Predicting liver SBRT eligibility and plan quality for VMAT and 4Π plans

Angelia Tran, Kaley Woods, Dan Nguyen, Victoria Y. Yu, Tianye Niu, Minsong Cao, Percy Lee, Ke Sheng

Research output: Contribution to journalArticle

18 Scopus citations

Abstract

Background: It is useful to predict planned dosimetry and determine the eligibility of a liver cancer patient for SBRT treatment using knowledge based planning (KBP). We compare the predictive accuracy using the overlap volume histogram (OVH) and statistical voxel dose learning (SVDL) KBP prediction models for coplanar VMAT to non-coplanar 4Π radiotherapy plans. Methods: In this study, 21 liver SBRT cases were selected, which were initially treated using coplanar VMAT plans. They were then re-planned using 4Π IMRT plans with 20 inversely optimized non-coplanar beams. OVH was calculated by expanding the planning target volume (PTV) and then plotting the percent overlap volume v with the liver vs. r v , the expansion distance. SVDL calculated the distance to the PTV for all liver voxels and bins the voxels of the same distance. Their dose information is approximated by either taking the median or using a skew-normal or non-parametric fit, which was then applied to voxels of unknown dose for each patient in a leave-one-out test. The liver volume receiving less than 15Gy (V<15Gy), DVHs, and 3D dose distributions were predicted and compared between the prediction models and planning methods. Results: On average, V<15Gy was predicted within 5%. SVDL was more accurate than OVH and able to predict DVH and 3D dose distributions. Median SVDL yielded predictive errors similar or lower than the fitting methods and is more computationally efficient. Prediction of the 4Π dose was more accurate compared to VMAT for all prediction methods, with significant (p<0.05) results except for OVH predicting liver V<15Gy (p=0.063). Conclusions: In addition to evaluating plan quality, KBP is useful to automatically determine the patient eligibility for liver SBRT and quantify the dosimetric gains from non-coplanar 4Π plans. The two here analyzed dose prediction methods performed more accurately for the 4Π plans than VMAT.

Original languageEnglish (US)
Article number70
JournalRadiation Oncology
Volume12
Issue number1
DOIs
StatePublished - Apr 24 2017

ASJC Scopus subject areas

  • Oncology
  • Radiology Nuclear Medicine and imaging

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    Tran, A., Woods, K., Nguyen, D., Yu, V. Y., Niu, T., Cao, M., Lee, P., & Sheng, K. (2017). Predicting liver SBRT eligibility and plan quality for VMAT and 4Π plans. Radiation Oncology, 12(1), [70]. https://doi.org/10.1186/s13014-017-0806-z