Evidence map›Paper›PMID 41604115›Full record

ArticleInsights into imaging2026

Assessment of MRI susceptibility-weighted imaging-based liver-to-muscle signal intensity ratios for the staging of liver fibrosis.

Xuan Jin, Yufan Ren, Xuchang Zhang, Haojun Lu, Jiaqi Lv, Tianyuan Zhang, Wen Liang, Yongzhou Xu, Qing Yu, Xianyue Quan and 1 more

Abstract read
In one paragraph

Article in Insights into imaging, 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
0cells of the map it votes in
0citing 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

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

11 authors.

Xuan Jin *Department of Radiology, The Fourth Affiliated Hospital of School of Medicine, and International School of Medicine, International Institutes of Medicine, Zhejiang University, Yiwu, China.
Yufan Ren *Department of Radiology, Zhujiang Hospital, Southern Medical University, Guangzhou, China.
Xuchang Zhang *Department of Radiology, Zhujiang Hospital, Southern Medical University, Guangzhou, China.
Haojun LuDepartment of Radiology, Zhujiang Hospital, Southern Medical University, Guangzhou, China.
Jiaqi LvDepartment of Radiology, Zhujiang Hospital, Southern Medical University, Guangzhou, China.
Tianyuan ZhangMedical Imaging Center, The First People's Hospital of Foshan, Foshan, China.
Wen LiangDepartment of Radiology, Zhujiang Hospital, Southern Medical University, Guangzhou, China.
Yongzhou XuPhilips Healthcare, Guangzhou, China.
Qing YuDepartment of Radiology, Zhujiang Hospital, Southern Medical University, Guangzhou, China. liudehuayuqing@163.com.
Xianyue QuanDepartment of Radiology, Zhujiang Hospital, Southern Medical University, Guangzhou, China. quanxianyue2014@163.com.ORCID http://orcid.org/0000-0003-2293-9345
Xinming LiDepartment of Radiology, Zhujiang Hospital, Southern Medical University, Guangzhou, China. lixinmingsmu@163.com.

Funding

Basic and Applied Basic Research Foundation of Guangdong Province 2024A1515012170, 2023A1515012242Clinical Research Startup Program of Southern Medical University by High-level University Construction Funding of Guangdong Provincial Department of Education LC2016PY034Natural Science Foundation of China 82370674, 82302315
6 · The paper itself

Abstract

objectiveTo investigate the feasibility of susceptibility-weighted imaging (SWI) for the diagnosis of different stages of liver fibrosis, and to assess its diagnostic accuracy compared with the serum fibrosis index commonly used in clinical settings. MATERIALS AND

methodsThis prospective study included 108 patients and 16 healthy volunteers. All patients underwent MRI with SWI and histopathological evaluation. Liver and bilateral erector spinae signal intensities were measured on SWI to calculate liver-to-muscle signal intensity ratios (SIR). Serological biomarkers were collected to calculate the aspartate aminotransferase-to-platelet ratio index (APRI) and fibrosis index based on four factors (FIB-4). Histological correlation analysis between the SIR and liver fibrosis/iron deposition was performed using Spearman's rank correlation analysis. The diagnostic accuracies of SIR, APRI, and FIB-4 for staging liver fibrosis were assessed, and their performances were compared using the DeLong test.

resultsReceiver operating characteristic (ROC) curve analysis showed good-to-excellent diagnostic performance of SIR for different stages of liver fibrosis. The areas under the curve (AUC) of SIR for the diagnosis of liver fibrosis stages S0 vs S1-S4, S0-S1 vs S2-S4, S0-S2 vs S3-S4, and S0-S3 vs S4 were 0.851, 0.868, 0.872, and 0.931. Delong's test showed that the SIR outperformed the APRI and FIB-4 in the diagnosis of liver fibrosis S0-S1 vs S2-S4, S0-S2 vs S3-S4, and S0-S3 vs S4 (p = 0.011-0.036).

conclusionSWI-based SIR outperforms the serum indicators APRI and FIB-4 in diagnosing liver fibrosis of S0-S1 vs S2-S4, S0-S2 vs S3-S4, and S0-S3 vs S4. CRITICAL RELEVANCE STATEMENT: SWI-based SIR offers a new perspective on non-invasive diagnostic methods to guide the clinical diagnosis of liver fibrosis, particularly in cases where biopsy is contraindicated or impractical. KEY POINTS: Searching for a non-invasive method to accurately diagnose stages of liver fibrosis is necessary because of the limitations of histopathological evaluation. SWI offers a dependable and non-invasive diagnostic approach for evaluating different stages of liver fibrosis compared to serological biomarkers. SWI-based SIR provides a highly accurate, non-invasive alternative to serum biomarkers for detecting advanced liver fibrosis.

Indexed as

Liver fibrosisMagnetic resonance imagingSerological biomarkersSusceptibility-weighted imaging

Identifiers

PMID41604115
PMCPMC12852556

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.