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SU‐F‐303‐05: DCE‐MRI Before and During Treatment for Prediction of Concurrent Chemotherapy and Radiation Therapy Response in Head and Neck Cancer
Author(s) -
Liu Y,
Diwanji T,
Zhang B,
Zhuo J,
Gullapalli R,
Morales R,
D'souza W
Publication year - 2015
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.4925232
Subject(s) - medicine , radiation therapy , nuclear medicine , head and neck cancer , dynamic contrast enhanced mri , receiver operating characteristic , magnetic resonance imaging , chemotherapy , primary tumor , radiology , pharmacokinetics , cancer , metastasis
Purpose: To determine the ability of pharmacokinetic parameters derived from dynamic contrast‐enhanced MRI (DCE‐ MRI) acquired before and during concurrent chemotherapy and radiation therapy to predict clinical response in patients with head and neck cancer. Methods: Eleven patients underwent a DCE‐MRI scan at three time points: 1–2 weeks before treatment, 4–5 weeks after treatment initiation, and 3–4 months after treatment completion. Post‐processing of MRI data included correction to reduce motion artifacts. The arterial input function was obtained by measuring the dynamic tracer concentration in the jugular veins. The volume transfer constant (Ktrans), extracellular extravascular volume fraction (ve), rate constant (Kep; Kep = Ktrans/ve), and plasma volume fraction (vp) were computed for primary tumors and cervical nodal masses. Patients were categorized into two groups based on response to therapy at 3–4 months: responders (no evidence of disease) and partial responders (regression of disease). Responses of the primary tumor and nodes were evaluated separately. A linear classifier and receiver operating characteristic curve analyses were used to determine the best model for discrimination of responders from partial responders. Results: When the above pharmacokinetic parameters of the primary tumor measured before and during treatment were incorporated into the linear classifier, a discriminative accuracy of 88.9%, with sensitivity =100% and specificity = 66.7%, was observed between responders (n=6) and partial responders (n=3) for the primary tumor with the corresponding accuracy = 44.4%, sensitivity = 66.7%, and specificity of 0% for nodal masses. When only pre‐treatment parameters were used, the accuracy decreased to 66.7%, with sensitivity = 66.7% and specificity = 66.7% for the primary tumor and decreased to 33.3%, sensitivity of 50%, and specificity of 0% for nodal masses. Conclusion: Higher accuracy, sensitivity, and specificity were obtained using DCE‐MRI‐derived pharmacokinetic parameters acquired before and during treatment as compared with those derived from the pre‐treatment time‐point, exclusively.