Next Article in Journal
Standardization in Quantitative Imaging: A Multicenter Comparison of Radiomic Features from Different Software Packages on Digital Reference Objects and Patient Data Sets
Previous Article in Journal
Comparison of Segmentation Methods in Assessing Background Parenchymal Enhancement as a Biomarker for Response to Neoadjuvant Therapy
 
 
Tomography is published by MDPI from Volume 7 Issue 1 (2021). Previous articles were published by another publisher in Open Access under a CC-BY (or CC-BY-NC-ND) licence, and they are hosted by MDPI on mdpi.com as a courtesy and upon agreement with Grapho, LLC.
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Stanford DRO Toolkit: Digital Reference Objects for Standardization of Radiomic Features

by
Akshay Jaggi
1,
Sarah A. Mattonen
1,2,
Michael McNitt-Gray
3 and
Sandy Napel
1,*
1
Department of Radiology, Stanford University, Stanford, CA 94305, USA
2
Department of Medical Biophysics, The University of Western Ontario, London, ON, Canada
3
Department of Radiology, University of California, Los Angeles, Los Angeles, CA, USA
*
Author to whom correspondence should be addressed.
Submission received: 9 March 2020 / Revised: 7 April 2020 / Accepted: 4 May 2020 / Published: 1 June 2020

Abstract

Several institutions have developed image feature extraction software to compute quantitative descriptors of medical images for radiomics analyses. With radiomics increasingly proposed for use in research and clinical contexts, new techniques are necessary for standardizing and replicating radiomics findings across software implementations. We have developed a software toolkit for the creation of 3D digital reference objects with customizable size, shape, intensity, texture, and margin sharpness values. Using user-supplied input parameters, these objects are defined mathematically as continuous functions, discretized, and then saved as DICOM objects. Here, we present the definition of these objects, parameterized derivations of a subset of their radiomics values, computer code for object generation, example use cases, and a user-downloadable sample collection used for the examples cited in this paper.
Keywords: radiomics; radiology; quantitative imaging; phantoms; standardization radiomics; radiology; quantitative imaging; phantoms; standardization

Share and Cite

MDPI and ACS Style

Jaggi, A.; Mattonen, S.A.; McNitt-Gray, M.; Napel, S. Stanford DRO Toolkit: Digital Reference Objects for Standardization of Radiomic Features. Tomography 2020, 6, 111-117. https://0-doi-org.brum.beds.ac.uk/10.18383/j.tom.2019.00030

AMA Style

Jaggi A, Mattonen SA, McNitt-Gray M, Napel S. Stanford DRO Toolkit: Digital Reference Objects for Standardization of Radiomic Features. Tomography. 2020; 6(2):111-117. https://0-doi-org.brum.beds.ac.uk/10.18383/j.tom.2019.00030

Chicago/Turabian Style

Jaggi, Akshay, Sarah A. Mattonen, Michael McNitt-Gray, and Sandy Napel. 2020. "Stanford DRO Toolkit: Digital Reference Objects for Standardization of Radiomic Features" Tomography 6, no. 2: 111-117. https://0-doi-org.brum.beds.ac.uk/10.18383/j.tom.2019.00030

Article Metrics

Back to TopTop