Evidence map›Paper›PMID 39981949›Full record

ReviewJournal of magnetic resonance imaging : JMRI2025

Current State of Evidence for Use of MRI in LI-RADS.

Ameya Madhav Kulkarni, Danielle Kruse, Kelly Harper, Eric Lam, Hoda Osman, Danyaal H Ansari, Umaseh Sivanesan, Mustafa R Bashir, Andreu F Costa, Matthew McInnes and 1 more

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Journal of magnetic resonance imaging : JMRI, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Review
  2. Review
  3. Article
  4. Current State of Evidence for Use of MRI in LI-RADS.Journal of magnetic resonance imaging : JMRI · 2025
    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

11 authors.

Ameya Madhav KulkarniDepartment of Medical Imaging, Hamilton Health Sciences, McMaster University, Hamilton, Ontario, Canada.
Danielle KruseDepartments of Radiology and Medicine, Duke University Medical Center, Durham, North Carolina, USA.
Kelly HarperDepartment of Radiology, The Ottawa Hospital, University of Ottawa, Ottawa, Ontario, Canada.
Eric LamOttawa Hospital Research Institute Clinical Epidemiology Program, Ottawa, Ontario, Canada.
Hoda OsmanOttawa Hospital Research Institute Clinical Epidemiology Program, Ottawa, Ontario, Canada.
Danyaal H AnsariOttawa Hospital Research Institute Clinical Epidemiology Program, Ottawa, Ontario, Canada.
Umaseh SivanesanDepartment of Diagnostic Radiology, Kingston Health Sciences Centre, Kingston General Hospital, Kingston, Ontario, Canada.
Mustafa R BashirDepartments of Radiology and Medicine, Duke University Medical Center, Durham, North Carolina, USA.
Andreu F CostaQueen Elizabeth II Health Sciences Centre and Dalhousie University, Halifax, Nova Scotia, Canada.ORCID https://orcid.org/0000-0003-1683-8230
Matthew McInnesDepartment of Radiology, The Ottawa Hospital, University of Ottawa, Ottawa, Ontario, Canada.
Christian B van der PolDepartment of Medical Imaging, Hamilton Health Sciences, McMaster University, Hamilton, Ontario, Canada.ORCID https://orcid.org/0000-0002-1718-2619

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The American College of Radiology Liver Imaging Reporting and Data System (LI-RADS) is the preeminent framework for classification and risk stratification of liver observations on imaging in patients at high risk for hepatocellular carcinoma. In this review, the pathogenesis of hepatocellular carcinoma and the use of MRI in LI-RADS is discussed, including specifically the LI-RADS diagnostic algorithm, its components, and its reproducibility with reference to the latest supporting evidence. The LI-RADS treatment response algorithms are reviewed, including the more recent radiation treatment response algorithm. The application of artificial intelligence, points of controversy, LI-RADS relative to other liver imaging systems, and possible future directions are explored. After reading this article, the reader will have an understanding of the foundation and application of LI-RADS as well as possible future directions.

Indexed as

Carcinoma, HepatocellularImage Interpretation, Computer-AssistedLiver NeoplasmsMagnetic Resonance ImagingRadiology Information SystemsAlgorithmsEvidence-Based MedicineHumansReproducibility of ResultsSensitivity and Specificitycarcinomadiagnostic techniquesdigestive systemhepatocellularlivermagnetic resonance imagingobserver variation

Identifiers

PMID39981949
PMCPMC12335345

What OpenQuestion holds

Textmetadata
LicenceCC BY
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 OpenQuestion graph.