Comparative study of the collapsed cone convolution and Monte Carlo algorithms for radiation therapy planning of canine sinonasal tumors reveals significant dosimetric differences.

Abstract

Computer-based radiation therapy requires high targeting and dosimetric precision. Analytical dosimetric algorithms typically are fast and clinically viable but can have increasing errors near air-bone interfaces. These are commonly found within dogs undergoing radiation planning for sinonasal cancer. This retrospective methods comparison study is designed to compare the dosimetry of both tumor volumes and organs at risk and quantify the differences between collapsed cone convolution (CCC) and Monte Carlo (MC) algorithms. Canine sinonasal tumor plans were optimized with CCC and then recalculated by MC with identical control points and monitor units. Planning target volume (PTV)air , PTVsoft tissue , and PTVbone were created to analyze the dose discrepancy within the PTV. Thirty imaging sets of dogs were included. Monte Carlo served as the gold standard calculation for the dosimetric comparison. Collapsed cone convolution overestimated the mean dose (Dmean ) to PTV and PTVsoft tissue by 0.9% and 0.5%, respectively (both P<0.001). Collapsed cone convolution overestimated Dmean to PTVbone by 3% (P<0.001). Collapsed cone convolution underestimated the near-maximum dose (D2 ) to PTVair by 1.1% (P<0.001), and underestimated conformity index and homogeneity index in PTV (both P<0.001). Mean doses of contralateral and ipsilateral eyes were overestimated by CCC by 1.6% and 1.7%, respectively (both P<0.001). Near-maximum doses of skin and brain were overestimated by CCC by 2.2% and 0.7%, respectively (both P<0.001). As clinical accessibility of Monte Carlo becomes more widespread, dose constraints may need to be re-evaluated with appropriate plan evaluation and follow-up.