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Tomography
Volume 6
Issue 2
10.18383/j.tom.2020.00004
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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.
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Article

Multisite Technical and Clinical Performance Evaluation of Quantitative Imaging Biomarkers from 3D FDG PET Segmentations of Head and Neck Cancer Images

by
Brian J. Smith
1,*,
John M. Buatti
3,
Christian Bauer
2,
Ethan J. Ulrich
2,4,
Payam Ahmadvand
5,
Mikalai M. Budzevich
6,
Robert J. Gillies
6,
Dmitry Goldgof
7,
Milan Grkovski
8,
Ghassan Hamarneh
5,
Paul E. Kinahan
10,
John P. Muzi
10,
Mark Muzi
10,
Charles M. Laymon
11,12,
James M. Mountz
12,
Sadek Nehmeh
13,
Matthew J. Oborski
11,
Binsheng Zhao
9,
John J. Sunderland
14 and
Reinhard R. Beichel
2
1
Departments of Biostatistics, The University of Iowa, 145 N Riverside Dr, Iowa City, IA 52252-2007, USA
2
Departments of Electrical and Computer Engineering, The University of Iowa, Iowa City, IA, USA
3
Departments of Radiation Oncology, The University of Iowa, Iowa City, IA, USA
4
Departments of Biomedical Engineering, The University of Iowa, Iowa City, IA, USA
5
School of of Computing Science, Simon Fraser University, Burnaby, BC, Canada
6
H. Lee Moffitt Cancer Center & Research Institute, Department of Cancer Physiology, Tampa, FL, USA
7
Department of Computer Science and Engineering, University of South Florida, Tampa, FL, USA
8
Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
9
Department of Radiology, Columbia University Medical Center, New York, NY, USA
10
Department of Radiology, The University of Washington Medical Center, Seattle, WA, USA
11
Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA
12
Department of Radiology, University of Pittsburgh, Pittsburgh, PA, USA
13
Department of Radiology, Weill Cornell Medical College, New York, NY, USA
14
Department of Radiology, The University of Iowa, Iowa City, IA, USA
 
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*
Author to whom correspondence should be addressed.
Tomography 2020, 6(2), 65-76; https://doi.org/10.18383/j.tom.2020.00004
Submission received: 9 March 2020 / Revised: 8 April 2020 / Accepted: 3 May 2020 / Published: 1 June 2020
Versions Notes

Abstract

Quantitative imaging biomarkers (QIBs) provide medical image–derived intensity, texture, shape, and size features that may help characterize cancerous tumors and predict clinical outcomes. Successful clinical translation of QIBs depends on the robustness of their measurements. Biomarkers derived from positron emission tomography images are prone to measurement errors owing to differences in image processing factors such as the tumor segmentation method used to define volumes of interest over which to calculate QIBs. We illustrate a new Bayesian statistical approach to characterize the robustness of QIBs to different processing factors. Study data consist of 22 QIBs measured on 47 head and neck tumors in 10 positron emission tomography/computed tomography scans segmented manually and with semiautomated methods used by 7 institutional members of the NCI Quantitative Imaging Network. QIB performance is estimated and compared across institutions with respect to measurement errors and power to recover statistical associations with clinical outcomes. Analysis findings summarize the performance impact of different segmentation methods used by Quantitative Imaging Network members. Robustness of some advanced biomarkers was found to be similar to conventional markers, such as maximum standardized uptake value. Such similarities support current pursuits to better characterize disease and predict outcomes by developing QIBs that use more imaging information and are robust to different processing factors. Nevertheless, to ensure reproducibility of QIB measurements and measures of association with clinical outcomes, errors owing to segmentation methods need to be reduced.
Keywords: FDG PET; head and neck cancer; multi-site performance analysis; radiomics; segmentation FDG PET; head and neck cancer; multi-site performance analysis; radiomics; segmentation

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MDPI and ACS Style

Smith, B.J.; Buatti, J.M.; Bauer, C.; Ulrich, E.J.; Ahmadvand, P.; Budzevich, M.M.; Gillies, R.J.; Goldgof, D.; Grkovski, M.; Hamarneh, G.; et al. Multisite Technical and Clinical Performance Evaluation of Quantitative Imaging Biomarkers from 3D FDG PET Segmentations of Head and Neck Cancer Images. Tomography 2020, 6, 65-76. https://doi.org/10.18383/j.tom.2020.00004

AMA Style

Smith BJ, Buatti JM, Bauer C, Ulrich EJ, Ahmadvand P, Budzevich MM, Gillies RJ, Goldgof D, Grkovski M, Hamarneh G, et al. Multisite Technical and Clinical Performance Evaluation of Quantitative Imaging Biomarkers from 3D FDG PET Segmentations of Head and Neck Cancer Images. Tomography. 2020; 6(2):65-76. https://doi.org/10.18383/j.tom.2020.00004

Chicago/Turabian Style

Smith, Brian J., John M. Buatti, Christian Bauer, Ethan J. Ulrich, Payam Ahmadvand, Mikalai M. Budzevich, Robert J. Gillies, Dmitry Goldgof, Milan Grkovski, Ghassan Hamarneh, and et al. 2020. "Multisite Technical and Clinical Performance Evaluation of Quantitative Imaging Biomarkers from 3D FDG PET Segmentations of Head and Neck Cancer Images" Tomography 6, no. 2: 65-76. https://doi.org/10.18383/j.tom.2020.00004

APA Style

Smith, B. J., Buatti, J. M., Bauer, C., Ulrich, E. J., Ahmadvand, P., Budzevich, M. M., Gillies, R. J., Goldgof, D., Grkovski, M., Hamarneh, G., Kinahan, P. E., Muzi, J. P., Muzi, M., Laymon, C. M., Mountz, J. M., Nehmeh, S., Oborski, M. J., Zhao, B., Sunderland, J. J., & Beichel, R. R. (2020). Multisite Technical and Clinical Performance Evaluation of Quantitative Imaging Biomarkers from 3D FDG PET Segmentations of Head and Neck Cancer Images. Tomography, 6(2), 65-76. https://doi.org/10.18383/j.tom.2020.00004

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