Evidence map›Paper›PMID 37660402›Full record

ReviewThe British journal of radiology2023

Artificial intelligence (AI) and machine learning (ML) in precision oncology: a review on enhancing discoverability through multiomics integration.

Lise Wei, Dipesh Niraula, Evan D H Gates, Jie Fu, Yi Luo, Matthew J Nyflot, Stephen R Bowen, Issam M El Naqa, Sunan Cui

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in The British journal of radiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 81 papers.

0numbers the graph read from it
0cells of the map it votes in
81citing 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

81 citing papers in PubMed.

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  6. Interpretable deep learning model of circulating genomics for quantitative survival prediction in advanced non-small cell lung cancer.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026
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21 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

9 authors.

Lise WeiDepartment of Radiation Oncology, University of Michigan, Michigan, United States.
Dipesh NiraulaDepartment of Radiation Oncology, Moffitt Cancer Center, Tampa, United States.
Evan D H GatesDepartment of Radiation Oncology, University of Washington, Washington, United States.
Jie FuDepartment of Radiation Oncology, Stanford University, Stanford, California, United States.
Yi LuoDepartment of Radiation Oncology, Moffitt Cancer Center, Tampa, United States.
Matthew J NyflotDepartment of Radiation Oncology, University of Washington, Washington, United States.
Stephen R BowenDepartment of Radiation Oncology, University of Washington, Washington, United States.
Issam M El NaqaDepartment of Radiation Oncology, Moffitt Cancer Center, Tampa, United States.
Sunan CuiDepartment of Radiation Oncology, University of Washington, Washington, United States.ORCID 0000-0002-8846-9449

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Multiomics data including imaging radiomics and various types of molecular biomarkers have been increasingly investigated for better diagnosis and therapy in the era of precision oncology. Artificial intelligence (AI) including machine learning (ML) and deep learning (DL) techniques combined with the exponential growth of multiomics data may have great potential to revolutionize cancer subtyping, risk stratification, prognostication, prediction and clinical decision-making. In this article, we first present different categories of multiomics data and their roles in diagnosis and therapy. Second, AI-based data fusion methods and modeling methods as well as different validation schemes are illustrated. Third, the applications and examples of multiomics research in oncology are demonstrated. Finally, the challenges regarding the heterogeneity data set, availability of omics data, and validation of the research are discussed. The transition of multiomics research to real clinics still requires consistent efforts in standardizing omics data collection and analysis, building computational infrastructure for data sharing and storing, developing advanced methods to improve data fusion and interpretability, and ultimately, conducting large-scale prospective clinical trials to fill the gap between study findings and clinical benefits.

Indexed as

Artificial IntelligenceNeoplasmsHumansMachine LearningMultiomicsPrecision MedicineProspective Studies

Identifiers

PMID37660402
PMCPMC10546458

What OpenQuestion holds

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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.