Original Research ARTICLE
Design and selection of machine learning methods using radiomics and dosiomics for NTCP modeling of xerostomia
- 1Department of Medical Physics in Radiation Oncology, Deutsches Krebsforschungszentrum (DKFZ), Germany
- 2Medical Faculty of Heidelberg, Universität Heidelberg, Germany
- 3Heidelberg Institute for Radiation Oncology (HIRO), Germany
- 4European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), United Kingdom
- 5Institute of Computational Biology, Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt, Germany
- 6Clinical Cooperation Unit Radiation Oncology, Deutsches Krebsforschungszentrum (DKFZ), Germany
- 7Department of Radiation Oncology, University Hospital Heidelberg, Germany
To investigate whether machine learning with dosiomic, radiomic, and demographic features allows for xerostomia risk assessment more precise than normal tissue complication probability (NTCP) models based on the mean radiation dose to parotid glands.
Material and methods:
A cohort of 153 head-and-neck cancer patients was used to model xerostomia at 0-6 months (early), 6-15 months (late), 15-24 months (long-term), and at any time (a longitudinal model) after radiotherapy.
Predictive power of the features was evaluated by the area under the receiver operating characteristic curve (AUC) of univariate logistic regression models.
The multivariate NTCP models were tuned and tested with single and nested cross-validation, respectively.
We compared predictive performance of seven classification algorithms, six feature selection methods, and ten data cleaning/class balancing techniques using the Friedman test and the Nemenyi post-hoc analysis.
NTCP models based on the parotid mean dose failed to predict xerostomia (AUCs < 0.60).
The most informative predictors were found for late and long-term xerostomia.
Late xerostomia correlated with the contralateral dose gradient in the anterior-posterior (AUC = 0.72) and the right-left (AUC = 0.68) direction, whereas long-term xerostomia was associated with parotid volumes (AUCs > 0.85), dose gradients in the right-left (AUCs > 0.78), and the anterior-posterior (AUCs > 0.72) direction.
Multivariate models of long-term xerostomia were typically based on the parotid volume, the parotid eccentricity, and the dose-volume histogram (DVH) spread with the generalization AUCs ranging from 0.74 to 0.88.
On average, support vector machines and extra-trees were the top performing classifiers, whereas the algorithms based on logistic regression were the best choice for feature selection.
We found no advantage in using data cleaning or class balancing methods.
We demonstrated that incorporation of organ- and dose-shape descriptors is beneficial for xerostomia prediction in highly conformal radiotherapy treatments.
Due to strong reliance on patient-specific, dose-independent factors, our results underscore the need for development of personalized data-driven risk profiles for NTCP models of xerostomia.
The facilitated machine learning pipeline is described in detail and can serve as a valuable reference for future work in radiomic and dosiomic NTCP modeling.
Keywords: Radiotherapy, IMRT, NTCP, Xerostomia, head and neck, machine learning, Radiomics, dosiomics
Received: 21 Nov 2017;
Accepted: 01 Feb 2018.
Edited by:Issam El Naqa, University of Michigan, United States
Reviewed by:John A. Vargo, West Virginia University Hospitals, United States
John C. Roeske, Loyola University Medical Center, United States
Copyright: © 2018 Gabryś, Buettner, Sterzing, Hauswald and Bangert. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Mr. Hubert S. Gabryś, Deutsches Krebsforschungszentrum (DKFZ), Department of Medical Physics in Radiation Oncology, Heidelberg, Germany, firstname.lastname@example.org
Dr. Mark Bangert, Deutsches Krebsforschungszentrum (DKFZ), Department of Medical Physics in Radiation Oncology, Heidelberg, Germany, email@example.com