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      Design and Selection of Machine Learning Methods Using Radiomics and Dosiomics for Normal Tissue Complication Probability Modeling of Xerostomia

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          Abstract

          Purpose

          The purpose of this study is 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.

          Results

          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.

          Conclusion

          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.

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          Most cited references41

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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            The meaning and use of the area under a receiver operating characteristic (ROC) curve.

            A representation and interpretation of the area under a receiver operating characteristic (ROC) curve obtained by the "rating" method, or by mathematical predictions based on patient characteristics, is presented. It is shown that in such a setting the area represents the probability that a randomly chosen diseased subject is (correctly) rated or ranked with greater suspicion than a randomly chosen non-diseased subject. Moreover, this probability of a correct ranking is the same quantity that is estimated by the already well-studied nonparametric Wilcoxon statistic. These two relationships are exploited to (a) provide rapid closed-form expressions for the approximate magnitude of the sampling variability, i.e., standard error that one uses to accompany the area under a smoothed ROC curve, (b) guide in determining the size of the sample required to provide a sufficiently reliable estimate of this area, and (c) determine how large sample sizes should be to ensure that one can statistically detect differences in the accuracy of diagnostic techniques.
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              Wrappers for feature subset selection

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                Author and article information

                Contributors
                Journal
                Front Oncol
                Front Oncol
                Front. Oncol.
                Frontiers in Oncology
                Frontiers Media S.A.
                2234-943X
                05 March 2018
                2018
                : 8
                : 35
                Affiliations
                [1] 1Department of Medical Physics in Radiation Oncology, German Cancer Research Center (DKFZ) , Heidelberg, Germany
                [2] 2Medical Faculty of Heidelberg, Heidelberg University , Heidelberg, Germany
                [3] 3Heidelberg Institute for Radiation Oncology (HIRO) , Heidelberg, Germany
                [4] 4Institute of Computational Biology, Helmholtz Zentrum München , Neuherberg, Germany
                [5] 5Clinical Cooperation Unit Radiation Oncology, German Cancer Research Center (DKFZ) , Heidelberg, Germany
                [6] 6Department of Radiation Oncology, Heidelberg University Hospital , Heidelberg, Germany
                Author notes

                Edited by: Issam El Naqa, University of Michigan, United States

                Reviewed by: John C. Roeske, Loyola University Medical Center, United States; John Austin Vargo, West Virginia University Hospitals, United States

                *Correspondence: Hubert S. Gabryś, h.gabrys@ 123456dkfz.de ; Mark Bangert, m.bangert@ 123456dkfz.de

                Specialty section: This article was submitted to Radiation Oncology, a section of the journal Frontiers in Oncology

                Article
                10.3389/fonc.2018.00035
                5844945
                29556480
                c235993c-e850-43a7-90b8-a737fff190dc
                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.

                History
                : 21 November 2017
                : 01 February 2018
                Page count
                Figures: 8, Tables: 7, Equations: 17, References: 67, Pages: 20, Words: 12286
                Categories
                Oncology
                Original Research

                Oncology & Radiotherapy
                radiotherapy,imrt,ntcp,xerostomia,head and neck,machine learning,radiomics,dosiomics
                Oncology & Radiotherapy
                radiotherapy, imrt, ntcp, xerostomia, head and neck, machine learning, radiomics, dosiomics

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