Evidence map›Paper›PMID 39001510›Full record

ReviewCancers2024

Integrating Omics Data and AI for Cancer Diagnosis and Prognosis.

Yousaku Ozaki, Phil Broughton, Hamed Abdollahi, Homayoun Valafar, Anna V Blenda

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Cancers, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.

0numbers the graph read from it
0cells of the map it votes in
36citing 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

36 citing papers in PubMed.

  1. Review
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  12. Multi-Omics Integration for Advancing Glioma Precision Medicine.Annals of clinical and translational neurology · 2026
    Review
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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

5 authors.

Yousaku OzakiDepartment of Biomedical Sciences, University of South Carolina School of Medicine Greenville, Greenville, SC 29605, USA.ORCID 0000-0003-4989-4319
Phil BroughtonDepartment of Biomedical Sciences, University of South Carolina School of Medicine Greenville, Greenville, SC 29605, USA.ORCID 0009-0001-4290-1304
Hamed AbdollahiDepartment of Computer Science and Engineering, Molinaroli College of Engineering and Computing, Columbia, SC 29208, USA.ORCID 0000-0002-2202-2193
Homayoun ValafarDepartment of Computer Science and Engineering, Molinaroli College of Engineering and Computing, Columbia, SC 29208, USA.ORCID 0000-0002-1581-3464
Anna V BlendaDepartment of Biomedical Sciences, University of South Carolina School of Medicine Greenville, Greenville, SC 29605, USA.ORCID 0000-0001-7669-3120

Funding

South Carolina IDeA Networks of Biomedical Research (SC INBRE V)P20GM103499 · NIGMS · UNIVERSITY OF SOUTH CAROLINA AT COLUMBIA · PI EDIE C GOLDSMITH · 2012 to 2026
$61.0M
Research reported in this publication was supported by the National Institute of General Medical Sciences of the National Institutes of Health P20GM103499
6 · The paper itself

Abstract

Cancer is one of the leading causes of death, making timely diagnosis and prognosis very important. Utilization of AI (artificial intelligence) enables providers to organize and process patient data in a way that can lead to better overall outcomes. This review paper aims to look at the varying uses of AI for diagnosis and prognosis and clinical utility. PubMed and EBSCO databases were utilized for finding publications from 1 January 2020 to 22 December 2023. Articles were collected using key search terms such as "artificial intelligence" and "machine learning." Included in the collection were studies of the application of AI in determining cancer diagnosis and prognosis using multi-omics data, radiomics, pathomics, and clinical and laboratory data. The resulting 89 studies were categorized into eight sections based on the type of data utilized and then further subdivided into two subsections focusing on cancer diagnosis and prognosis, respectively. Eight studies integrated more than one form of omics, namely genomics, transcriptomics, epigenomics, and proteomics. Incorporating AI into cancer diagnosis and prognosis alongside omics and clinical data represents a significant advancement. Given the considerable potential of AI in this domain, ongoing prospective studies are essential to enhance algorithm interpretability and to ensure safe clinical integration.

Indexed as

artificial intelligencecancerdeep learningmachine learningomics technologies

Identifiers

PMID39001510
PMCPMC11240413

What Socratic 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 Socratic graph.