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
. 2023 Oct 21;10(1):732.
doi: 10.1038/s41597-023-02641-x.

Enhancing radiomics and Deep Learning systems through the standardization of medical imaging workflows

Miriam Cobo  1 , Pablo Menéndez Fernández-Miranda  2 , Gorka Bastarrika  2 , Lara Lloret Iglesias  3
Affiliations

Enhancing radiomics and Deep Learning systems through the standardization of medical imaging workflows

Miriam Cobo et al. Sci Data. .

Abstract

Recent advances in computer-aided diagnosis, treatment response and prognosis in radiomics and deep learning challenge radiology with requirements for world-wide methodological standards for labeling, preprocessing and image acquisition protocols. The adoption of these standards in the clinical workflows is a necessary step towards generalization and interoperability of radiomics and artificial intelligence algorithms in medical imaging.

PubMed Disclaimer

Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Fig. 1
Fig. 1
Effect of different preprocessing steps on the same nodule and the corresponding histograms calculated for the nodule mask: (A) mediastinal window, (B) lung window (a.u. refers to arbitrary units).
Fig. 2
Fig. 2
The nine stages of reaching standardization and making medical imaging data as FAIR as possible.
See this image and copyright information in PMC

References

    1. Siegel RL, Miller KD, Wagle NS, Jemal A. Cancer statistics, 2023. Ca Cancer J Clin. 2023;73:17–48. - PubMed
    1. Hricak H, et al. Medical imaging and nuclear medicine: a lancet oncology commission. The Lancet Oncology. 2021;22:e136–e172. - PMC - PubMed
    1. Elshafeey N, et al. Multicenter study demonstrates radiomic features derived from magnetic resonance perfusion images identify pseudoprogression in glioblastoma. Nature communications. 2019;10:3170. - PMC - PubMed
    1. Kobayashi K, Miyake M, Takahashi M, Hamamoto R. Observing deep radiomics for the classification of glioma grades. Scientific Reports. 2021;11:10942. - PMC - PubMed
    1. Fournier L, et al. Incorporating radiomics into clinical trials: expert consensus endorsed by the european society of radiology on considerations for data-driven compared to biologically driven quantitative biomarkers. European radiology. 2021;31:6001–6012. - PMC - PubMed