Evidence map›Paper›PMID 37296890›Full record

ReviewCancers2023

The Role of Artificial Intelligence in the Detection and Implementation of Biomarkers for Hepatocellular Carcinoma: Outlook and Opportunities.

Arian Mansur, Andrea Vrionis, Jonathan P Charles, Kayesha Hancel, John C Panagides, Farzad Moloudi, Shams Iqbal, Dania Daye

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Cancers, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed, 2 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

20 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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  12. Circulating biomarkers for diagnosis and response to therapies in cancer patients.International review of cell and molecular biology · 2025
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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

8 authors.

Arian MansurHarvard Medical School, Boston, MA 02115, USA.ORCID 0000-0003-2406-5127
Andrea VrionisMorsani College of Medicine, University of South Florida Health, Tampa, FL 33602, USA.ORCID 0009-0000-0305-5291
Jonathan P CharlesMorsani College of Medicine, University of South Florida Health, Tampa, FL 33602, USA.ORCID 0000-0002-2868-6687
Kayesha HancelDepartment of Radiology, Massachusetts General Hospital, Boston, MA 02114, USA.ORCID 0000-0002-3313-3454
John C PanagidesHarvard Medical School, Boston, MA 02115, USA.ORCID 0000-0002-8008-7649
Farzad MoloudiDepartment of Radiology, Massachusetts General Hospital, Boston, MA 02114, USA.ORCID 0000-0003-4480-2972
Shams IqbalDepartment of Radiology, Massachusetts General Hospital, Boston, MA 02114, USA.
Dania DayeDepartment of Radiology, Massachusetts General Hospital, Boston, MA 02114, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Liver cancer is a leading cause of cancer-related death worldwide, and its early detection and treatment are crucial for improving morbidity and mortality. Biomarkers have the potential to facilitate the early diagnosis and management of liver cancer, but identifying and implementing effective biomarkers remains a major challenge. In recent years, artificial intelligence has emerged as a promising tool in the cancer sphere, and recent literature suggests that it is very promising in facilitating biomarker use in liver cancer. This review provides an overview of the status of AI-based biomarker research in liver cancer, with a focus on the detection and implementation of biomarkers for risk prediction, diagnosis, staging, prognostication, prediction of treatment response, and recurrence of liver cancers.

Indexed as

artificial intelligencebiomarkersdeep learninghepatocellular carcinomaliver cancermachine learning

Identifiers

PMID37296890
PMCPMC10251861

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.