Evidence map›Paper›PMID 39525622›Full record

ArticleFrontiers in oncology2024

Distributed parameter model of dynamic contrast-enhanced MRI in the identification of IDH mutation, 1p19q codeletion, and tumor cell proliferation in glioma patients.

Kai Zhao, Huiyu Huang, Eryuan Gao, Jinbo Qi, Ting Chen, Gaoyang Zhao, Guohua Zhao, Yu Zhang, Peipei Wang, Jie Bai and 4 more

Abstract read
In one paragraph

Article in Frontiers in oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
–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

8 citing papers in PubMed.

  1. Article
  2. Review
  3. Observational
  4. Article
  5. Review
  6. Article
  7. Article
  8. Article
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

14 authors.

Kai ZhaoDepartment of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Huiyu HuangDepartment of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Eryuan GaoDepartment of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Jinbo QiDepartment of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Ting ChenDepartment of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Gaoyang ZhaoDepartment of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Guohua ZhaoDepartment of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Yu ZhangDepartment of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Peipei WangDepartment of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Jie BaiDepartment of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Yong ZhangDepartment of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Zujun HouJiangsu Key Laboratory of Medical Optics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China.
Jingliang ChengDepartment of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Xiaoyue MaDepartment of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objectives: To investigate the clinical value of hemodynamic parameters derived from dynamic contrast-enhanced MRI (DCE-MRI) in predicting glioma genotypes including isocitrate dehydrogenase ( Materials and methods: Dynamic contrast-enhanced MRI (DCE-MRI) data of patients with glioma were prospectively enrolled from April 2021 to May 2023. The imaging data were analyzed using DP and Ex-Tofts model for evaluating the perfusion and permeability characteristics of glioma. Comparisons were performed according to Results: In Conclusion: DP model showed a clear advantage in predicting

Indexed as

1p/19q codeletiondistributed parameter modeldynamic contrast-enhanced MRIgliomaIDH mutationKi-67

Identifiers

PMID39525622
PMCPMC11544007

What OpenQuestion holds

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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.