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Volume 6
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10.18383/j.tom.2019.00031
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Article

Standardization in Quantitative Imaging: A Multicenter Comparison of Radiomic Features from Different Software Packages on Digital Reference Objects and Patient Data Sets

by
M. McNitt-Gray
1,*,
S. Napel
2,
A. Jaggi
2,
S.A. Mattonen
2,3,
L. Hadjiiski
4,
M. Muzi
5,
D. Goldgof
6,
Y. Balagurunathan
7,
L.A. Pierce
5,
P.E. Kinahan
5,
E.F. Jones
8,
A. Nguyen
8,
A. Virkud
4,
H.P. Chan
4,
N. Emaminejad
1,
M. Wahi-Anwar
1,
M. Daly
1,
M. Abdalah
7,
H. Yang
9,
L. Lu
9,
W. Lv
10,
A. Rahmim
10,
A. Gastounioti
11,
S. Pati
11,
S. Bakas
11,
D. Kontos
11,
B. Zhao
9,
J. Kalpathy-Cramer
12 and
K. Farahani
13add Show full author list remove Hide full author list
1
David Geffen School of Medicine, University of California, 924 Westwood Blvd, Suite 650, Los Angeles, CA 90024, USA
2
Stanford University School of Medicine, Stanford, CA, USA
3
The University of Western Ontario, Canada
4
University of Michigan, Ann Arbor, MI, USA
5
University of Washington, Seattle, WA, USA
6
University of South Florida, Tampa, FL, USA
7
H. Lee Moffitt Cancer Center, Tampa, FL, USA
8
UC San Francisco, School of Medicine, San Francisco, CA, USA
9
Columbia University Medical Center, New York, NY, USA
10
BC Cancer Research Centre, Vancouver, BC, Canada
11
Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
12
Massachusetts General Hospital, Boston, MA, USA
13
National Cancer Institute, Bethesda, MD, USA
 
add Show full affiliation list
 
remove Hide full affiliation list
*
Author to whom correspondence should be addressed.
Tomography 2020, 6(2), 118-128; https://doi.org/10.18383/j.tom.2019.00031
Submission received: 11 March 2020 / Revised: 7 April 2020 / Accepted: 14 May 2020 / Published: 1 June 2020
Versions Notes

Abstract

Radiomic features are being increasingly studied for clinical applications. We aimed to assess the agreement among radiomic features when computed by several groups by using different software packages under very tightly controlled conditions, which included standardized feature definitions and common image data sets. Ten sites (9 from the NCI's Quantitative Imaging Network] positron emission tomography–computed tomography working group plus one site from outside that group) participated in this project. Nine common quantitative imaging features were selected for comparison including features that describe morphology, intensity, shape, and texture. The common image data sets were: three 3D digital reference objects (DROs) and 10 patient image scans from the Lung Image Database Consortium data set using a specific lesion in each scan. Each object (DRO or lesion) was accompanied by an already-defined volume of interest, from which the features were calculated. Feature values for each object (DRO or lesion) were reported. The coefficient of variation (CV), expressed as a percentage, was calculated across software packages for each feature on each object. Thirteen sets of results were obtained for the DROs and patient data sets. Five of the 9 features showed excellent agreement with CV < 1%; 1 feature had moderate agreement (CV < 10%), and 3 features had larger variations (CV ≥ 10%) even after attempts at harmonization of feature calculations. This work highlights the value of feature definition standardization as well as the need to further clarify definitions for some features.
Keywords: radiomics; quantitative imaging; standardization; Multi-center; feature definitions radiomics; quantitative imaging; standardization; Multi-center; feature definitions

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MDPI and ACS Style

McNitt-Gray, M.; Napel, S.; Jaggi, A.; Mattonen, S.A.; Hadjiiski, L.; Muzi, M.; Goldgof, D.; Balagurunathan, Y.; Pierce, L.A.; Kinahan, P.E.; et al. Standardization in Quantitative Imaging: A Multicenter Comparison of Radiomic Features from Different Software Packages on Digital Reference Objects and Patient Data Sets. Tomography 2020, 6, 118-128. https://doi.org/10.18383/j.tom.2019.00031

AMA Style

McNitt-Gray M, Napel S, Jaggi A, Mattonen SA, Hadjiiski L, Muzi M, Goldgof D, Balagurunathan Y, Pierce LA, Kinahan PE, et al. Standardization in Quantitative Imaging: A Multicenter Comparison of Radiomic Features from Different Software Packages on Digital Reference Objects and Patient Data Sets. Tomography. 2020; 6(2):118-128. https://doi.org/10.18383/j.tom.2019.00031

Chicago/Turabian Style

McNitt-Gray, M., S. Napel, A. Jaggi, S.A. Mattonen, L. Hadjiiski, M. Muzi, D. Goldgof, Y. Balagurunathan, L.A. Pierce, P.E. Kinahan, and et al. 2020. "Standardization in Quantitative Imaging: A Multicenter Comparison of Radiomic Features from Different Software Packages on Digital Reference Objects and Patient Data Sets" Tomography 6, no. 2: 118-128. https://doi.org/10.18383/j.tom.2019.00031

APA Style

McNitt-Gray, M., Napel, S., Jaggi, A., Mattonen, S. A., Hadjiiski, L., Muzi, M., Goldgof, D., Balagurunathan, Y., Pierce, L. A., Kinahan, P. E., Jones, E. F., Nguyen, A., Virkud, A., Chan, H. P., Emaminejad, N., Wahi-Anwar, M., Daly, M., Abdalah, M., Yang, H., ... Farahani, K. (2020). Standardization in Quantitative Imaging: A Multicenter Comparison of Radiomic Features from Different Software Packages on Digital Reference Objects and Patient Data Sets. Tomography, 6(2), 118-128. https://doi.org/10.18383/j.tom.2019.00031

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