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Using imaging biomarkers to predict radiation induced xerostomia in head and neck cancer
Author(s): Khadija Sheikh; Sang Ho Lee; Zhi Cheng; Pranav Lakshminarayanan; Luke Peng; Peijin Han; Todd R. McNutt; Harry Quon; Junghoon Lee
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Paper Abstract

In this study, we analyzed baseline CT- and MRI-based image features of salivary glands to predict radiation-induced xerostomia after head-and-neck cancer (HNC) radiotherapy. A retrospective analysis was performed on 216 HNC patients who were treated using radiotherapy at a single institution between 2009 and 2016. CT and T1 post-contrast MR images along with NCI-CTCAE xerostomia grade (3-month follow-up) were prospectively collected at our institution. Image features were extracted for ipsilateral/contralateral parotid and submandibular glands relative to the location of the primary tumor. Dose-volume-histogram (DVH) parameters were also acquired. Features that were correlated with xerostomia (p<0.05) were further reduced using a LASSO logistic regression. Generalized Linear Model (GLM) and the Support Vector Machine (SVM) classifiers were used to predict xerostomia under five conditions (DVH-only, CT-only, MR-only, CT+MR, and DVH+CT+MR) using a ten-fold cross validation. The prediction performance was determined using the area under the receiver operator characteristic curve (ROC-AUC). DeLong’s test was used to determine the difference between the ROC curves. Among extracted features, 13 CT, 6 MR, and 4 DVH features were selected. The ROC-AUC values for GLM/SVM classifiers with DVH, CT, MR, CT+MR and all features were 0.72±0.01/0.72±0.01, 0.73±0.01/0.68±0.01, 0.68±0.01/0.63±0.01, 0.74±0.01/0.75±0.01, and 0.78±0.01/0.79±0.01, respectively. DeLong’s test demonstrated an improved in AUC for both classifiers with the addition of all features compared to DVH, CT, and MR-alone (p<0.05) and the SVM CT+MR model (p=0.03). The integration of baseline image features into prediction models has the potential to improve xerostomia risk stratification with the ultimate goal of personalized HNC radiotherapy.

Paper Details

Date Published: 15 March 2019
PDF: 8 pages
Proc. SPIE 10954, Medical Imaging 2019: Imaging Informatics for Healthcare, Research, and Applications, 109540W (15 March 2019); doi: 10.1117/12.2512789
Show Author Affiliations
Khadija Sheikh, Johns Hopkins Univ. School of Medicine (United States)
Sang Ho Lee, Johns Hopkins Univ. School of Medicine (United States)
Zhi Cheng, Johns Hopkins Univ. School of Medicine (United States)
Pranav Lakshminarayanan, Johns Hopkins Univ. School of Medicine (United States)
Luke Peng, Johns Hopkins Univ. School of Medicine (United States)
Peijin Han, Johns Hopkins Univ. School of Medicine (United States)
Todd R. McNutt, Johns Hopkins Univ. School of Medicine (United States)
Harry Quon, Johns Hopkins Univ. School of Medicine (United States)
Junghoon Lee, Johns Hopkins Univ. School of Medicine (United States)


Published in SPIE Proceedings Vol. 10954:
Medical Imaging 2019: Imaging Informatics for Healthcare, Research, and Applications
Po-Hao Chen; Peter R. Bak, Editor(s)

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