Evidence map›Paper›PMID 35589255›Full record

ReviewJournal of hepatology2022

Artificial intelligence for the prevention and clinical management of hepatocellular carcinoma.

Julien Calderaro, Tobias Paul Seraphin, Tom Luedde, Tracey G Simon

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Journal of hepatology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 177 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
177citing papers in PubMed, 3 pooled it
–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

177 citing papers in PubMed, 3 syntheses or guidelines pooled it.

  1. Racial Discrimination and Multiple Health Outcomes: An Umbrella Review of Systematic Reviews and Meta-Analyses.Medical principles and practice : international journal of the Kuwait University, Health Science Centre · 2025
    Pooled it
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  11. Preoperative MRI Intratumoral Heterogeneity Radiomics for Microvascular Invasion and Recurrence-Free Survival in HCC.Liver international : official journal of the International Association for the Study of the Liver · 2026
    Observational
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117 more citing papers are in PubMed but not listed here.

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

4 authors.

Julien CalderaroAssistance Publique-Hôpitaux de Paris, Henri Mondor University Hospital, Department of Pathology, Créteil, France; Inserm U955 and Univ Paris Est Creteil, INSERM, IMRB, 94010, Creteil, France.
Tobias Paul SeraphinDepartment of Gastroenterology, Hepatology and Infectious Diseases, University Hospital Duesseldorf, Medical Faculty at Heinrich-Heine-University Duesseldorf, Duesseldorf, Germany.
Tom LueddeDepartment of Gastroenterology, Hepatology and Infectious Diseases, University Hospital Duesseldorf, Medical Faculty at Heinrich-Heine-University Duesseldorf, Duesseldorf, Germany.
Tracey G SimonLiver Center, Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA; Clinical and Translational Epidemiology Unit (CTEU), Massachusetts General Hospital, Boston, MA, USA. Electronic address: tgsimon@mgh.harvard.edu.

Funding

Aspirin and Novel Lipid Mediators in Nonalcoholic Fatty Liver DiseaseK23DK122104 · NIDDK · MASSACHUSETTS GENERAL HOSPITAL · PI SIMON, TRACEY · 2019 to 2023
$1.1M
NIDDK NIH HHS K23 DK122104
6 · The paper itself

Abstract

Hepatocellular carcinoma (HCC) currently represents the fifth most common malignancy and the third-leading cause of cancer-related death worldwide, with incidence and mortality rates that are increasing. Recently, artificial intelligence (AI) has emerged as a unique opportunity to improve the full spectrum of HCC clinical care, by improving HCC risk prediction, diagnosis, and prognostication. AI approaches include computational search algorithms, machine learning (ML) and deep learning (DL) models. ML consists of a computer running repeated iterations of models, in order to progressively improve performance of a specific task, such as classifying an outcome. DL models are a subtype of ML, based on neural network structures that are inspired by the neuroanatomy of the human brain. A growing body of recent data now apply DL models to diverse data sources - including electronic health record data, imaging modalities, histopathology and molecular biomarkers - to improve the accuracy of HCC risk prediction, detection and prediction of treatment response. Despite the promise of these early results, future research is still needed to standardise AI data, and to improve both the generalisability and interpretability of results. If such challenges can be overcome, AI has the potential to profoundly change the way in which care is provided to patients with or at risk of HCC.

Indexed as

Carcinoma, HepatocellularLiver NeoplasmsAlgorithmsArtificial IntelligenceHumansMachine LearningArtificial intelligenceDeep learningLiver cancerMachine learning

Identifiers

PMID35589255
PMCPMC9126418

What OpenQuestion holds

Textmetadata
LicenceTDM
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.