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EJNMMI Research
Published in: EJNMMI Research 1/2020

Open Access 01-12-2020 | Computed Tomography | Original research

Predictive value of quantitative 18F-FDG-PET radiomics analysis in patients with head and neck squamous cell carcinoma

Authors: Roland M. Martens, Thomas Koopman, Daniel P. Noij, Elisabeth Pfaehler, Caroline Übelhör, Sughandi Sharma, Marije R. Vergeer, C. René Leemans, Otto S. Hoekstra, Maqsood Yaqub, Gerben J. Zwezerijnen, Martijn W. Heymans, Carel F. W. Peeters, Remco de Bree, Pim de Graaf, Jonas A. Castelijns, Ronald Boellaard

Published in: EJNMMI Research | Issue 1/2020

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Abstract

Background

Radiomics is aimed at image-based tumor phenotyping, enabling application within clinical-decision-support-systems to improve diagnostic accuracy and allow for personalized treatment. The purpose was to identify predictive 18-fluor-fluoro-2-deoxyglucose (18F-FDG) positron-emission tomography (PET) radiomic features to predict recurrence, distant metastasis, and overall survival in patients with head and neck squamous cell carcinoma treated with chemoradiotherapy.

Methods

Between 2012 and 2018, 103 retrospectively (training cohort) and 71 consecutively included patients (validation cohort) underwent 18F-FDG-PET/CT imaging. The 434 extracted radiomic features were subjected, after redundancy filtering, to a projection resulting in outcome-independent meta-features (factors). Correlations between clinical, first-order 18F-FDG-PET parameters (e.g., SUVmean), and factors were assessed. Factors were combined with 18F-FDG-PET and clinical parameters in a multivariable survival regression and validated. A clinically applicable risk-stratification was constructed for patients’ outcome.

Results

Based on 124 retained radiomic features from 103 patients, 8 factors were constructed. Recurrence prediction was significantly most accurate by combining HPV-status, SUVmean, SUVpeak, factor 3 (histogram gradient and long-run-low-grey-level-emphasis), factor 4 (volume-difference, coarseness, and grey-level-non-uniformity), and factor 6 (histogram variation coefficient) (CI = 0.645). Distant metastasis prediction was most accurate assessing metabolic-active tumor volume (MATV)(CI = 0.627). Overall survival prediction was most accurate using HPV-status, SUVmean, SUVmax, factor 1 (least-axis-length, non-uniformity, high-dependence-of-high grey-levels), and factor 5 (aspherity, major-axis-length, inversed-compactness and, inversed-flatness) (CI = 0.764).

Conclusions

Combining HPV-status, first-order 18F-FDG-PET parameters, and complementary radiomic factors was most accurate for time-to-event prediction. Predictive phenotype-specific tumor characteristics and interactions might be captured and retained using radiomic factors, which allows for personalized risk stratification and optimizing personalized cancer care.

Trial registration

Trial NL3946 (NTR4111), local ethics commission reference: Prediction 2013.191 and 2016.498. Registered 7 August 2013, https://​www.​trialregister.​nl/​trial/​3946
Appendix
Available only for authorised users
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Metadata
Title
Predictive value of quantitative 18F-FDG-PET radiomics analysis in patients with head and neck squamous cell carcinoma
Authors
Roland M. Martens
Thomas Koopman
Daniel P. Noij
Elisabeth Pfaehler
Caroline Übelhör
Sughandi Sharma
Marije R. Vergeer
C. René Leemans
Otto S. Hoekstra
Maqsood Yaqub
Gerben J. Zwezerijnen
Martijn W. Heymans
Carel F. W. Peeters
Remco de Bree
Pim de Graaf
Jonas A. Castelijns
Ronald Boellaard
Publication date
01-12-2020
Publisher
Springer Berlin Heidelberg
Published in
EJNMMI Research / Issue 1/2020
Electronic ISSN: 2191-219X
DOI
https://doi.org/10.1186/s13550-020-00686-2

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