Evidence map›Paper›PMID 40781513›Full record

ArticleEuropean radiology2026

Digital pathology and lipid droplet size as a key determinant of discrepancies between histology and MRI gradings in steatotic liver disease.

David Marti-Aguado, Clara Alfaro-Cervello, Matías Fernández-Patón, Amadeo Ten-Esteve, Leonor Cerdá-Alberich, Ana Crespo, Irene Navarrete-Pérez, María Pilar Ballester, Alexandre Perez-Girbes, Cristina Montón and 10 more

Abstract readMulticenter Study
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 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

20 authors.

David Marti-Aguado *Digestive Disease Department, Clinic University Hospital, INCLIVA Health Research Institute, Valencia, Spain. david_marti_aguado@hotmail.com.ORCID http://orcid.org/0000-0002-4266-6076
Clara Alfaro-Cervello *University of Valencia, Faculty of Medicine, Valencia, Spain.
Matías Fernández-PatónBiomedical Imaging Research Group (GIBI230) La Fe Health Research Institute, Valencia, Spain.
Amadeo Ten-EsteveBiomedical Imaging Research Group (GIBI230) La Fe Health Research Institute, Valencia, Spain.
Leonor Cerdá-AlberichBiomedical Imaging Research Group (GIBI230) La Fe Health Research Institute, Valencia, Spain.
Ana CrespoDigestive Disease Department, Hospital Arnau de Vilanova, Valencia, Spain.
Irene Navarrete-PérezDigestive Disease Department, La Fe University and Polytechnic Hospital, Valencia, Spain.
María Pilar BallesterDigestive Disease Department, Clinic University Hospital, INCLIVA Health Research Institute, Valencia, Spain.
Alexandre Perez-GirbesRadiology Department, La Fe University and Polytechnic Hospital, Valencia, Spain.
Cristina MontónDigestive Disease Department, Clinic University Hospital, INCLIVA Health Research Institute, Valencia, Spain.
Judith Pérez-RojasPathology Department, La Fe University and Polytechnic Hospital, Valencia, Spain.
Víctor PugliaPathology Department, Hospital Arnau de Vilanova, Valencia, Spain.
Antonio FerrándezUniversity of Valencia, Faculty of Medicine, Valencia, Spain.
Victoria AguileraCIBERehd, Centro de Investigación Biomédica en Red en Enfermedades Hepáticas y Digestivas, Instituto de Salud Carlos III, Madrid, Spain.
Desamparados Escudero-GarcíaDigestive Disease Department, Clinic University Hospital, INCLIVA Health Research Institute, Valencia, Spain.
Salvador BenllochDigestive Disease Department, Hospital Arnau de Vilanova, Valencia, Spain.
Ana Jimenez-PastorQuantitative Imaging Biomarkers in Medicine, QUIBIM SL, Valencia, Spain.
Ángel Alberich-BayarriQuantitative Imaging Biomarkers in Medicine, QUIBIM SL, Valencia, Spain.
Claude B SirlinLiver Imaging Group, Department of Radiology, University of California San Diego, La Jolla, CA, USA.
Luis Marti-BonmatiBiomedical Imaging Research Group (GIBI230) La Fe Health Research Institute, Valencia, Spain.

Funding

Conselleria de Innovación, Universidades, Ciencia y Sociedad Digital, Generalitat Valenciana CIGE/2022/37
6 · The paper itself

Abstract

backgroundHepatic steatosis grades derived from magnetic resonance imaging proton density fat fraction (MRI-PDFF) might disagree with those determined by histology. We investigated whether the size distribution of lipid droplets (LDs) assessed with digital image analysis (DIA) explains the discrepancies between histology and MRI-PDFF. MATERIALS AND

methodsMulticentric, prospective study of 355 patients with chronic liver disease, having paired biopsy and MRI. Using conventional microscopy, steatosis was graded by pathologists (S0-S3), based on the proportion of hepatocytes containing large LDs. MRI-PDFF graded steatosis using validated thresholds (PDFF-S1 ≥ 5.75%, PDFF-S2 ≥ 15.5%, PDFF-S3 ≥ 21.35%). DIA categorized LDs into tiny (< 1 μm

resultsHistology- and PDFF-derived steatosis grades were discordant in 36%. Disagreement was associated with higher proportions of tiny and small LDs, and lower content of large LDs. Within histology-derived S0, disagreements were due to MRI overestimation having a higher content of tiny and small LDs. Within histology-derived S2-S3, disagreements were due to MRI underestimation, having a lower content of large and total LDs. Total LD proportionate area strongly correlated with MRI-PDFF (r = 0.89). DIA showed that as steatosis accumulates, the size of LDs progressively increases.

conclusionTaking DIA as ground truth, the size distribution of LDs explains the discrepancies between histology and MRI-PDFF steatosis gradings. Compared to histology, MRI-PDFF overestimation is due to abundant tiny-small LDs, and underestimation is due to lower content of large LDs. These results indicate that MRI captures the whole spectrum of LD size, avoiding the conventional histology subjective assessment bias. KEY POINTS: Question Can the size distribution of hepatic lipid droplets (LDs), determined with digital pathology, provide insights into the discordances between histology and MRI-PDFF steatosis grading? Findings There is a 36% disagreement rate between histology and MRI-PDFF. Tiny and small LDs explain discordance, while large LDs favor concordance between both techniques. Clinical relevance MRI-PDFF captures the whole spectrum of LDs size beyond pathologists scoring system and strongly correlates with LDs total content avoiding conventional histology bias.

Indexed as

Fatty LiverLipid DropletsMagnetic Resonance ImagingAdultAgedBiopsyFemaleHumansLiverMaleMiddle AgedProspective StudiesDigital image analysisHepatic steatosisLipid dropletsMagnetic resonance imagingProton density fat fraction

Identifiers

PMID40781513
PMCPMC12953288

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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.