PV QA 3 - Poster Viewing Q&A 3
Purpose/Objective(s): Head and Neck cancer (HNC) patients often experience significant anatomical changes during the course of radiation treatment resulting in a higher risk in xerostomia. Adaptive Radiation Therapy (ART) in HNC, by regular monitoring anatomical changes of a patient, adapts the treatment plans to maintain the target coverage without elevating the risk of xerostomia. The monitoring is usually achieved by performing regular CBCT’s which are reviewed by attending physicians. The additional resources required for ART post a challenge for a broad-based implementation. Transit fluence is the photon fluence exiting the patient during radiation treatment. In this study, it is hypothesized that the change in transit fluence is associated with volumetric change in local anatomy, which is a decision support metric (DSM) in the ART process. This hypothesis is evaluated by comparing change in transit fluence and volume in five HNC patients.
Materials/Methods: The transit fluence is measured by an in-vivo portal dosimetry system (RTPD). Five patients are monitored with RTPD on a daily basis. Weekly cone beam computed tomography (CBCT) is performed. The first day of the treatment is used as the baseline. The region of interest (ROI) of each patient is defined as the neck and mandible inside the beam’s eye view. The integrated transit fluence, Dt,n, of the ROI of each patient at each fraction is calculated from the corresponding RTPD measurement using in-house software. The CBCT, performed on the same day, is first deformably registered to the planning CT with commercial software. The registered CBCT is imported to the treatment planning system to determine the volume of the ROI (VROI,n). The changes on nth day relative to the baseline, day 0, in the transit fluence, ΔDt, and the volume of the ROI, ΔVROI, are defined as the Dt,n – Dt,0 and VROI,n – VROI,0 respectively. The effectiveness of monitoring the volumetric change is assessed by calculating the correlation and t-statistics between ΔDt and ΔVROI.
Results: A total of 18 pairs of CBCT and RTPD measurements were obtained. The correlation, excluding the baseline points, between ΔDt and ΔVROI were found to be -0.8962 with a p-value <0.001.
Conclusion: The statistically significant negative correlation between ΔDt and ΔVROI is attributed to the increase in the photon fluence transport resulting from the ROI volume reduction. Change in transit fluence over the course of treatment can likely be used as a DSM for clinicians to expedite the patient selection for replanning in ART without the need for serial CBCT. The correlation will be further verified with a larger sample of data.
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