiology, Seoul National University Hospital, Seoul, Republic of Korea.
Sung-Hye ParkDepartment of Pathology, Seoul National University Hospital, Seoul, Korea.
Jae-Kyung WonDepartment of Pathology, Seoul National University Hospital, Seoul, Korea.
Tae Min KimDepartment of Internal Medicine, Seoul National University Hospital, Seoul, Korea.
Chul-Kee ParkDepartment of Neurosurgery, Seoul National University Hospital, Seoul, Korea.
Il Han KimDepartment of Radiation Oncology, Seoul National University Hospital, Seoul, Korea.
Soon Tae LeeDepartment of Neurology, Seoul National University Hospital, Seoul, Korea.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesTo assess the association between MR imaging features and major genomic profiles in glioblastoma.

methodsQualitative and quantitative imaging features such as volumetrics and histogram analysis from normalised CBV (nCBV) and ADC (nADC) were evaluated based on both T2WI and CET1WI. The imaging parameters of different genetic profile groups were compared and regression analyses were used for identifying imaging-molecular associations. Progression-free survival (PFS) was analysed by a Kaplan-Meier test and Cox proportional hazards model.

resultsAn IDH mutation was observed in 18/176 patients, and ATRX loss was positive in 17/158 of the IDH-wt cases. The IDH-mut group showed a larger volume on T2WI and a higher volume ratio between T2WI and CET1WI than the IDH-wt group (p < 0.05). In the IDH-mut group, higher mean nADC values were observed compared with the IDH-wt tumours (p < 0.05). Among the IDH-wt tumours, IDH-wt, ATRX-loss tumours revealed higher 5th percentile nADC values than the IDH-wt, ATRX-noloss tumours (p = 0.03). PFS was the longest in the IDH-mut group, followed by the IDH-wt, ATRX-loss groups and the IDH-wt, ATRX-noloss groups, consecutively (p < 0.05). We found significant associations of PFS with the genetic profiles and imaging parameters.

conclusionMajor genetic profiles of glioblastoma showed a significant association with MR imaging features, along with some genetic profiles, which are independent prognostic parameters for GBM. KEY POINTS: • Significant correlation exists between radiological parameters such as volumetric and ADC values and major genomic profiles such as IDH mutation and ATRX loss status • Radiological parameters such as the ADC value were feasible predictors of glioblastoma patients' prognosis • Imaging features can predict major genomic profiles of the tumours and the prognosis of glioblastoma patients.

Indexed as

Magnetic Resonance ImagingAdultAgedBrain NeoplasmsDisease-Free SurvivalFemaleGlioblastomaHumansIsocitrate DehydrogenaseKaplan-Meier EstimateMaleMiddle AgedMutationPrognosisProportional Hazards ModelsRetrospective StudiesIsocitrate DehydrogenaseX-linked Nuclear ProteinATRX protein, humanDiffusion magnetic resonance imagingGlioblastomaIsocitrate dehydrogenaseMagnetic resonance imaging

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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.