Predictive value of quantitative 18F-FDG-PET radiomics analysis in patients with head and neck squamous cell carcinoma
Martens, Roland M; Koopman, Thomas; Noij, Daniel P; Pfaehler, Elisabeth; Übelhör, Caroline; Sharma, Sughandi; Vergeer, Marije R; Leemans, C René; Hoekstra, Otto S; Yaqub, Maqsood; Zwezerijnen, Gerben J; Heymans, Martijn W; Peeters, Carel F W; de Bree, Remco; de Graaf, Pim; Castelijns, Jonas A; Boellaard, Ronald
(2020) EJNMMI Research, volume 10, issue 1, pp. 1 - 15
(Article)
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
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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.
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Keywords: Head and Neck Neoplasms, Positron Emission Tomography Computed Tomography, Prognosis, Radiomics, Radiology Nuclear Medicine and imaging
ISSN: 2191-219X
Publisher: Springer Berlin
Note: Funding Information: The authors thank the Amsterdam University Medical Center, clinical staff of the Department of Otolaryngology-Head and Neck Surgery (Chief: Prof. Dr. CR Leemans), Department of Radiology and Nuclear Medicine (Chief: Prof. Dr. C van Kuijk) and Dr. CS Schouten for help in successfully completing the studies. Funding Information: This work was funded by the Netherlands Organization for Health Research and Development, grant 10-10400-98-14002 and in part by the research program STRaTeGy with project number 14929, which is financed by the Netherlands Organization for Scientific Research (NWO). Acknowledgements Publisher Copyright: © 2020, The Author(s).
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