Design and selection of machine learning methods using radiomics and dosiomics for normal tissue complication probability modeling of xerostomia

• Verfasst von: Gabryś, Hubert [VerfasserIn]
• Verfasst von: Sterzing, Florian [VerfasserIn]
• Verfasst von: Hauswald, Henrik [VerfasserIn]
• Verfasst von: Bangert, Mark [VerfasserIn]
• Titel: Design and selection of machine learning methods using radiomics and dosiomics for normal tissue complication probability modeling of xerostomia
• Verf.angabe: Hubert S. Gabryś, Florian Buettner, Florian Sterzing, Henrik Hauswald and Mark Bangert
• E-Jahr: 2018
• Jahr: 05 March 2018
• Umfang: 20 S.
• Fussnoten: Gesehen am 05.08.2019
• Titel Quelle: Enthalten in: Frontiers in oncology
• Ort Quelle: Lausanne : Frontiers Media, 2011
• Jahr Quelle: 2018
• Band/Heft Quelle: 8(2018) Artikel-Nummer 35, 20 Seiten
• ISSN Quelle: 2234-943X
• DOI: doi:10.3389/fonc.2018.00035
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: dosiomics
• Sach-SW: head and neck
• Sach-SW: IMRT
• Sach-SW: machine learning
• Sach-SW: NTCP
• Sach-SW: Radiomics
• Sach-SW: Radiotherapy
• Sach-SW: Xerostomia
• K10plus-PPN: 1670568237

Abstract

Purpose: 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 0.85), 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. Conclusions: 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.