Evidence map›Paper›PMID 40727330›Full record

ArticleQuantitative imaging in medicine and surgery2025

Evaluation of multiparametric MRI and clinical indicators for renal fibrosis in chronic kidney disease.

Chaogang Wei, Rong Liu, Xiaojing Li, Chunshan Zhou, Qing Ma, Yilin Xu, Ye Zhu, Junkang Shen, Ying Zeng, Kai Song and 1 more

Abstract read
In one paragraph

Article in Quantitative imaging in medicine and surgery, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
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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.

Chaogang Wei *Department of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou, China.
Rong Liu *Department of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou, China.
Xiaojing Li *Department of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou, China.
Chunshan ZhouDepartment of Radiology, The Fifth People's Hospital of Huai'an, Huai'an, China.
Qing MaDepartment of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou, China.
Yilin XuDepartment of Nephrology, The Second Affiliated Hospital of Soochow University, Suzhou, China.
Ye ZhuDepartment of Nephrology, The Second Affiliated Hospital of Soochow University, Suzhou, China.
Junkang ShenDepartment of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou, China.
Ying Zeng *Department of Nephrology, The Second Affiliated Hospital of Soochow University, Suzhou, China.
Kai Song *Department of Nephrology, The Second Affiliated Hospital of Soochow University, Suzhou, China.
Zhen Jiang *Department of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou, China.ORCID https://orcid.org/0009-0004-4971-7034

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Chronic kidney disease (CKD) is a major global health challenge, while renal fibrosis (RF) is the key pathological process and represents irreversible kidney damage. There is an urgent need for non-invasive techniques for assessment of RF. This study aimed to assess the diagnostic value of integrating native T Methods: A prospective analysis was conducted on 117 patients with a clinical diagnosis of CKD who were scheduled for renal biopsy and underwent multiparametric magnetic resonance imaging (MRI) (native T Results: The independent predictors for the presence of RF were the estimated glomerular filtration rate (eGFR), mean corticomedullary T Conclusions: Native T

Indexed as

chronic kidney disease (CKD)combined diagnosisMagnetic resonance imaging (MRI)quantitative imagingrenal fibrosis (RF)

Identifiers

PMID40727330
PMCPMC12290741

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Registered trials

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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.