Evidence map›Paper›PMID 36359519›Full record

ArticleDiagnostics (Basel, Switzerland)2022

Predicting Progression of Kidney Injury Based on Elastography Ultrasound and Radiomics Signatures.

Minyan Zhu, Lumin Tang, Wenqi Yang, Yao Xu, Xiajing Che, Yin Zhou, Xinghua Shao, Wenyan Zhou, Minfang Zhang, Guanghan Li and 4 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Diagnostics (Basel, Switzerland), 2022. 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
  2. Review
  3. Review
  4. 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.

Minyan ZhuMolecular Cell Laboratory for Kidney Disease, Department of Nephrology, Shanghai Peritoneal Dialysis Research Center, Uremia Diagnosis and Treatment Center, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, China.ORCID 0000-0002-1868-3896
Lumin TangMolecular Cell Laboratory for Kidney Disease, Department of Nephrology, Shanghai Peritoneal Dialysis Research Center, Uremia Diagnosis and Treatment Center, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, China.
Wenqi YangSchool of Medicine, Department of Ultrasound, Renji Hospital, Shanghai Jiao Tong University, Shanghai 200127, China.
Yao XuMolecular Cell Laboratory for Kidney Disease, Department of Nephrology, Shanghai Peritoneal Dialysis Research Center, Uremia Diagnosis and Treatment Center, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, China.
Xiajing CheMolecular Cell Laboratory for Kidney Disease, Department of Nephrology, Shanghai Peritoneal Dialysis Research Center, Uremia Diagnosis and Treatment Center, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, China.
Yin ZhouMolecular Cell Laboratory for Kidney Disease, Department of Nephrology, Shanghai Peritoneal Dialysis Research Center, Uremia Diagnosis and Treatment Center, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, China.
Xinghua ShaoMolecular Cell Laboratory for Kidney Disease, Department of Nephrology, Shanghai Peritoneal Dialysis Research Center, Uremia Diagnosis and Treatment Center, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, China.
Wenyan ZhouMolecular Cell Laboratory for Kidney Disease, Department of Nephrology, Shanghai Peritoneal Dialysis Research Center, Uremia Diagnosis and Treatment Center, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, China.
Minfang ZhangMolecular Cell Laboratory for Kidney Disease, Department of Nephrology, Shanghai Peritoneal Dialysis Research Center, Uremia Diagnosis and Treatment Center, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, China.
Guanghan LiChina-Japan Friendship Hospital, Department of Ultrasound, Beijing 100029, China.
Min ZhengChina-Japan Friendship Hospital, Department of Ultrasound, Beijing 100029, China.
Qin WangMolecular Cell Laboratory for Kidney Disease, Department of Nephrology, Shanghai Peritoneal Dialysis Research Center, Uremia Diagnosis and Treatment Center, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, China.
Hongli LiSchool of Medicine, Department of Ultrasound, Renji Hospital, Shanghai Jiao Tong University, Shanghai 200127, China.
Shan MouMolecular Cell Laboratory for Kidney Disease, Department of Nephrology, Shanghai Peritoneal Dialysis Research Center, Uremia Diagnosis and Treatment Center, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Shear wave elastography ultrasound (SWE) is an emerging non-invasive candidate for assessing kidney stiffness. However, its prognostic value regarding kidney injury is unclear. Methods: A prospective cohort was created from kidney biopsy patients in our hospital from May 2019 to June 2020. The primary outcome was the initiation of renal replacement therapy or death, while the secondary outcome was eGFR < 60 mL/min/1.73 m2. Ultrasound, biochemical, and biopsy examinations were performed on the same day. Radiomics signatures were extracted from the SWE images. Results: In total, 187 patients were included and followed up for 24.57 ± 5.52 months. The median SWE value of the left kidney cortex (L_C_median) is an independent risk factor for kidney prognosis for stage 3 or over (HR 0.890 (0.796−0.994), p < 0.05). The inclusion of 9 out of 2511 extracted radiomics signatures improved the prognostic performance of the Cox regression models containing the SWE and the traditional index (chi-square test, p < 0.001). The traditional Cox regression model had a c-index of 0.9051 (0.8460−0.9196), which was no worse than the machine learning models, Support Vector Machine (SVM), SurvivalTree, Random survival forest (RSF), Coxboost, and Deepsurv. Conclusions: SWE can predict kidney injury progression with an improved performance by radiomics and Cox regression modeling.

Indexed as

kidney prognosismachine learningregression analysistissue elasticity imaging

Identifiers

PMID36359519
PMCPMC9689562

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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.