Abstract

This study used a hybrid near-infrared diffuse optical instrument to monitor tumor hemodynamic responses to chemoradiation therapy for early prediction of treatment outcomes in patients with head and neck cancer. Forty-seven patients were measured once per week to evaluate the hemodynamic status of clinically involved cervical lymph nodes as surrogates for the primary tumor response. Patients were classified into two groups: complete response (CR) (n = 29) and incomplete response (IR) (n = 18). Tumor hemodynamic responses were found to be associated with clinical outcomes (CR/IR), wherein the associations differed depending on human papillomavirus (HPV-16) status. In HPV-16 positive patients, significantly lower levels in tumor oxygenated hemoglobin concentration ([HbO2]) at weeks 1 to 3, total hemoglobin concentration at week 3, and blood oxygen saturation (StO2) at week 3 were found in the IR group. In HPV-16 negative patients, significantly higher levels in tumor blood flow index and reduced scattering coefficient (μ′s) at week 3 were observed in the IR group. These hemodynamic parameters exhibited significantly high accuracy for early prediction of clinical outcomes, within the first three weeks of therapy, with the areas under the receiver operating characteristic curves (AUCs) ranging from 0.83 to 0.96.

Document Type

Article

Publication Date

8-26-2016

Notes/Citation Information

Published in Journal of Biomedical Optics, v. 21, no. 8, 085004, p. 1-11.

Lixin Dong, Mahesh Kudrimoti, Daniel Irwin, Li Chen, Sameera Kumar, Yu Shang, Chong Huang, Ellis L. Johnson, Scott D. Stevens, Brent J. Shelton, Guoqiang Yu, "Diffuse optical measurements of head and neck tumor hemodynamics for early prediction of chemoradiation therapy outcomes," Journal of Biomedical Optics 21(8), 085004 (August 26, 2016). http://dx.doi.org/10.1117/1.JBO.21.8.085004

Copyright 2016 Society of Photo Optical Instrumentation Engineers. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited.

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Digital Object Identifier (DOI)

https://doi.org/10.1117/1.JBO.21.8.085004

Funding Information

This work was partially supported by the National Institutes of Health under Grant R01 CA149274 (GY), Grant R21 AR062356 (GY), Grant UL1RR033173 (GY), and the Biostatistics and Bioinformatics Shared Resource Facility of the University of Kentucky Markey Cancer Center (P30CA177558).

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