Evidence map›Paper›PMID 40815430›Full record

ArticleInsights into imaging2025

Prognostic and predictive imaging markers of hepatocellular carcinoma: a pictorial essay.

Claudia Deyirmendjian, Banmeet Padda, Kathryn J Fowler, Victoria Chernyak, Claude B Sirlin, Hanyu Jiang, Kim-Nhien Vu, Joseph R Dadour, Jessica Murphy-Lavallée, Jean-Sébastien Billiard and 3 more

Abstract read
In one paragraph

Article in Insights into imaging, 2025. 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. Article
  3. 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

13 authors.

Claudia DeyirmendjianDepartment of Radiology, Radiation Oncology, and Nuclear Medicine, Université de Montréal, Montréal, Québec, Canada.
Banmeet PaddaDepartment of Radiology, Radiation Oncology, and Nuclear Medicine, Université de Montréal, Montréal, Québec, Canada.
Kathryn J FowlerLiver Imaging Group, Department of Radiology, University of California San Diego, San Diego, CA, USA.
Victoria ChernyakDepartment of Radiology, Columbia University Irving Medical Center, New York, NY, USA.
Claude B SirlinLiver Imaging Group, Department of Radiology, University of California San Diego, San Diego, CA, USA.
Hanyu JiangDepartment of Radiology, West China Hospital, Chengdu, China.
Kim-Nhien VuDepartment of Radiology, Radiation Oncology, and Nuclear Medicine, Université de Montréal, Montréal, Québec, Canada.
Joseph R DadourDepartment of Radiology, Radiation Oncology, and Nuclear Medicine, Université de Montréal, Montréal, Québec, Canada.
Jessica Murphy-LavalléeDepartment of Radiology, Radiation Oncology, and Nuclear Medicine, Université de Montréal, Montréal, Québec, Canada.
Jean-Sébastien BilliardDepartment of Radiology, Radiation Oncology, and Nuclear Medicine, Université de Montréal, Montréal, Québec, Canada.
Damien OliviéDepartment of Radiology, Radiation Oncology, and Nuclear Medicine, Université de Montréal, Montréal, Québec, Canada.
Bich N NguyenCentre de recherche du Centre hospitalier de l'Université de Montréal (CRCHUM), Montréal, Québec, Canada.
An TangDepartment of Radiology, Radiation Oncology, and Nuclear Medicine, Université de Montréal, Montréal, Québec, Canada. an.tang@umontreal.ca.ORCID http://orcid.org/0000-0001-8967-5503

Funding

Fonds de Recherche du Québec - Santé 298509
6 · The paper itself

Abstract

Hepatocellular carcinoma (HCC) encompasses a wide array of histopathologic and genetic features that can be broadly categorized as proliferative or non-proliferative HCC to reflect tumor aggressiveness. However, accurately characterizing tumor behavior remains challenging due to the biologic heterogeneity of HCC and limited access to tissue samples. Currently, imaging is used for the diagnosis of HCC using the Liver Imaging Reporting and Data System (LI-RADS) without histologic confirmation in most cases. Emerging data suggest that imaging can provide clinical insight beyond diagnosis and predict patient outcomes by identifying key prognostic features, including those not yet integrated in LI-RADS. Certain CT and MRI features correlate with proliferative and non-proliferative HCC, and may yield prognostic information. Imaging findings such as tumor size, multifocality, and low apparent diffusion coefficient (ADC) have also been associated with microvascular invasion-an independent marker of poor prognosis. Growing data support the role of imaging in predicting treatment responsiveness before therapy initiation, which may influence the selection of a therapeutic agent. The radiologist can offer key clinical information by understanding and describing the prognostic and predictive features in HCC imaging. CRITICAL RELEVANCE STATEMENT: This study provides radiologists with a comprehensive summary of imaging findings associated with HCC prognosis, treatment responsiveness, and microvascular invasion. KEY POINTS: Hepatocellular carcinoma (HCC) is a heterogeneous cancer leading to challenges in diagnosis and management. Tumors can exhibit imaging features associated with proliferative or non-proliferative HCC. Key imaging features can help predict tumor aggressiveness and treatment responsiveness before the therapy is applied. Further research leveraging molecular data and applying machine learning models can improve our understanding of HCC prognostication.

Indexed as

CarcinogenesisCarcinoma (hepatocellular)Magnetic resonance imagingTomography (X-ray computed)

Identifiers

PMID40815430
PMCPMC12356813

What OpenQuestion holds

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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.