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

Roland M. Martens*, Thomas Koopman, Daniel P. Noij, Elisabeth Pfaehler, Caroline Ubelhor, Sughandi Sharma, Marije R. Vergeer, C. Rene 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

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

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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 (F-18-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(18)F-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(18)F-FDG-PET parameters (e.g., SUVmean), and factors were assessed. Factors were combined with(18)F-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(18)F-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.

Original languageEnglish
Article number102
Number of pages15
JournalEJNMMI Research
Issue number1
Publication statusPublished - 7-Sep-2020


  • Head and Neck Neoplasms
  • Positron Emission Tomography Computed Tomography
  • Radiomics
  • Prognosis
  • PET
  • CT

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