Evidence map›Paper›PMID 39638942›Full record

Trial reportEuropean radiology2025

Head-to-head comparison among FAST, MAST, and multiparametric MRI-based new score in diagnosing at-risk MASH.

Kento Imajo, Yusuke Saigusa, Takashi Kobayashi, Koki Nagai, Shinya Nishida, Nobuyoshi Kawamura, Hiroyoshi Doi, Michihiro Iwaki, Asako Nogami, Yasushi Honda and 14 more

Abstract readClinical TrialComparative Study
PubMed Publisher
In one paragraph

Trial report in European radiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 pooled it
–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 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  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

24 authors.

Kento ImajoDepartment of Gastroenterology and Hepatology, Yokohama City University Graduate School of Medicine, Yokohama, Japan.
Yusuke SaigusaDepartment of Biostatistics, Yokohama City University School of Medicine, Kanazawa-ku, Japan.
Takashi KobayashiDepartment of Gastroenterology and Hepatology, Yokohama City University Graduate School of Medicine, Yokohama, Japan.
Koki NagaiDepartment of Gastroenterology, Shin-yurigaoka General Hospital, Kawasaki, Japan.
Shinya NishidaDepartment of Gastroenterology, Shin-yurigaoka General Hospital, Kawasaki, Japan.
Nobuyoshi KawamuraDepartment of Gastroenterology, Shin-yurigaoka General Hospital, Kawasaki, Japan.
Hiroyoshi DoiDepartment of Gastroenterology, Shin-yurigaoka General Hospital, Kawasaki, Japan.
Michihiro IwakiDepartment of Gastroenterology and Hepatology, Yokohama City University Graduate School of Medicine, Yokohama, Japan.
Asako NogamiDepartment of Gastroenterology and Hepatology, Yokohama City University Graduate School of Medicine, Yokohama, Japan.
Yasushi HondaDepartment of Gastroenterology and Hepatology, Yokohama City University Graduate School of Medicine, Yokohama, Japan.
Takaomi KessokuDepartment of Gastroenterology and Hepatology, Yokohama City University Graduate School of Medicine, Yokohama, Japan.
Yuji OgawaDepartment of Gastroenterology, National Hospital Organization Yokohama Medical Center, Yokohama, Japan.
Hiroyuki KirikoshiDepartment of Clinical Laboratory, Yokohama City University Hospital, Yokohama, Japan.
Shigehiro KokubuDepartment of Gastroenterology, Shin-yurigaoka General Hospital, Kawasaki, Japan.
Daisuke UtsunomiyaDepartment of Radiology, Yokohama City University Graduate School of Medicine, Yokohama, Japan.
Hirokazu TakahashiDivision of Metabolism and Endocrinology, Faculty of Medicine, Saga University, Saga, Japan.
Shinichi AishimaDepartment of Pathology & Microbiology, Faculty of Medicine, Saga University, Saga, Japan.
Yoshio SumidaGraduate School of Healthcare Management, International University of Healthcare and Welfare, Minato-ku, Japan.
Satoru SaitoDepartment of Gastroenterology and Hepatology, Yokohama City University Graduate School of Medicine, Yokohama, Japan.
Masato YonedaDepartment of Gastroenterology and Hepatology, Yokohama City University Graduate School of Medicine, Yokohama, Japan.
Andrea DennisPerspectum Ltd, Gemini One, Oxford, UK.
Stella KinPerspectum Ltd, Gemini One, Oxford, UK.
Anneli AnderssonPerspectum Ltd, Gemini One, Oxford, UK.
Atsushi NakajimaDepartment of Gastroenterology and Hepatology, Yokohama City University Graduate School of Medicine, Yokohama, Japan. nakajima-tky@umin.ac.jp.ORCID http://orcid.org/0000-0002-6263-1436

Funding

Japan Society for the Promotion of Science London JP 21K07987
6 · The paper itself

Abstract

objectivesNew scores were developed to identify at-risk metabolic dysfunction-associated steatohepatitis (MASH) using multiparametric MRI (mpMRI). MATERIALS AND

methodsA prospective study was conducted on 176 patients with suspected or diagnosed metabolic dysfunction-associated steatotic liver disease (MASLD) paired with an MR scan, vibration-controlled transient elastography (VCTE), and liver biopsy. Liver stiffness measurement (LSM) using magnetic resonance elastography (MRE), proton density fat fraction (PDFF), and mpMRI-based corrected T1 (cT1) were combined to develop a one-step strategy, named MPcT (MRE + PDFF + cT1, combined score), and a two-step strategy-MRE-based LSM followed by PDFF with cT1 (M-PcT, paired score) for diagnosing at-risk MASH. Each model was categorized using rule-in and rule-out criteria (three categorized analyses). To avoid overfitting, the diagnostic accuracies were evaluated based on 5-fold cross-validation.

resultsPDFF + cT1 (PcT) had the highest diagnostic performance for severe activity (hepatic inflammation plus ballooning grade ≥ 3) and for NAS ≥ 4 (active MASH). Areas under receiver operating characteristic curves (AUROCs) of M-PcT (0.832) for detecting at-risk MASH were significantly higher than those of Fibroscan-AST (FAST) (0.744, p = 0.017), MRI-AST (MAST) (0.710, p = 0.002), and MPcT (0.695, p < 0.001) in three categorized analysis. Following the rule-in criteria, positive predictive values of M-PcT (84.5%) were higher than those of FAST (73.5%), MAST (70.0%), and MPcT (66.7%). Following the rule-out criteria, negative predictive values of M-PcT (88.7%) were higher than those of FAST (84.0%), MAST (73.9%), and MPcT (84.9%).

conclusionsThe two-step strategy, M-PcT (paired score), showed the reliability of rule-in/-out for at-risk MASH, with better predictive performance compared with FAST and MAST (combined score). CLINICAL

trial registrationThis study is registered with ClinicalTrials.gov (number, UMIN000012757). KEY POINTS: Question There is no mpMRI-based method for detecting as-risk MASH (NAFLD activity score ≥ 4 with fibrosis stage ≥ 2) like FAST and MAST scores. Findings MRE-based LSMs followed by PDFF with cT1 (M-PcT) were more useful in detecting at-risk MASH than the combined score (FAST and MAST). Clinical relevance By combining MRE and PDFF with cT1, it becomes possible to evaluate the pathology of MASH without the need for a liver biopsy, assisting in prognosis prediction and decision-making for treatment options.

Indexed as

Fatty LiverMagnetic Resonance ImagingMultiparametric Magnetic Resonance ImagingAdultAgedBiopsyElasticity Imaging TechniquesFemaleHumansLiverMaleMiddle AgedProspective StudiesReproducibility of ResultsSensitivity and SpecificityAt-risk MASHFASTMASTM-PcTMPcT

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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.