Evidence map›Paper›PMID 39990836›Full record

ReviewSichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition2024

[Clincial Research Progress in Using Magnetic Resonance Imaging to Assess Myocardial Fibrosis in Hypertrophic Cardiomyopathy].

Ke Shi, Shiqin Yu, Dong Xia, Yingkun Guo, Zhigang Yang

Abstract readReviewEnglish Abstract
In one paragraph

Review in Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

5 authors.

Ke Shi( 610041) Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, China.
Shiqin Yu( 610041) Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, China.
Dong Xia( 610041) Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, China.
Yingkun Guo( 610041) Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, China.
Zhigang Yang( 610041) Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Hypertrophic cardiomyopathy (HCM) is the most common type of primary cardiomyopathy that causes sudden cardiac death in adolescents and athletes. With over 1 million HCM patients, China has the largest population of HCM patients in the world, and the total number of cases is increasing year on year. Myocardial fibrosis is the most important histopathological characterization in HCM and is regarded as the primary cause of malignant ventricular arrhythmia, cardiac remodeling, and heart failure. At present, cardiac magnetic resonance imaging (MRI) serves as the gold-standard imaging modality for noninvasive evaluation of myocardial fibrosis. Several techniques, such as late gadolinium enhancement and T1 mapping, are showing considerable promise for potential applications. These techniques have emerged as viable imaging approaches to the elucidation of HCM tissue characterization. They are also helpful in predicting the long-term prognosis of patients. Herein, we summarized recent advances in using cardiac MRI to assess myocardial fibrosis in HCM from four perspectives, including late gadolinium enhancement, T1 mapping, T1ρ mapping, and MRI-based radiomics and machine learning models.

Indexed as

Cardiomyopathy, HypertrophicMagnetic Resonance ImagingMyocardiumContrast MediaFibrosisGadoliniumHumansMachine LearningContrast MediaGadoliniumHypertrophic cardiomyopathyMagnetic resonance imagingMyocardial fibrosisReview

Identifiers

PMID39990836
PMCPMC11839347

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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.