Improved outcome prediction of oropharyngeal cancer by combining clinical and MRI features in machine learning models.

Abstract

METHODS

177 OPSCC patients were eligible for this study. Radiomic features were extracted from the primary tumor region in T1-weighted postcontrast MRI acquired before chemoradiation. Logistic regression models were created using either clinical variables (clinical model), radiomic features (radiomic model) or clinical and radiomic features combined (combined model) to predict LRC and OS 2-years posttreatment. Model performance was evaluated using area under the curve (AUC), 95 % confidence intervals were calculated using 500 iterations of bootstrap. All analyses were performed for the total population and the Human papillomavirus (HPV) negative tumor subgroup.

CONCLUSION

Predictive models that include clinical variables and radiomic tumor features derived from MR images of OPSCC better predict LRC after chemoradiation than models based on only clinical variables. Predictive models that include clinical variables perform better than models based on only radiomic features for the prediction of OS.

RESULTS

A combined model predicted treatment outcome with a higher AUC (LRC: 0.745 [0.734-0.757], OS: 0.744 [0.735-0.753]) than the clinical model (LRC: 0.607 [0.594-0.620], OS: 0.708 [0.697-0.719]). Performance of the radiomic model was comparable to the combined model for LRC (AUC: 0.740 [0.729-0.750]), but not for OS prediction (AUC: 0.654 [0.646-0.662]). In HPV negative patients, the performance of all models was not sufficient with AUCs ranging from 0.587 to 0.660 for LRC and 0.559 to 0.600 for OS prediction.

OBJECTIVES

New markers are required to predict chemoradiation response in oropharyngeal squamous cell carcinoma (OPSCC) patients. This study evaluated the ability of magnetic resonance (MR) radiomics to predict locoregional control (LRC) and overall survival (OS) after chemoradiation and aimed to determine whether this has added value to traditional clinical outcome predictors.

More about this publication

European journal of radiology
  • Volume 139
  • Pages 109701
  • Publication date 01-06-2021

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