Abstract
BackgroundIt 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.MethodsIn 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. rv, 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 15 Gy (V<15Gy), DVHs, and 3D dose distributions were predicted and compared between the prediction models and planning methods.ResultsOn 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).ConclusionsIn 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.
Highlights
Stereotactic Body Radiation Therapy (SBRT) has emerged as a promising treatment modality for hepatocellular carcinoma (HCC), liver oligometastatic disease and other liver tumors when patients are contraindicated for surgical resection [1, 2]
A single point from the overlap volume histogram (OVH): rv, the expansion distance to overlap fractional volume v, was used to quantify the relationship between the OVH and the liver volume receiving less than 15 Gy, denoted by V
OVH Figure 1a shows the liver OVH of all 21 patients for 4π and volumetric modulated arc therapy (VMAT) plans and Fig. 1b shows the correlation between r10 and the liver volume receiving less than 15 Gy. r10 showed the best correlation, with correlation coefficients of 0.897 and 0.815 for 4π and VMAT liver V
Summary
Stereotactic Body Radiation Therapy (SBRT) has emerged as a promising treatment modality for hepatocellular carcinoma (HCC), liver oligometastatic disease and other liver tumors when patients are contraindicated for surgical resection [1, 2]. In an earlier study to provide guidelines to treatment planning, a predictive model was developed to calculate the maximum tolerable dose for Helical TomoTherapy-based. A trial and error planning process is adopted to determine the level of normal organ doses and patient eligibility but the result can be operator and planning method dependent. A recently developed paradigm referred to as knowledge based planning (KBP) may be helpful to improve the process. 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
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