Publication

The prognostic value of CT-based image-biomarkers for head and neck cancer patients treated with definitive (chemo-)radiation

Zhai, T. T., Langendijk, J. A., van Dijk, L. V., Halmos, G. B., Witjes, M. J. H., Oosting, S. F., Noordzij, W., Sijtsema, N. M. & Steenbakkers, R. J. H. M., 1-Aug-2019, In : Oral Oncology. 95, p. 178-186 9 p.

Research output: Contribution to journalArticleAcademicpeer-review

Objectives: The aim of this study was to investigate whether quantitative CT image-biomarkers (IBMs) can improve the prediction models with only classical prognostic factors for local-control (LC), regional-control (RC), distant metastasis-free survival (DMFS) and disease-free survival (DFS) for head and neck cancer (HNC) patients.

Materials and Methods: The cohort included 240 and 204 HNC patients in the training and validation analysis, respectively. Clinical variables were scored prospectively and IBMs of the primary tumor and lymph nodes were extracted from planning CT-images. Clinical, IBM and combined models were created from multivariable Cox proportional-hazard analyses based on clinical features, IBMs, and both for LC, RC, DMFS and DFS.

Results: Clinical variables identified in the multivariable analysis included tumor-site, WHO performance-score, tumor-stage and age. Bounding-box-volume describing the tumor volume and irregular shape, IBM correlation representing radiological heterogeneity, and LN_major-axis-length showing the distance between lymph nodes were included in the IBM models. The performance of IBM LC, RC, DMFS and DFS models (c-index(validated): 0.62, 0.80, 0.68 and 0.65) were comparable to that of the clinical models (0.62, 0.76, 0.70 and 0.66). The combined DFS model (0.70) including clinical features and IBMs performed significantly better than the clinical model. Patients stratified with the combined models revealed larger differences between risk groups in the validation cohort than with clinical models for LC, RC and DFS. For DMFS, the differences were similar to the clinical model.

Conclusion: For prediction of HNC treatment outcomes, image-biomarkers performed as good as or slightly better than clinical variables.

Original languageEnglish
Pages (from-to)178-186
Number of pages9
JournalOral Oncology
Volume95
Publication statusPublished - 1-Aug-2019

    Keywords

  • Head and neck cancer, Image-biomarker, Radiomics, Local-regional recurrence, Metastasis-free survival, Disease-free survival, Radiotherapy, Treatment outcome, Prediction model, INTRATUMOR HETEROGENEITY, OROPHARYNGEAL CANCER, HUMAN-PAPILLOMAVIRUS, SURVIVAL, PREDICTION, RADIOTHERAPY, EVOLUTION, IMPACT, MODEL, RISK

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