Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2022 Jun 10;23(12):6504.
doi: 10.3390/ijms23126504.

What Genetics Can Do for Oncological Imaging: A Systematic Review of the Genetic Validation Data Used in Radiomics Studies

Rebeca Mirón Mombiela  1 , Anne Rix Arildskov  1 , Frederik Jager Bruun  1 , Lotte Harries Hasselbalch  1 , Kristine Bærentz Holst  1 , Sine Hvid Rasmussen  1 , Consuelo Borrás  2   3
Affiliations

What Genetics Can Do for Oncological Imaging: A Systematic Review of the Genetic Validation Data Used in Radiomics Studies

Rebeca Mirón Mombiela et al. Int J Mol Sci. .

Abstract

(1) Background: Radiogenomics is motivated by the concept that biomedical images contain information that reflects underlying pathophysiology. This review focused on papers that used genetics to validate their radiomics models and outcomes and assess their contribution to this emerging field. (2) Methods: All original research with the words radiomics and genomics in English and performed in humans up to 31 January 2022, were identified on Medline and Embase. The quality of the studies was assessed with Radiomic Quality Score (RQS) and the Cochrane recommendation for diagnostic accuracy study Quality Assessment 2. (3) Results: 45 studies were included in our systematic review, and more than 50% were published in the last two years. The studies had a mean RQS of 12, and the studied tumors were very diverse. Up to 83% investigated the prognosis as the main outcome, with the rest focusing on response to treatment and risk assessment. Most applied either transcriptomics (54%) and/or genetics (35%) for genetic validation. (4) Conclusions: There is enough evidence to state that new science has emerged, focusing on establishing an association between radiological features and genomic/molecular expression to explain underlying disease mechanisms and enhance prognostic, risk assessment, and treatment response radiomics models in cancer patients.

Keywords: cancer; genetic validation; genomics; prognosis; radiogenomics; radiomics; risk assessment; survival; treatment response.

PubMed Disclaimer

Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
The image shows how multiscale quantification provides complimentary tumor insight. Histologic and genomic analysis can provide specific small-scale insight, which is useful for the validation of radiomic results, focused on quantification of spatial patterns of size exceeding image resolution [6]. Reprinted with permission of RSNA.
Figure 2
Figure 2
Flow diagram for the systemic review, which included searches of databases and other sources.
Figure 3
Figure 3
Stacked area chart of the percentage of published articles included in the systemic review by year of publication.
Figure 4
Figure 4
A pie chart of the different tumors studied in the articles included in the systemic review.
Figure 5
Figure 5
Stacked column chart of the number of studies grouped by the utility of the radiomics model and the outcome measure of the included articles in the systemic review. Abbreviations: MFS: Metastasis-free survival, RFS: Recurrence-free survival, DFS: Disease-free survival, PFS: Progression-free survival, PFI: progression-free interval, DSS: Disease-specific survival, OS: Overall Survival, pCR: pathological complete response, DCB: Durable clinical benefit.
Figure 6
Figure 6
Pie charts of genetics data of the included articles in the systemic review: (a) Type of genetic science applied. (b) Analysis of the genetic data. (c) Used public genetics databases. (d) Public genetics databases used in the included articles.
Figure 7
Figure 7
Pie chart of the limitations and bias of the reviewed publications.
Figure 8
Figure 8
Graphical representation of summarizing the main characteristics of the included articles of the systemic review. Icons utilized in this figure were obtained from the Noun Project from the following authors: Oleksandr Panasovskyi (tomography), Pedro Baños Cancer (DNA), Gilbert Bages (network circles), Eucalyp (network boxes).
See this image and copyright information in PMC

References

    1. Lambin P., Leijenaar R.T.H., Deist T.M., Peerlings J., de Jong E.E.C., van Timmeren J., Sanduleanu S., Larue R., Even A.J.G., Jochems A., et al. Radiomics: The bridge between medical imaging and personalized medicine. Nat. Rev. Clin. Oncol. 2017;14:749–762. doi: 10.1038/nrclinonc.2017.141. - DOI - PubMed
    1. Shui L., Ren H., Yang X., Li J., Chen Z., Yi C., Zhu H., Shui P. The Era of Radiogenomics in Precision Medicine: An Emerging Approach to Support Diagnosis, Treatment Decisions, and Prognostication in Oncology. Front. Oncol. 2020;10:570465. doi: 10.3389/fonc.2020.570465. - DOI - PMC - PubMed
    1. Yip S.S., Aerts H.J. Applications and limitations of radiomics. Phys. Med. Biol. 2016;61:R150–R166. doi: 10.1088/0031-9155/61/13/R150. - DOI - PMC - PubMed
    1. Gatta R., Depeursinge A., Ratib O., Michielin O., Leimgruber A. Integrating radiomics into holomics for personalised oncology: From algorithms to bedside. Eur. Radiol. Exp. 2020;4:11. doi: 10.1186/s41747-019-0143-0. - DOI - PMC - PubMed
    1. Lambin P., Rios-Velazquez E., Leijenaar R., Carvalho S., van Stiphout R.G., Granton P., Zegers C.M., Gillies R., Boellard R., Dekker A., et al. Radiomics: Extracting more information from medical images using advanced feature analysis. Eur. J. Cancer. 2012;48:441–446. doi: 10.1016/j.ejca.2011.11.036. - DOI - PMC - PubMed

Publication types