Purpose To assess whether alterations in tumor blood volume (BV) and flow (BF) during the early course of chemo-radiation therapy (RT) for head and neck cancer (HNC) predict treatment outcome. Methods Fourteen patients receiving concomitant chemo-RT for non-resectable, locally advanced HNC underwent dynamic-contrast enhanced (DCE) MRI scans pre-therapy and two weeks after initiation of chemo-RT. BV and BF were quantified from DCE MRI. Pre-RT BV and BF as well as their changes during RT were evaluated separately in the primary gross tumor volume (GTV) and nodal GTV for association with outcomes. Results At a median follow-up of ten months (range of 5 – 27 months), nine patients had local-regional controlled disease (LRC). One patient had regional failure (RF), three had local failures (LF) and one had local-regional failure (LRF). Reduction in tumor volume after 2 weeks of chemo-RT did not predict for local control. In contrast, the BV in the primary GTV after 2 weeks of chemo-RT was increased significantly in the LC patients compared to the LF patients (p<0.03). Conclusions Our data suggest that an increase in available primary tumor blood for oxygen extraction during the early course of RT is associated with local control, thus yielding a predictor with potential to modify treatment. These findings require validation in larger studies.
Rationale and Objectives This study aims to investigate the sensitivity of quantitative metrics derived from dynamic contrast-enhanced (DCE) magnetic resonance imaging and a pharmacokinetic (PK) model to image quality and acquisition parameters. Materials and Methods A computer-synthesized DCE model that consisted of a large range of values of Ktrans (transfer constant of a paramagnetic contrast agent from blood to tissue), vp (fractional plasma volume), and kep (back flux rate) was created to test the reliability of quantitative metrics derived from a standard PK model. Effects of the contrast-to-noise ratio (CNR), total acquisition time, and sampling interval on the stability and bias of the derived metrics were investigated. Results The instability and bias of the estimated Ktrans, vp, and kep values increased with sampling interval and decreased with increasing CNR. Total acquisition times had limited influence on the estimations of Ktrans and vp values, but increasing the total acquisition time improved the stability of the estimation of kep values. However, for small kep values, the stability was still poor even with a total acquisition time of 8 minutes. Also, the stability and bias of the estimated values of Ktrans, vp, and kep are interrelated. Conclusions Our synthesized DCE model represents perfectly reproduced data except for the presence of Gaussian-distributed random noise. Our analysis suggests minimum changes that may be considered potentially significant in longitudinal therapy assessment studies. Our data are complementary to experimental data from human subjects and phantoms, and provide guidance for the design of image acquisition strategies.
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