Evidence map›Paper›PMID 31858206›Full record

ArticleEuropean radiology2020

Diffusion kurtosis imaging differs between primary central nervous system lymphoma and high-grade glioma and is correlated with the diverse nuclear-to-cytoplasmic ratio: a histopathologic, biopsy-based study.

Pang Haopeng, Dang Xuefei, Ren Yan, Yao Zhenwei, Huang Wei, Wang Ziyin, Shan Qungang, Li Chaojie, Yao Linyan, Wang Zhongmin and 1 more

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Article in European radiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
1.0field-weighted citation impact, top 22% 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

14 citing papers in PubMed, 19 citations in OpenAlex.

  1. Microstructure mapping with time-dependent diffusion MRI differentiates primary central nervous system lymphoma from glioblastoma.Cancer imaging : the official publication of the International Cancer Imaging Society · 2026
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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

11 authors at 3 institutions in 1 country.

Pang HaopengDepartment of Interventional Radiology, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, # 197 Rui Jin Er Road, Shanghai, 200025, People's Republic of China.
Dang XuefeiDepartment of Oncology, Minhang Branch of Fudan University Shanghai Cancer Center, # 106 Ruili Road, Shanghai, 200240, People's Republic of China.
Ren YanDepartment of Radiology, Affiliated Huashan Hospital of Fudan University, #12 Mid Urumqi Road, Shanghai, 200040, People's Republic of China.
Yao ZhenweiDepartment of Radiology, Affiliated Huashan Hospital of Fudan University, #12 Mid Urumqi Road, Shanghai, 200040, People's Republic of China.
Huang WeiDepartment of Interventional Radiology, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, # 197 Rui Jin Er Road, Shanghai, 200025, People's Republic of China.
Wang ZiyinDepartment of Interventional Radiology, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, # 197 Rui Jin Er Road, Shanghai, 200025, People's Republic of China.
Shan QungangDepartment of Interventional Radiology, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, # 197 Rui Jin Er Road, Shanghai, 200025, People's Republic of China.
Li ChaojieDepartment of Interventional Radiology, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, # 197 Rui Jin Er Road, Shanghai, 200025, People's Republic of China.
Yao LinyanDepartment of Interventional Radiology, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, # 197 Rui Jin Er Road, Shanghai, 200025, People's Republic of China.
Wang ZhongminDepartment of Interventional Radiology, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, # 197 Rui Jin Er Road, Shanghai, 200025, People's Republic of China. wzm0722@hotmail.com.ORCID http://orcid.org/0000-0001-7994-4538
Feng XiaoyuanDepartment of Radiology, Affiliated Huashan Hospital of Fudan University, #12 Mid Urumqi Road, Shanghai, 200040, People's Republic of China. xyfeng@shmu.edu.cn.
Shanghai Jiao Tong University · CNFudan University · CNFudan University Shanghai Cancer Center · CN

Funding

National Natural Science Foundation of China 81271632
6 · The paper itself

Abstract

objectivesTo determine whether water kurtosis and diffusional metrics derived from diffusional kurtosis imaging (DKI) within primary central nervous system lymphomas (PCNSLs) and high-grade gliomas (HGGs) correlate with cellularity and/or nuclear-to-cytoplasmic (N/C) ratio.

methodsForty-four and 43 pathologically confirmed high-grade glioma and primary central nervous system lymphoma specimens between May 2013 and November 2016 were retrospectively reviewed. Diffusional metrics, kurtosis metrics, cellularity, and N/C ratios in PCNSLs and HGGs were compared using the Mann-Whitney U test (significant level, p < 0.007 [0.05/7]); Bonferroni correction).

resultsMean kurtosis (MK), axial kurtosis (K

conclusionsDifference of kurtosis parameters between PCNSLs and HGGs is correlated with their diverse N/C ratio. KEY POINTS: • DKI has considerable value in differentiating between PCNSLs and HGGs. • DKI can provide important information on nuclear-to-cytoplasmic ratio. • Difference of kurtosis parameters between PCNSLs and HGGs correlated well with their diverse N/C ratios.

Indexed as

AdultAgedBiopsyBrain NeoplasmsCentral Nervous System NeoplasmsDiagnosis, DifferentialDiffusion Magnetic Resonance ImagingFemaleGliomaHumansLymphoma, Non-HodgkinMaleMiddle AgedNeoplasm StagingRetrospective StudiesYoung AdultBiopsyCentral nervous systemDiffusion kurtosis imagingGliomaLymphoma

Identifiers

PMID31858206
OpenAlexW2995491023

What OpenQuestion holds

Textmetadata
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.