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

S.A. Keek, F.W.R. Wesseling, H.C. Woodruff, J.E. van Timmeren, I.H. Nauta, T.K. Hoffmann, S. Cavalieri, G. Calareso, S. Primakov, R.T.H. Leijenaar, L. Licitra, M. Ravanelli, K. Scheckenbach, T. Poli, D. Lanfranco, M.R. Vergeer, C.R. Leemans, R.H. Brakenhoff, F.J.P. Hoebers, P. Lambin*

*Corresponding author for this work

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Abstract

Simple Summary Patients that suffer from advanced head and neck cancer have a low average survival chance. Improving prognosis could improve this survival rate as it may help in clinical decision making. Radiomics features calculated from images of the tumour describe tumour size, shape, and pattern. These characteristics may be linked to patient survival, which is investigated in this paper. We combined radiomics features with other biomarkers of survival of 809 patients to make a prognosis before treatment. We then compared the predicted prognosis with the actual outcome to see how well our model performs. Our model was able to make three distinct risk groups of low-, medium-, and high-survival patients. With these findings, doctors may make a better judgement of treatment and follow-up per patient, which might improve clinical outcomes. Background: Locoregionally advanced head and neck squamous cell carcinoma (HNSCC) patients have high relapse and mortality rates. Imaging-based decision support may improve outcomes 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 multivariable 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 radiomics 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 center dot 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
Number of pages17
JournalCancers
Volume13
Issue number13
DOIs
Publication statusPublished - 1 Jul 2021

Keywords

  • radiomics
  • machine learning
  • precision medicine
  • head and neck cancer
  • survival study
  • CANCER PATIENTS
  • PREDICTION
  • SURVIVAL
  • DELINEATION
  • DIAGNOSIS
  • LARYNGEAL
  • PET

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