SU-F-T-152: Experimental Validation and Calculation Benchmark for a Commercial Monte Carlo Pencil BeamScanning Proton Therapy Treatment Planning System in Heterogeneous Media

2016 ◽  
Vol 43 (6Part15) ◽  
pp. 3496-3497
Author(s):  
L Lin ◽  
S Huang ◽  
M Kang ◽  
C Ainsley ◽  
C Simone ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Treatment Planning ◽  
Proton Therapy ◽  
Experimental Validation ◽  
Heterogeneous Media ◽  
Planning System ◽  
Treatment Planning System ◽  
Therapy Treatment

Proton therapy dose distribution comparison between Monte Carlo and a treatment planning system for pediatric patients with ependymomaa)

2012 ◽  
Vol 39 (8) ◽  
pp. 4742-4747 ◽  
Author(s):  
Yingcui Jia ◽  
Chris Beltran ◽  
Daniel J. Indelicato ◽  
Stella Flampouri ◽  
Zuofeng Li ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Treatment Planning ◽  
Proton Therapy ◽  
Dose Distribution ◽  
Pediatric Patients ◽  
Planning System ◽  
Treatment Planning System

Clinical validation of a GPU-based Monte Carlo dose engine of a commercial treatment planning system for pencil beam scanning proton therapy

2021 ◽  
Vol 88 ◽  
pp. 226-234
Author(s):  
Francesco Fracchiolla ◽  
Erik Engwall ◽  
Martin Janson ◽  
Fredrik Tamm ◽  
Stefano Lorentini ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Treatment Planning ◽  
Proton Therapy ◽  
Planning System ◽  
Treatment Planning System ◽  
Clinical Validation ◽  
Pencil Beam ◽  
Beam Scanning ◽  
Pencil Beam Scanning

scholarly journals Comparative photon and proton dosimetry for patients with mediastinal lymphoma in the era of Monte Carlo treatment planning and variable relative biological effectiveness

2019 ◽  
Vol 14 (1) ◽  
Author(s):  
Yolanda D. Tseng ◽  
Shadonna M. Maes ◽  
Gregory Kicska ◽  
Patricia Sponsellor ◽  
Erik Traneus ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Treatment Planning ◽  
Proton Therapy ◽  
Breast Tissue ◽  
Relative Biological Effectiveness ◽  
Planning System ◽  
Treatment Planning System ◽  
Target Coverage ◽  
Mediastinal Lymphoma ◽  
Biological Effectiveness

Abstract Background Existing pencil beam analytical (PBA) algorithms for proton therapy treatment planning are not ideal for sites with heterogeneous tissue density and do not account for the spatial variations in proton relative biological effectiveness (vRBE). Using a commercially available Monte Carlo (MC) treatment planning system, we compared various dosimetric endpoints between proton PBA, proton MC, and photon treatment plans among patients with mediastinal lymphoma. Methods Eight mediastinal lymphoma patients with both free breathing (FB) and deep inspiration breath hold (DIBH) CT simulation scans were analyzed. The original PBA plans were re-calculated with MC. New proton plans that used MC for both optimization and dose calculation with equivalent CTV/ITV coverage were also created. A vRBE model, which uses a published model for DNA double strand break (DSB) induction, was applied on MC plans to study the potential impact of vRBE on cardiac doses. Comparative photon plans were generated on the DIBH scan. Results Re-calculation of FB PBA plans with MC demonstrated significant under coverage of the ITV V99 and V95. Target coverage was recovered by re-optimizing the PT plan with MC with minimal change to OAR doses. Compared to photons with DIBH, MC-optimized FB and DIBH proton plans had significantly lower dose to the mean lung, lung V5, breast tissue, and spinal cord for similar target coverage. Even with application of vRBE in the proton plans, the putative increase in RBE at the end of range did not decrease the dosimetric advantages of proton therapy in cardiac substructures. Conclusions MC should be used for PT treatment planning of mediastinal lymphoma to ensure adequate coverage of target volumes. Our preliminary data suggests that MC-optimized PT plans have better sparing of the lung and breast tissue compared to photons. Also, the potential for end of range RBE effects are unlikely to be large enough to offset the dosimetric advantages of proton therapy in cardiac substructures for mediastinal targets, although these dosimetric findings require validation with late toxicity data.

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