Evidence map›Paper›PMID 33824869›Full record

ArticleKidney diseases (Basel, Switzerland)2021

Evaluation of Renal Fibrosis by Mapping Histology and Magnetic Resonance Imaging.

Jiong Zhang, Yuanmeng Yu, Xiaoshuang Liu, Xiong Tang, Feng Xu, Mingchao Zhang, Guotong Xie, Longjiang Zhang, Xiang Li, Zhi-Hong Liu

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Kidney diseases (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed, 1 pooled it
2.5field-weighted citation impact, top 11% 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

19 citing papers in PubMed, 1 synthesis or guideline pooled it, 27 citations in OpenAlex.

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  19. Diffusion-Weighted MRI in the Genitourinary System.Journal of clinical medicine · 2022
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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

10 authors at 4 institutions in 1 country.

Jiong ZhangNational Clinical Research Center of Kidney Diseases, Jinling Hospital, Second Military Medical University, Nanjing, China.
Yuanmeng YuDepartment of Medical Imaging, Jinling Hospital, Nanjing University School of Medicine, Nanjing, China.
Xiaoshuang LiuPing An Health Technology, Beijing, China.
Xiong TangNational Clinical Research Center of Kidney Diseases, Jinling Hospital, Second Military Medical University, Nanjing, China.
Feng XuNational Clinical Research Center of Kidney Diseases, Jinling Hospital, Second Military Medical University, Nanjing, China.
Mingchao ZhangNational Clinical Research Center of Kidney Diseases, Jinling Hospital, Second Military Medical University, Nanjing, China.
Guotong XiePing An Healthcare Technology, Ping An Health Cloud Company Limited, Ping An International Smart City Technology Co., Ltd., Beijing, China.
Longjiang ZhangDepartment of Medical Imaging, Jinling Hospital, Nanjing University School of Medicine, Nanjing, China.
Xiang LiPing An Health Technology, Beijing, China.
Zhi-Hong LiuNational Clinical Research Center of Kidney Diseases, Jinling Hospital, Second Military Medical University, Nanjing, China.
Ping An (China)Second Military Medical University · CNNanjing General Hospital of Nanjing Military Command · CNSecond Affiliated Hospital of Nanjing Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundRenal fibrosis is a key driver of progression in chronic kidney disease (CKD). Recent advances in diagnostic imaging techniques have shown promising results for the noninvasive assessment of renal fibrosis. However, the specificity and accuracy of these techniques are controversial because they indirectly assess renal fibrosis. This limits fibrosis assessment by imaging in CKD for clinical practice. To validate magnetic resonance imaging (MRI) assessment for fibrosis, we derived representative models by mapping histology-proven renal fibrosis and imaging in CKD.

methodsNinety-seven adult Chinese CKD participants with histology were studied. The kidney cortex interstitial extracellular matrix volume was calculated by the Aperio ScanScope system using Masson's trichrome slices. The kidney cortex microcirculation was quantitatively assessed by peritubular capillary density using CD34 staining. The imaging techniques included intravoxel incoherent motion diffusion-weighted imaging and magnetic resonance elastography (MRE) imaging. Relevant analyses were performed to evaluate the correlations between MRI parameters and histology variables. Multiple linear regression models were used to describe the relationships between a response variable and other variables. The best-fit lines, which minimize the sum of squared residuals of the multiple linear regression models, were generated.

resultsMRE values were negatively associated with the interstitial extracellular matrix volume (

conclusionsThe study provides histological evidence to support that MRI can effectively evaluate fibrosis in the kidney. These findings picture the graphs of the mapping model from imaging and eGFR into fibrosis, which has significant value for clinical implementation.

Indexed as

Chronic kidney diseaseDiffusionExtracellular matrixMagnetic resonance elastographyPeritubular capillariesWeighted imaging

Identifiers

PMID33824869
PMCPMC8010230
OpenAlexW3131009867

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.