Development and External Validation of a PET Radiomic Model for Prognostication of Head and Neck Cancer

Wyanne A. Noortman*, Nicolas Aide, Dennis Vriens, Lisa S. Arkes, Cornelis H. Slump, Ronald Boellaard, Jelle J. Goeman, Christophe M. Deroose, Jean Pascal Machiels, Lisa F. Licitra, Renaud Lhommel, Alessandra Alessi, Erwin Woff, Karolien Goffin, Christophe Le Tourneau, Jocelyn Gal, Stéphane Temam, Jean Pierre Delord, Floris H.P. van Velden, Lioe Fee de Geus-Oei

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

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Abstract

Aim: To build and externally validate an [18F]FDG PET radiomic model to predict overall survival in patients with head and neck squamous cell carcinoma (HNSCC). Methods: Two multicentre datasets of patients with operable HNSCC treated with preoperative afatinib who underwent a baseline and evaluation [18F]FDG PET/CT scan were included (EORTC: n = 20, Unicancer: n = 34). Tumours were delineated, and radiomic features were extracted. Each cohort served once as a training and once as an external validation set for the prediction of overall survival. Supervised feature selection was performed using variable hunting with variable importance, selecting the top two features. A Cox proportional hazards regression model using selected radiomic features and clinical characteristics was fitted on the training dataset and validated in the external validation set. Model performances are expressed by the concordance index (C-index). Results: In both models, the radiomic model surpassed the clinical model with validation C-indices of 0.69 and 0.79 vs. 0.60 and 0.67, respectively. The model that combined the radiomic features and clinical variables performed best, with validation C-indices of 0.71 and 0.82. Conclusion: Although assessed in two small but independent cohorts, an [18F]FDG-PET radiomic signature based on the evaluation scan seems promising for the prediction of overall survival for HNSSC treated with preoperative afatinib. The robustness and clinical applicability of this radiomic signature should be assessed in a larger cohort.

Original languageEnglish
Article number2681
JournalCancers
Volume15
Issue number10
DOIs
Publication statusPublished - May 2023

Keywords

  • afatinib
  • head and neck squamous cell carcinoma
  • machine learning
  • overall survival
  • radiomics
  • [F]FDG PET/CT

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