Highlights
- •Chemoradiotherapy induces hearing loss in head and neck cancer patients.
- •Radiomics is a new approach to assess and predict radiotherapy induced injuries.
- •CT radiomic could help in the prediction of hearing loss induced by chemoradiation.
- •Combination of radiomic features with clinical and dosimetric variables can model hearing loss.
Abstract
Objectives
Immediately or after head-and-neck (H&N) cancer chemoradiotherapy (CRT), patients
may undergone significant sensorineural hearing loss (SNHL) which could affect their
quality of life. Radiomic feature analysis is proposed to predict SNHL induced by
CRT.
Material and methods
490 image features of 94 cochlea from 47 patients treated with three dimensional conformal
RT (3DCRT) for different H&N cancers were extracted from CT images. Different machine
learning (ML) algorithms and also least absolute shrinkage and selection operator
(LASSO) penalized logistic regression were implemented on radiomic features for feature
selection, classification and prediction. Also, LASSO penalized logistic model was
used for outcome modelling.
Results
The predictive power of ten ML methods was more than 70% (in accuracy, precision and
area under the curve of receiver operating characteristic curve (AUC)). According
to the LASSO penalized logistic modelling, 10 of the 490 radiomic features selected
as the associated features with SNHL status. All of the 10 features were statistically
associated with SNHL (all of adjusted P-values < .001).
Conclusion
CT radiomic analysis proposed in this study, could help in the prediction of hearing
loss induced by chemoradiation. Our study also, demonstrates that combination of radiomic
features with clinical and dosimetric variables can model radiotherapy outcome such
as SNHL.
Keywords
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Article info
Publication history
Published online: January 09, 2018
Accepted:
October 14,
2017
Received in revised form:
October 5,
2017
Received:
July 22,
2017
Identification
Copyright
© 2017 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.
