Date Approved
8-25-2022
Graduate Degree Type
Project
Degree Name
Medical Dosimetry (M.S.)
Degree Program
Health Professions
First Advisor
MALISSA RANDOLPH
Academic Year
2021/2022
Abstract
Chordomas and chondrosarcomas are rare tumors that commonly experience loco-regional recurrence after primary treatment, making management of these tumors complex and difficult. Published case series reporting post-surgical outcomes for chordoma indicate that loco-regional recurrence affects more than 50% of patients treated with macroscopic complete resection with or without RT. Notably, a high proportion of recurrences occur late (after 5 and 10 years), requiring long-term follow-up. Chordomas and chondrosarcomas are considered to be radioresistant and require high doses of radiation greater than 60 Gy (Catanzano et al., 2019). There is a little data available regarding the most effective way to minimize dose to the spinal cord when treating chordoma and chondrosarcoma patients. Seven patients that were previously treated for chordoma or chondrosarcoma at our institution utilizing conventional VMAT optimization techniques were replanned using the jaw-blocking technique. Plans were generated in Raystation 11A for a 2.5mm leaf Truebeam linear accelerator. Comparisons were made using the relative reduction in the dose indices for the spinal cord. The dose metrics analyzed were the maximum dose, dose to 0.1cc, and mean dose. Additionally, comparisons were made for the GTV, CTV, and PTV coverage for each plan. The results showed statistically significant reduction is max, mean, and dose to 0.1cc of the spinal cord and overall better coverage to target volumes. Using the jaw-blocking VMAT technique is an effective treatment technique when the goal is to minimize as much dose to the spinal cord as possible.
ScholarWorks Citation
Wyatt, Amber, "SPINAL CORD DOSE REDUCTION USING A JAW-BLOCKING VMAT TECHNIQUE - A RETROSPECTIVE STUDY OF CHORDOMA AND CHONDROSARCOMA PATIENTS" (2022). Culminating Experience Projects. 207.
https://scholarworks.gvsu.edu/gradprojects/207