A prospectively validated prognostic model for patients with locally advanced squamous cell carcinoma of the head and neck based on radiomics of computed tomography images

Simon A. Keek, Frederik W.R. Wesseling, Henry C. Woodruff, Janita E. van Timmeren, Irene H. Nauta, Thomas K. Hoffmann, Stefano Cavalieri, Giuseppina Calareso, Sergey Primakov, Ralph T.H. Leijenaar, Lisa Licitra, Marco Ravanelli, Kathrin Scheckenbach, Tito Poli, Davide Lanfranco, Marije R. Vergeer, C. René Leemans, Ruud H. Brakenhoff, Frank J.P. Hoebers, Philippe Lambin

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Background: Locoregionally advanced head and neck squamous cell carcinoma (HNSCC) patients have high relapse and mortality rates. Imaging-based decision support may improve out-comes by optimising personalised treatment, and support patient risk stratification. We propose a multifactorial prognostic model including radiomics features to improve risk stratification for advanced HNSCC, compared to TNM eighth edition, the gold standard. Patient and methods: Data of 666 retrospective-and 143 prospective-stage III-IVA/B HNSCC patients were collected. A multivar-iable Cox proportional-hazards model was trained to predict overall survival (OS) using diagnostic CT-based radiomics features extracted from the primary tumour. Separate analyses were performed using TNM8, tumour volume, clinical and biological variables, and combinations thereof with radi-omics features. Patient risk stratification in three groups was assessed through Kaplan–Meier (KM) curves. A log-rank test was performed for significance (p-value < 0.05). The prognostic accuracy was reported through the concordance index (CI). Results: A model combining an 11-feature radiomics signature, clinical and biological variables, TNM8, and volume could significantly stratify the validation cohort into three risk groups (p < 0∙01, CI of 0.79 as validation). Conclusion: A combination of radiomics features with other predictors can predict OS very accurately for advanced HNSCC patients and improves on the current gold standard of TNM8.

Original languageEnglish
Article number3271
Issue number13
Publication statusPublished - 1 Jul 2021


  • Head and neck cancer
  • Machine learning
  • Precision medicine
  • Radiomics
  • Survival study

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