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Tomography
Volume 5
Issue 1
10.18383/j.tom.2018.00038
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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

FLT PET Radiomics for Response Prediction to Chemoradiation Therapy in Head and Neck Squamous Cell Cancer

by
Ethan J. Ulrich
1,2,
Yusuf Menda
3,
Laura L. Boles Ponto
3,
Carryn M. Anderson
4,
Brian J. Smith
5,
John J. Sunderland
3,
Michael M. Graham
3,
John M. Buatti
4 and
Reinhard R. Beichel
1,6,*
1
Department of Electrical and Computer Engineering, University of Iowa, 4016 Seamans Center for the Engineering Arts and Sciences, Iowa City, IA 52242, USA
2
Department of Biomedical Engineering, University of Iowa, Iowa City, IA, USA
3
Department of Radiology, University of Iowa, Iowa City, IA, USA
4
Department of Radiation Oncology, University of Iowa, Iowa City, IA, USA
5
Department of Biostatistics, University of Iowa, Iowa City, IA, USA
6
Department of Internal Medicine, University of Iowa, Iowa City, IA 52242, USA
*
Author to whom correspondence should be addressed.
Tomography 2019, 5(1), 161-169; https://doi.org/10.18383/j.tom.2018.00038
Submission received: 12 December 2018 / Revised: 6 January 2019 / Accepted: 10 February 2019 / Published: 1 March 2019
Versions Notes

Abstract

Radiomics is an image analysis approach for extracting large amounts of quantitative information from medical images using a variety of computational methods. Our goal was to evaluate the utility of radiomic feature analysis from 18F-fluorothymidine positron emission tomography (FLT PET) obtained at baseline in prediction of treatment response in patients with head and neck cancer. Thirty patients with advanced-stage oropharyngeal or laryngeal cancer, treated with definitive chemoradiation therapy, underwent FLT PET imaging before treatment. In total, 377 radiomic features of FLT uptake and feature variants were extracted from volumes of interest; these features variants were defined by either the primary tumor or the total lesion burden, which consisted of the primary tumor and all FLT-avid nodes. Feature variants included normalized measurements of uptake, which were calculated by dividing lesion uptake values by the mean uptake value in the bone marrow. Feature reduction was performed using clustering to remove redundancy, leaving 172 representative features. Effects of these features on progression-free survival were modeled with Cox regression and P-values corrected for multiple comparisons. In total, 9 features were considered significant. Our results suggest that smaller, more homogenous lesions at baseline were associated with better prognosis. In addition, features extracted from total lesion burden had a higher concordance index than primary tumor features for 8 of the 9 significant features. Furthermore, total lesion burden features showed lower interobserver variability.
Keywords: PET; FLT; radiomics; prediction; head and neck cancer PET; FLT; radiomics; prediction; head and neck cancer

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MDPI and ACS Style

Ulrich, E.J.; Menda, Y.; Ponto, L.L.B.; Anderson, C.M.; Smith, B.J.; Sunderland, J.J.; Graham, M.M.; Buatti, J.M.; Beichel, R.R. FLT PET Radiomics for Response Prediction to Chemoradiation Therapy in Head and Neck Squamous Cell Cancer. Tomography 2019, 5, 161-169. https://doi.org/10.18383/j.tom.2018.00038

AMA Style

Ulrich EJ, Menda Y, Ponto LLB, Anderson CM, Smith BJ, Sunderland JJ, Graham MM, Buatti JM, Beichel RR. FLT PET Radiomics for Response Prediction to Chemoradiation Therapy in Head and Neck Squamous Cell Cancer. Tomography. 2019; 5(1):161-169. https://doi.org/10.18383/j.tom.2018.00038

Chicago/Turabian Style

Ulrich, Ethan J., Yusuf Menda, Laura L. Boles Ponto, Carryn M. Anderson, Brian J. Smith, John J. Sunderland, Michael M. Graham, John M. Buatti, and Reinhard R. Beichel. 2019. "FLT PET Radiomics for Response Prediction to Chemoradiation Therapy in Head and Neck Squamous Cell Cancer" Tomography 5, no. 1: 161-169. https://doi.org/10.18383/j.tom.2018.00038

APA Style

Ulrich, E. J., Menda, Y., Ponto, L. L. B., Anderson, C. M., Smith, B. J., Sunderland, J. J., Graham, M. M., Buatti, J. M., & Beichel, R. R. (2019). FLT PET Radiomics for Response Prediction to Chemoradiation Therapy in Head and Neck Squamous Cell Cancer. Tomography, 5(1), 161-169. https://doi.org/10.18383/j.tom.2018.00038

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