Evidence map›Paper›PMID 34272981›Full record

ArticleEuropean radiology2022

Multi-parametric Z-spectral MRI may have a good performance for glioma stratification in clinical patients.

Changliang Su, Shijie Xu, Danlin Lin, Haoqiang He, Zhenghe Chen, Frederick C Damen, Chao Ke, Xiaofei Lv, Kejia Cai

Abstract read
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In one paragraph

Article in European radiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed, 1 pooled it
3.2field-weighted citation impact, top 8% 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

22 citing papers in PubMed, 1 synthesis or guideline pooled it, 35 citations in OpenAlex.

  1. Pooled it
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  18. Advanced MR Techniques for Preoperative Glioma Characterization: Part 2.Journal of magnetic resonance imaging : JMRI · 2023
    Review
  19. Review
  20. TowardBiosensors · 2022
    Review
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

9 authors at 2 institutions in 2 countries.

Changliang Su *Department of Medical Imaging, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, 651 Dongfeng Road East, 510060, Guangzhou, China.
Shijie Xu *Department of Neurosurgery, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, 651 Dongfeng Road East, 510060, Guangzhou, China.
Danlin LinDepartment of Medical Imaging, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, 651 Dongfeng Road East, 510060, Guangzhou, China.
Haoqiang HeDepartment of Medical Imaging, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, 651 Dongfeng Road East, 510060, Guangzhou, China.
Zhenghe ChenDepartment of Neurosurgery, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, 651 Dongfeng Road East, 510060, Guangzhou, China.
Frederick C DamenDepartment of Radiology College of Medicine, University of Illinois at Chicago, Chicago, IL, USA.
Chao KeDepartment of Neurosurgery, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, 651 Dongfeng Road East, 510060, Guangzhou, China. kechao@sysucc.org.cn.
Xiaofei LvDepartment of Medical Imaging, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, 651 Dongfeng Road East, 510060, Guangzhou, China. lvxf@sysucc.org.cn.ORCID http://orcid.org/0000-0002-1712-630X
Kejia CaiDepartment of Radiology College of Medicine, University of Illinois at Chicago, Chicago, IL, USA.
Sun Yat-sen University · CNUniversity of Illinois Chicago · US

Funding

Guangdong Basic and Applied Basic Research Foundation 2019A1515011143
6 · The paper itself

Abstract

objectivesTo comprehensively and noninvasively risk-stratify glioma grade, isocitrate dehydrogenase (IDH) genotype, and 1p/19q codeletion status using multi-contrast Z-spectral magnetic resonance imaging (MRI).

methodsOne hundred and thirteen patients with glioma were retrospectively included. Multiple contrasts contributing to Z-spectra, including direct saturation of water (DSW), semi-solid magnetization transfer contrast (MTC), amide proton transfer (APT) effect, aliphatic nuclear Overhauser effect, and the 2-ppm chemical exchange saturation transfer peak (CEST@2ppm), were fitted with five individual Lorentzian functions. Z-spectral contrasts were compared according to the three most important risk stratifications: tumor grade, IDH genotype, and 1p/19q codeletion status. We further investigated the differentiation of 1p/19q codeletion status within IDH mutant gliomas. The stratification performance of individual Z-spectral contrasts and their combination was quantified using receiver operating characteristic (ROC) analyses.

resultsDSW was significantly different within grade, IDH genotypes, and 1p/19q codeletion status. APT was significantly different with grade and IDH mutation, but not with 1p/19q subtypes. CEST@2ppm was only significantly different with 1p/19q codeletion subtypes. DSW and CEST@2ppm were the two Z-spectral contrasts able to differentiate 1p/19q codeletion subtypes within IDH mutant gliomas. For differentiating glioma grades using ROC analyses, DSW achieved the largest AUC. For differentiating IDH genotypes, DSW and APT achieved comparable AUCs. DSW was the best metric for differentiating 1p/19q codeletion status within all patients and within the IDH mutant patients. Combining all Z-spectral contrasts improved sensitivity and specificity for all risk stratifications.

conclusionsMulti-parametric Z-spectral MRI serves as a useful, comprehensive, and noninvasive imaging technique for glioma stratification in clinical patients. KEY POINTS: • Multiple contrasts contributing to Z-spectra were separately fitted with Lorentzian functions. • Z-spectral contrasts were compared within the three most important and common tumor risk stratifications for gliomas: tumor grade, IDH genotype, and 1p/19q codeletion status. • The stratification performance of individual Z-spectral contrasts and their combination was quantified using receiver operating characteristic analyses, which found Z-spectral MRI to be a useful and comprehensive imaging biomarker for glioma stratification.

Indexed as

Brain NeoplasmsGliomaHumansIsocitrate DehydrogenaseMagnetic Resonance ImagingMutationRetrospective StudiesIsocitrate DehydrogenaseGenotypeGliomaMagnetic resonance imagingRisk assessmentROC curve

Identifiers

PMID34272981
OpenAlexW3178813178

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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.