Using imaging biomarkers to predict radiation induced xerostomia in head and neck cancer

Khadija Sheikh, Sang Ho Lee, Zhi Cheng, Pranav Lakshminarayanan, Luke Peng, Peijin Han, Todd R. McNutt, Harry Quon, Junghoon Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2019
Subtitle of host publicationImaging Informatics for Healthcare, Research, and Applications
EditorsPo-Hao Chen, Peter R. Bak
PublisherSPIE
ISBN (Electronic)9781510625556
DOIs
StatePublished - 2019
EventMedical Imaging 2019: Imaging Informatics for Healthcare, Research, and Applications - San Diego, United States
Duration: Feb 17 2019Feb 18 2019

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume10954
ISSN (Print)1605-7422

Conference

ConferenceMedical Imaging 2019: Imaging Informatics for Healthcare, Research, and Applications
CountryUnited States
CitySan Diego
Period2/17/192/18/19

Keywords

  • CT
  • Head and neck cancer
  • MRI
  • Radiomics
  • Radiotherapy
  • Xerostomia

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Biomaterials
  • Atomic and Molecular Physics, and Optics
  • Radiology Nuclear Medicine and imaging

Fingerprint Dive into the research topics of 'Using imaging biomarkers to predict radiation induced xerostomia in head and neck cancer'. Together they form a unique fingerprint.

  • Cite this

    Sheikh, K., Lee, S. H., Cheng, Z., Lakshminarayanan, P., Peng, L., Han, P., McNutt, T. R., Quon, H., & Lee, J. (2019). Using imaging biomarkers to predict radiation induced xerostomia in head and neck cancer. In P-H. Chen, & P. R. Bak (Eds.), Medical Imaging 2019: Imaging Informatics for Healthcare, Research, and Applications [109540W] (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 10954). SPIE. https://doi.org/10.1117/12.2512789