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ArticleEuropean radiology2026

Multi-institutional survey on abdominopelvic applications of MR elastography.

Liang Zhu, Haibin Zhu, Jinhao Lyu, Shaozhen Yan, Han Lv, Dong Li, Xuan Wang, Jin Cheng, Feng Feng, Nan Hong

Abstract readMulticenter Study
PubMed Publisher
In one paragraph

Article in European radiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. A U-Net Transformer for magnetic resonance elastography.International journal of computer assisted radiology and surgery · 2026
    Article
  2. Article
  3. Compact Tabletop Magnetic Resonance Elastography for Mapping Soft Tissue Viscoelasticity.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
    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

10 authors.

Liang Zhu *Department of Radiology, Peking Union Medical College Hospital, Beijing, China.
Haibin Zhu *Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiology, Peking University Cancer Hospital & Institute, Beijing, China.
Jinhao Lyu *Department of Radiology, Chinese PLA General Hospital, Beijing, China.
Shaozhen Yan *Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, Beijing, China.
Han Lv *Department of Radiology, Beijing Friendship Hospital, Beijing, China.
Dong Li *Department of Radiology, Tianjin Medical University General Hospital, Tianjin, China.
Xuan WangDepartment of Radiology, Peking Union Medical College Hospital, Beijing, China.
Jin ChengDepartment of Radiology, Peking University People's Hospital, Beijing, China. chengjinpkuph@outlook.com.ORCID http://orcid.org/0009-0006-5744-8131
Feng FengDepartment of Radiology, Peking Union Medical College Hospital, Beijing, China.
Nan HongDepartment of Radiology, Peking University People's Hospital, Beijing, China.

Funding

Education Fund of Peking Union Medical College 2024KCSZ001National Natural Science Foundation of China 82371950
6 · The paper itself

Abstract

objectivesTo investigate the knowledge and expectations of abdominopelvic MR elastography (MRE) among radiologists from multiple institutions, and to rate the image quality of MRE maps. MATERIAL AND

methodsRadiologists from Beijing and Tianjin were invited to participate in an online survey. Full MRE maps of the uterus, prostate, pancreas, liver, and kidney from 93 research papers published between 2017 and 2024 were displayed for image quality rating, blinded to the MRE systems. Before and after the image-review session, the participants' knowledge and expectations about abdominopelvic MRE were investigated.

resultsEighty-one radiologists finalized the survey, all but one from tertiary hospitals. Their knowledge of MRE mainly came from the literature and conferences, and only 29.6% had hands-on experience. Additional setup and scanning time, as well as patient safety and comfort, were the major concerns that may hinder MRE application. Parametric maps from two MRE systems were reviewed, and the pressurized air (PA) system exhibited consistently higher subjective image quality for all five abdominopelvic organs compared to the acoustic driver (AD) system (all p < 0.05). The participants' knowledge about MRE improved after the image-review session, whereas their expectation declined regarding its utility in abdominopelvic tumor detection, characterization, staging, and treatment monitoring. Technical unfamiliarity with MRE was high, with ignorance rates ranging from 42.0% to 64.2%.

conclusionThere was limited knowledge and availability of MRE even in tertiary hospitals in China's capital region. Currently available MRE systems offer varying levels of anatomical details, and the PA system might be more advantageous for applications in abdominopelvic oncology. KEY POINTS: Question The radiologists' current knowledge and expectations of abdominopelvic MRE remained poorly investigated, and the impact of MRE image quality was unknown. Findings There was limited knowledge and availability of MRE in China's capital region, and MRE systems didn't consistently achieve sufficient image quality for abdominopelvic oncology. Clinical relevance Although MRE holds promise for abdominopelvic oncology, its availability and familiarity among radiologists remain low, and the image quality of MRE maps is sometimes insufficient. System optimization and targeted education are essential for future integration into clinical practice.

Indexed as

AbdomenElasticity Imaging TechniquesMagnetic Resonance ImagingChinaClinical CompetenceFemaleHumansMalePelvisRadiologistsSurveys and QuestionnairesAbdomenImage qualityMagnetic resonance elastographyOncologyPelvis

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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.