Evidence map›Paper›PMID 40293522›Full record

ArticleAbdominal radiology (New York)2025

Predictors of hepatocellular carcinoma in LR-M category lesions, a multi-institutional analysis.

Marybeth Nedrud, Tanya Wolfson, Brian Allen, Anum Aslam, Lauren Burke, Victoria Chernyak, Kathryn Fowler, Tyler J Fraum, Hong-Il Ha, Elizabeth M Hecht and 15 more

Abstract readMulticenter Study
PubMed Publisher
In one paragraph

Article in Abdominal radiology (New York), 2025. 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

25 authors.

Marybeth NedrudDuke University, Durham, USA. marybeth.nedrud@duke.edu.
Tanya WolfsonUniversity of California, San Diego, San Diego, USA.
Brian AllenDuke University, Durham, USA.
Anum AslamUniversity of Michigan-Ann Arbor, Ann Arbor, USA.
Lauren BurkeUniversity of North Carolina at Chapel Hill, Chapel Hill, USA.
Victoria ChernyakColumbia University, New York, USA.
Kathryn FowlerUniversity of California, San Diego, San Diego, USA.
Tyler J FraumWashington University in St. Louis, St Louis, USA.
Hong-Il HaDuke University, Durham, USA.
Elizabeth M HechtCornell University, Ithaca, USA.
Tracy JaffeDuke University, Durham, USA.
Kevin KaliszDuke University, Durham, USA.
Andrea Siobhan KieransCornell University, Ithaca, USA.
Daniel R LudwigWashington University in St. Louis, St Louis, USA.
Jasnit S MakkarColumbia University, New York, USA.
Katrina McGintyUniversity of North Carolina at Chapel Hill, Chapel Hill, USA.
Matthew McInnesUniversity of Ottawa, Ottawa, Canada.
Mishal Mendiratta-LalaUniversity of Michigan-Ann Arbor, Ann Arbor, USA.
Omobonike OloruntobaDuke University, Durham, USA.
Damithri RanathungaPeterborough Regional Health Centre, Ontario, Canada.
Benjamin Wildman-TobrinerDuke University, Durham, USA.
Anthony C GamstUniversity of California, San Diego, San Diego, USA.
Diana M CardonaDuke University, Durham, USA.
Andrew MuirDuke University, Durham, USA.
Mustafa BashirDuke University, Durham, USA. mustafa.bashir@duke.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeThe Liver Imaging Reporting and Data System (LI-RADS, LR) provides a framework for diagnosing hepatocellular carcinoma (HCC, LR-5). However, not all HCCs meet LR-5 criteria and are instead categorized as LR-M, probably or definitely malignant but not specific for HCC, necessitating biopsy for diagnosis. The purpose is to identify factors associated with HCC in LR-M observations.

methodsThis is an IRB-approved, retrospective analysis of participants from 8 institutions that had a LR-M observation on CT or MRI with corresponding histopathologic diagnosis. Demographics and biochemical data were examined. Central review using the LI-RADS v2018 algorithm was performed. Kappa statistics defined inter-reader agreement. Random forest and logistic regression analyses generated a model for HCC diagnosis.

results162 participants with 162 LR-M observations were included. 46% of observations (74/162) were HCC and 37% were cholangiocarcinoma (60/162). Two of 34 imaging features- observation size and intra-lesion iron- showed moderate to strong inter-reader agreement (Kappa ≥ 0.60) while the remainder showed weak or no agreement (Kappa < 0.60). Random forest analysis showed biochemical features to be more predictive of HCC than imaging features. Logistic regression analysis of a model based on INR and AFP provided a 72% sensitivity and 61% specificity for HCC by Youden's index and a 90% specificity threshold yielded 38% sensitivity, 75% positive predictive value, and 66% negative predictive value.

conclusionsOur results show INR and AFP are associated with HCC in LR-M observations. A high-specificity threshold may assist in the non-invasive diagnosis of HCC in the appropriate setting. In certain at-risk patients with a LR-M observation on diagnostic imaging, serum AFP and INR maybe useful tools for the non-invasive diagnosis of HCC.

Indexed as

Carcinoma, HepatocellularLiver NeoplasmsMagnetic Resonance ImagingTomography, X-Ray ComputedAdultAgedFemaleHumansMaleMiddle AgedPredictive Value of TestsRetrospective StudiesAlpha-fetoprotein (AFP)Hepatocellular carcinoma (HCC)Liver imaging reporting and data system (LI-RADS)Malignancy predictors

Identifiers

PMID40293522

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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.