Functional Data Analysis Applied to Modeling of Severe Acute Mucositis and Dysphagia Resulting From Head and Neck Radiation Therapy

Jamie A. Dean, Kee H. Wong, Hiram Gay, Liam C. Welsh, Ann Britt Jones, Ulrike Schick, Jung Hun Oh, Aditya Apte, Kate L. Newbold, Shreerang A. Bhide, Kevin J. Harrington, Joseph O. Deasy, Christopher M. Nutting, Sarah L. Gulliford

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9 Scopus citations

Abstract

Purpose Current normal tissue complication probability modeling using logistic regression suffers from bias and high uncertainty in the presence of highly correlated radiation therapy (RT) dose data. This hinders robust estimates of dose-response associations and, hence, optimal normal tissue–sparing strategies from being elucidated. Using functional data analysis (FDA) to reduce the dimensionality of the dose data could overcome this limitation. Methods and Materials FDA was applied to modeling of severe acute mucositis and dysphagia resulting from head and neck RT. Functional partial least squares regression (FPLS) and functional principal component analysis were used for dimensionality reduction of the dose-volume histogram data. The reduced dose data were input into functional logistic regression models (functional partial least squares–logistic regression [FPLS-LR] and functional principal component–logistic regression [FPC-LR]) along with clinical data. This approach was compared with penalized logistic regression (PLR) in terms of predictive performance and the significance of treatment covariate–response associations, assessed using bootstrapping. Results The area under the receiver operating characteristic curve for the PLR, FPC-LR, and FPLS-LR models was 0.65, 0.69, and 0.67, respectively, for mucositis (internal validation) and 0.81, 0.83, and 0.83, respectively, for dysphagia (external validation). The calibration slopes/intercepts for the PLR, FPC-LR, and FPLS-LR models were 1.6/−0.67, 0.45/0.47, and 0.40/0.49, respectively, for mucositis (internal validation) and 2.5/−0.96, 0.79/−0.04, and 0.79/0.00, respectively, for dysphagia (external validation). The bootstrapped odds ratios indicated significant associations between RT dose and severe toxicity in the mucositis and dysphagia FDA models. Cisplatin was significantly associated with severe dysphagia in the FDA models. None of the covariates was significantly associated with severe toxicity in the PLR models. Dose levels greater than approximately 1.0 Gy/fraction were most strongly associated with severe acute mucositis and dysphagia in the FDA models. Conclusions FPLS and functional principal component analysis marginally improved predictive performance compared with PLR and provided robust dose-response associations. FDA is recommended for use in normal tissue complication probability modeling.

Original languageEnglish
Pages (from-to)820-831
Number of pages12
JournalInternational Journal of Radiation Oncology Biology Physics
Volume96
Issue number4
DOIs
StatePublished - Nov 15 2016

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    Dean, J. A., Wong, K. H., Gay, H., Welsh, L. C., Jones, A. B., Schick, U., Oh, J. H., Apte, A., Newbold, K. L., Bhide, S. A., Harrington, K. J., Deasy, J. O., Nutting, C. M., & Gulliford, S. L. (2016). Functional Data Analysis Applied to Modeling of Severe Acute Mucositis and Dysphagia Resulting From Head and Neck Radiation Therapy. International Journal of Radiation Oncology Biology Physics, 96(4), 820-831. https://doi.org/10.1016/j.ijrobp.2016.08.013