Evidence map›Paper›PMID 40810812›Full record

ReviewAbdominal radiology (New York)2026

LR-M for CT/MRI on LI-RADS v2018: a review of imaging criteria, performance, challenges and future directions from an end-user perspective.

Gavin Low, Tyler Pfanner, Xu Jing Qian, Ali Ramji, Karim Samji, Mitchell P Wilson

Abstract readReview
PubMed Publisher
In one paragraph

Review in Abdominal radiology (New York), 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. Review
  2. Review
  3. 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

6 authors.

Gavin LowUniversity of Alberta, Edmonton, Canada.
Tyler PfannerUniversity of Alberta, Edmonton, Canada.
Xu Jing QianUniversity of Alberta, Edmonton, Canada.
Ali RamjiUniversity of Alberta, Edmonton, Canada.
Karim SamjiUniversity of Alberta, Edmonton, Canada.
Mitchell P WilsonUniversity of Alberta, Edmonton, Canada. mitch.wilson@ualberta.ca.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

LR-M is a category within the Liver Imaging Reporting and Data System (LI-RADS) that refers to liver observations that are probably or definitely malignant but are not specific to hepatocellular carcinoma (HCC). It includes etiologies such as atypical HCC, intrahepatic cholangiocarcinoma, combined hepatocellular cholangiocarcinoma and metastases. The primary aim of LR-M is to ensure a high sensitivity for detecting all hepatic malignancies while preserving a high specificity for HCC in LR-5. The imaging criteria for LR-M encompass a variety of targetoid and non-targetoid features. LR-M is often less well understood by end-users compared to more prominent categories such as LR-4 and LR-5, which have garnered greater attention and familiarity. In this review written from an end-user perspective, we examine the critical role that LR-M plays within LI-RADS for CT/MRI, the prevalence of HCC and non-HCC malignancies in LR-M, and demonstrate how LR-M can impact prognosis and treatment outcomes. We discuss the current imaging criteria for LR-M and the challenges faced by end-users in LI-RADS v2018 for CT/MRI. Finally, we explore future directions for improving the application of LR-M in clinical practice.

Indexed as

Carcinoma, HepatocellularLiver NeoplasmsMagnetic Resonance ImagingRadiology Information SystemsTomography, X-Ray ComputedCholangiocarcinomaHumansSensitivity and SpecificityCombined hepatocellular cholangiocarcinomaComputed tomographyHepatocellular carcinomaIntrahepatic cholangiocarcinomaLiver imaging reporting and data system (LI-RADS)LR-MMagnetic resonance imaging

Identifiers

What OpenQuestion holds

Textmetadata
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.