Monte Carlo modeling of the Elekta Versa HD and patient dose calculation with EGSnrc/BEAMnrc.

INTRODUCTION: Numerous studies have proven the Monte Carlo method to be an accurate means of dose calculation. Although there are several commercial Monte Carlo treatment planning systems (TPSs), some clinics may not have access to these resources. We present a method for routine, independent patient dose calculations from treatment plans generated in a commercial TPS with our own Monte Carlo model using free, open-source software. MATERIALS AND METHODS: A model of the Elekta Versa HD linear accelerator was developed using the EGSnrc codes. A MATLAB script was created to take clinical patient plans and convert the DICOM RTP files into a format usable by EGSnrc. Ten patients' treatment plans were exported from the Monaco TPS to be recalculated using EGSnrc. Treatment simulations were done in BEAMnrc, and doses were calculated using Source 21 in DOSXYZnrc. Results were compared to patient plans calculated in the Monaco TPS and evaluated in Verisoft with a gamma criterion of 3%/2 mm. RESULTS: Our Monte Carlo model was validated within 1%/1-mm accuracy of measured percent depth doses and profiles. Gamma passing rates ranged from 82.1% to 99.8%, with 7 out of 10 plans having a gamma pass rate over 95%. Lung and prostate patients showed the best agreement with doses calculated in Monaco. All statistical uncertainties in DOSXYZnrc were less than 3.0%. CONCLUSION: A Monte Carlo model for routine patient dose calculation was successfully developed and tested. This model allows users to directly recalculate DICOM RP files containing patients' plans that have been exported from a commercial TPS.

Comparing conformal, arc radiotherapy and helical tomotherapy in craniospinal irradiation planning.

Currently, radiotherapy treatment plan acceptance is based primarily on dosimetric performance measures. However, use of radiobiological analysis to assess benefit in terms of tumor control and harm in terms of injury to normal tissues can be advantageous. For pediatric craniospinal axis irradiation (CSI) patients, in particular, knowing the technique that will optimize the probabilities of benefit versus injury can lead to better long-term outcomes. Twenty-four CSI pediatric patients (median age 10) were retrospectively planned with three techniques: three-dimensional conformal radiation therapy (3D CRT), volumetric-modulated arc therapy (VMAT), and helical tomotherapy (HT). VMAT plans consisted of one superior and one inferior full arc, and tomotherapy plans were created using a 5.02cm field width and helical pitch of 0.287. Each plan was normalized to 95% of target volume (whole brain and spinal cord) receiving prescription dose 23.4Gy in 13 fractions. Using an in-house MATLAB code and DVH data from each plan, the three techniques were evaluated based on biologically effective uniform dose (D=), the complication-free tumor control probability (P+), and the width of the therapeutically beneficial range. Overall, 3D CRT and VMAT plans had similar values of D= (24.1 and 24.2 Gy), while HT had a D= slightly lower (23.6 Gy). The average values of the P+ index were 64.6, 67.4, and 56.6% for 3D CRT, VMAT, and HT plans, respectively, with the VMAT plans having a statistically significant increase in P+. Optimal values of D= were 28.4, 33.0, and 31.9 Gy for 3D CRT, VMAT, and HT plans, respectively. Although P+ values that correspond to the initial dose prescription were lower for HT, after optimizing the D= prescription level, the optimal P+ became 94.1, 99.5, and 99.6% for 3D CRT, VMAT, and HT, respectively, with the VMAT and HT plans having statistically significant increases in P+. If the optimal dose level is prescribed using a radiobiological evaluation method, as opposed to a purely dosimetric one, the two IMRT techniques, VMAT and HT, will yield largest overall benefit to CSI patients by maximizing tumor control and limiting normal tissue injury. Using VMAT or HT may provide these pediatric patients with better long-term outcomes after radiotherapy.