Evidence map›Paper›PMID 42226306›Full record

ArticleCancer imaging : the official publication of the International Cancer Imaging Society2026

Microstructure mapping with time-dependent diffusion MRI differentiates primary central nervous system lymphoma from glioblastoma.

Jun Wu, Jue Lu, Xinli Zhang, Xiaotong Guo, Qian Qin, Jiaqi Chen, Ning Zheng, Peng Sun, Xuan Gao, Jing Wang

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Cancer imaging : the official publication of the International Cancer Imaging Society, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
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0citing papers in PubMed
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1 · What the graph read from it

What it found

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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

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

10 authors.

Jun Wu *Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.
Jue Lu *Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.
Xinli ZhangDepartment of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.
Xiaotong GuoDepartment of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.
Qian QinDepartment of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.
Jiaqi ChenDepartment of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.
Ning ZhengMSC Clinical & Technical Solutions, Philips Healthcare, Wuhan, China.
Peng SunInstitute of Research and Clinical Innovations, Neusoft Medical Systems Co. Ltd, Shenyang, 110167, China.
Xuan Gao *Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China. gaoxuan@hust.edu.cn.
Jing Wang *Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China. xhwangjing@hust.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundGlioblastoma (GBM) and primary central nervous system lymphoma (PCNSL) are two distinct malignant brain tumors, and precise preoperative differentiation is crucial for guiding optimal treatments. This study aimed to evaluate the diagnostic value of time-dependent diffusion MRI (t

methodsThis study included 32 GBM and 19 PCNSL patients who underwent 3.0-T MRI with oscillating gradient spin-echo (OGSE) and pulsed gradient spin-echo (PGSE) sequences. Microstructural parameters [intracellular volume fraction (V

resultsIn enhancing tumor regions, PCNSL exhibited significantly smaller cell diameter and lower D

conclusionAmong all t

Indexed as

Brain NeoplasmsCentral Nervous System NeoplasmsDiffusion Magnetic Resonance ImagingGlioblastomaLymphomaAdultAgedDiagnosis, DifferentialFemaleHumansMaleMiddle AgedGlioblastomaMicrostructurePrimary central nervous system lymphomaTime-dependent diffusion MRI

Identifiers

PMID42226306
PMCPMC13270852

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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.