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Kim D, Jensen LJ, Elgeti T, et al. Radiomics for Everyone: A New Tool Simplifies Creating Parametric Maps for the Visualization and Quantification of Radiomics Features. Tomography. 2021;7(3):477-487doi: 10.3390/tomography7030041.
Kim, D., Jensen, L. J., Elgeti, T., Steffen, I. G., Hamm, B., & Nagel, S. N. (2021). Radiomics for Everyone: A New Tool Simplifies Creating Parametric Maps for the Visualization and Quantification of Radiomics Features. Tomography (Ann Arbor, Mich.), 7(3), 477-487. https://doi.org/10.3390/tomography7030041
Kim, Damon, et al. "Radiomics for Everyone: A New Tool Simplifies Creating Parametric Maps for the Visualization and Quantification of Radiomics Features." Tomography (Ann Arbor, Mich.) vol. 7,3 (2021): 477-487. doi: https://doi.org/10.3390/tomography7030041
Kim D, Jensen LJ, Elgeti T, Steffen IG, Hamm B, Nagel SN. Radiomics for Everyone: A New Tool Simplifies Creating Parametric Maps for the Visualization and Quantification of Radiomics Features. Tomography. 2021 Sep 17;7(3):477-487. doi: 10.3390/tomography7030041. PMID: 34564303; PMCID: PMC8482265.
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Tomography. 2021 Sep 17;7(3):477-487. doi: 10.3390/tomography7030041.

Radiomics for Everyone: A New Tool Simplifies Creating Parametric Maps for the Visualization and Quantification of Radiomics Features.

Tomography (Ann Arbor, Mich.)

Damon Kim, Laura J Jensen, Thomas Elgeti, Ingo G Steffen, Bernd Hamm, Sebastian N Nagel

Affiliations

  1. Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Klinik für Radiologie, Hindenburgdamm 30, 12203 Berlin, Germany.
  2. Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Klinik für Radiologie, Arbeitsbereich Kinderradiologie, Augustenburger Platz 1, 13353 Berlin, Germany.

PMID: 34564303 PMCID: PMC8482265 DOI: 10.3390/tomography7030041

Abstract

Aim was to develop a user-friendly method for creating parametric maps that would provide a comprehensible visualization and allow immediate quantification of radiomics features. For this, a self-explanatory graphical user interface was designed, and for the proof of concept, maps were created for CT and MR images and features were compared to those from conventional extractions. Especially first-order features were concordant between maps and conventional extractions, some even across all examples. Potential clinical applications were tested on CT and MR images for the differentiation of pulmonary lesions. In these sample applications, maps of Skewness enhanced the differentiation of non-malignant lesions and non-small lung carcinoma manifestations on CT images and maps of Variance enhanced the differentiation of pulmonary lymphoma manifestations and fungal infiltrates on MR images. This new and simple method for creating parametric maps makes radiomics features visually perceivable, allows direct feature quantification by placing a region of interest, can improve the assessment of radiological images and, furthermore, can increase the use of radiomics in clinical routine.

Keywords: computer-assisted; diagnostic techniques and procedures; image enhancement; image processing

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