Evidence map›Paper›PMID 42477328›Full record

ArticleNature communications2026

A precision-constrained framework for evaluating noninvasive biomarkers in MASLD and beyond.

Guangyi Zhang, Xiaohong Wang, Arinc Ozturk, Eugene Cheah, Peng Guo, Marian Martin, Angela Shih, Atul K Bhan, Nancy Obuchowski, Amon Asgharpour and 4 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Nature communications, 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. Review
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

14 authors.

Guangyi Zhang *Center for Ultrasound Research & Translation, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.ORCID https://orcid.org/0000-0003-2356-0687
Xiaohong Wang *Center for Ultrasound Research & Translation, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.ORCID http://orcid.org/0000-0003-0924-9892
Arinc OzturkCenter for Ultrasound Research & Translation, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.ORCID http://orcid.org/0000-0002-5335-3737
Eugene CheahCenter for Ultrasound Research & Translation, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
Peng GuoCenter for Ultrasound Research & Translation, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
Marian MartinFaculty of Neurology, The Saul R. Korey Department of Neurology, Albert Einstein College of Medicine, Bronx, NY, USA.
Angela ShihDepartment of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.ORCID http://orcid.org/0000-0002-8945-5997
Atul K BhanDepartment of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
Nancy ObuchowskiQuantitative Health Sciences, Cleveland Clinic, Cleveland, OH, USA.
Amon AsgharpourStravitz-Sanyal Institute for Liver Disease and Metabolic Health and Division of Gastroenterology, Hepatology and Nutrition, Virginia Commonwealth University School of Medicine, Richmond, VA, USA.
Arun J SanyalStravitz-Sanyal Institute for Liver Disease and Metabolic Health and Division of Gastroenterology, Hepatology and Nutrition, Virginia Commonwealth University School of Medicine, Richmond, VA, USA.ORCID http://orcid.org/0000-0001-8682-5748
Brian A TelferLincoln Laboratory, Massachusetts Institute of Technology, Lexington, MA, USA.ORCID http://orcid.org/0000-0002-3534-7149
Theodore T PierceCenter for Ultrasound Research & Translation, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.ORCID http://orcid.org/0000-0003-3244-0994
Anthony E SamirCenter for Ultrasound Research & Translation, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA. asamir@mgh.harvard.edu.ORCID http://orcid.org/0000-0002-7801-8724

Funding

Development of a Machine Learning Model to Integrate Clinical, Laboratory, Sonographic, and Elastographic Data for Noninvasive Liver Tissue Characterization in NAFLDR01DK119860 · NIDDK · MASSACHUSETTS GENERAL HOSPITAL · PI SAMIR, ANTHONY EDWARD · 2019 to 2023
$2.3M
NIDDK NIH HHS R01 DK119860U.S. Department of Health & Human Services | NIH | National Institute of Diabetes and Digestive and Kidney Diseases (National Institute of Diabetes & Digestive & Kidney Diseases) R01 DK 119860
6 · The paper itself

Abstract

In patients with Metabolic Dysfunction-associated Steatotic Liver Disease (MASLD), accurate and low-cost non-invasive risk stratification remains a major unmet need. We developed a clinical records-based neural network integrating patient history, routine laboratory tests, and ultrasound imaging features. Here we show that in an internal test set (n = 209), the model achieved receiver operating characteristic area under the curve (ROC-AUC) values of 0.85 vs 0.82 (F ≥2), 0.90 vs 0.86 (F ≥3), 0.96 vs 0.89 (F  = 4) compared with Fibrosis-4 (FIB-4). To address limitations of ROC-AUC, we applied RP-AUC

Indexed as

BiomarkersFatty LiverNon-alcoholic Fatty Liver DiseaseArea Under CurveBiopsyHumansLiverLiver CirrhosisNeural Networks, ComputerROC CurveUltrasonographyBiomarkers

Identifiers

PMID42477328
PMCPMC13500828

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
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 Socratic graph.