Evidence map›Paper›PMID 41583907›Full record

ReviewMedComm2026

Radiogenomics: Current Understandings and Future Perspectives.

Xinyu Zhang, Qingpei Lai, Jin Cao, Jerry Chi Fung Ching, Xinzhi Teng, Jiang Zhang, Shara Wee Yee Lee, Ge Ren, Jing Cai

Abstract readReview
In one paragraph

Review in MedComm, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Review
  3. Review
  4. Review
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.

Xinyu ZhangDepartment of Health Technology and Informatics The Hong Kong Polytechnic University Hong Kong China.
Qingpei LaiDepartment of Health Technology and Informatics The Hong Kong Polytechnic University Hong Kong China.ORCID https://orcid.org/0009-0009-5644-1319
Jin CaoDepartment of Health Technology and Informatics The Hong Kong Polytechnic University Hong Kong China.
Jerry Chi Fung ChingDepartment of Health Technology and Informatics The Hong Kong Polytechnic University Hong Kong China.ORCID https://orcid.org/0000-0003-1704-4061
Xinzhi TengDepartment of Health Technology and Informatics The Hong Kong Polytechnic University Hong Kong China.
Jiang ZhangDepartment of Health Technology and Informatics The Hong Kong Polytechnic University Hong Kong China.
Shara Wee Yee LeeDepartment of Health Technology and Informatics The Hong Kong Polytechnic University Hong Kong China.
Ge RenDepartment of Health Technology and Informatics The Hong Kong Polytechnic University Hong Kong China.
Jing CaiDepartment of Health Technology and Informatics The Hong Kong Polytechnic University Hong Kong China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Radiogenomics is a rapidly developing field that links radiological image features (radiomics) to genomic-level data (genomics, transcriptomics, and epigenomics), addressing the limitations of single-omic approaches. Radiomics provides a noninvasive and cost-effective method to capture tissue-level characteristics, while genomics elucidates the underlying molecular mechanisms. The central hypothesis is that the formation of imaging phenotypes is associated with the genetic and molecular processes, and thus can reflect underlying biological activities. This review presents the fundamental principles of radiogenomic analysis, covering key concepts in image analysis and gene analysis, as well as advanced analytical techniques for linking imaging and genomic data. Moreover, we summarize recent research findings across various human diseases, including oncology and nononcology, to highlight the current understandings and achievements in this field. Radiogenomics shows potential in clinical applications for elucidating disease mechanisms, detecting genomic variations noninvasively, and improving prognosis predictions. However, its implementation in clinical practice is limited by data scarcity, analytical methods, and barriers in translational processes. Future research should focus on enhancing data quality and establishing guidelines, developing analytical platforms, and validating current findings through animal models and clinical trials.

Indexed as

artificial intelligencemultiomicsprecision medicineradiogenomics

Identifiers

PMID41583907
PMCPMC12828074

What OpenQuestion holds

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LicenceCC BY
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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.