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Review
. 2022 Apr;43(2):142-146.
doi: 10.1053/j.sult.2022.02.003. Epub 2022 Feb 12.

Radiomics: A Primer on Processing Workflow and Analysis

Emily Avery  1 , Pina C Sanelli  2 , Mariam Aboian  1 , Seyedmehdi Payabvash  3
Affiliations
Review

Radiomics: A Primer on Processing Workflow and Analysis

Emily Avery et al. Semin Ultrasound CT MR. 2022 Apr.

Abstract

Quantitative analysis of medical images can provide objective tools for diagnosis, prognostication, and disease monitoring. Radiomics refers to automated extraction of a large number of quantitative features from medical images for characterization of underlying pathologies. In this review, we will discuss the principles of radiomics, image preprocessing, feature extraction workflow, and statistical analysis. We will also address the limitations and future directions of radiomics.

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Figures

Figure 1.
Figure 1.
Radiomics Workflow
Figure 2.
Figure 2.. Commonly extracted first-order and texture radiomics features.
A complete list of radiomics features is described in van Griesen et al., 2017 [24], and feature definitions are described in pyRadiomics documentation.
See this image and copyright information in PMC

References

    1. Volzke H, et al., Population imaging as valuable tool for personalized medicine. Clin Pharmacol Ther, 2012. 92(4): p. 422–4. - PubMed
    1. Moon SH, et al., Correlations between metabolic texture features, genetic heterogeneity, and mutation burden in patients with lung cancer. Eur J Nucl Med Mol Imaging, 2019. 46(2): p. 446–454. - PubMed
    1. Morris LG, et al., Pan-cancer analysis of intratumor heterogeneity as a prognostic determinant of survival. Oncotarget, 2016. 7(9): p. 10051–63. - PMC - PubMed
    1. Huang Y, et al., Radiomics Signature: A Potential Biomarker for the Prediction of Disease-Free Survival in Early-Stage (I or II) Non-Small Cell Lung Cancer. Radiology, 2016. 281(3): p. 947–957. - PubMed
    1. Haider SP, et al., Applications of radiomics in precision diagnosis, prognostication and treatment planning of head and neck squamous cell carcinomas. Cancers Head Neck, 2020. 5: p. 6. - PMC - PubMed