Sheng Huang1,2, Kevin Souris3,4, Siyang Li1, Minglei Kang1, Ana Maria Barragan Montero3,4, Guillaume Janssens5, Alexander Lin1, Elizabeth Garver1, Christopher Ainsley1, Paige Taylor6, Ying Xiao1, Liyong Lin1,7. 1. Department of Radiation Oncology, University of Pennsylvania, 3400 Civic Central Blvd, Philadelphia, PA, 19104, USA. 2. Department of Medical Physics, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY, 10065, USA. 3. Center for Molecular Imaging and Experimental Radiotherapy, Institut de Recherche Expérimentale et Clinique, Université catholique de Louvain, Avenue Hippocrate 54, Brussels, 1200, Belgium. 4. ICTEAM Institute, Université catholique de Louvain, Louvain-la-Neuve, 1348, Belgium. 5. Advanced Technology Group, Ion Beam Applications SA, Louvain-la-Neuve, Belgium. 6. The Imaging and Radiation Oncology Core Houston Quality Assurance Center,, The University of Texas MD Anderson Cancer Center, 8060 El Rio St, Houston, TX, 77054, USA. 7. Department of Radiation Oncology, Winship Cancer Institute at Emory University, 1365 Clifton Rd. Atlanta, GA, 30322, USA.
Abstract
PURPOSE: Monte Carlo (MC) dose calculation is generally superior to analytical dose calculation (ADC) used in commercial TPS to model the dose distribution especially for heterogeneous sites, such as lung and head/neck patients. The purpose of this study was to provide a validated, fast, and open-source MC code, MCsquare, to assess the impact of approximations in ADC on clinical pencil beam scanning (PBS) plans covering various sites. METHODS: First, MCsquare was validated using tissue-mimicking IROC lung phantom measurements as well as benchmarked with the general purpose Monte Carlo TOPAS for patient dose calculation. Then a comparative analysis between MCsquare and ADC was performed for a total of 50 patients with 10 patients per site (including liver, pelvis, brain, head-and-neck, and lung). Differences among TOPAS, MCsquare, and ADC were evaluated using four dosimetric indices based on the dose-volume histogram (target Dmean, D95, homogeneity index, V95), a 3D gamma index analysis (using 3%/3 mm criteria), and estimations of tumor control probability (TCP). RESULTS: Comparison between MCsquare and TOPAS showed less than 1.8% difference for all of the dosimetric indices/TCP values and resulted in a 3D gamma index passing rate for voxels within the target in excess of 99%. When comparing ADC and MCsquare, the variances of all the indices were found to increase as the degree of tissue heterogeneity increased. In the case of lung, the D95s for ADC were found to differ by as much as 6.5% from the corresponding MCsquare statistic. The median gamma index passing rate for voxels within the target volume decreased from 99.3% for liver to 75.8% for lung. Resulting TCP differences can be large for lung (≤10.5%) and head-and-neck (≤6.2%), while smaller for brain, pelvis and liver (≤1.5%). CONCLUSIONS: Given the differences found in the analysis, accurate dose calculation algorithms such as Monte Carlo simulations are needed for proton therapy, especially for disease sites with high heterogeneity, such as head-and-neck and lung. The establishment of MCsquare can facilitate patient plan reviews at any institution and can potentially provide unbiased comparison in clinical trials given its accuracy, speed and open-source availability.
PURPOSE: Monte Carlo (MC) dose calculation is generally superior to analytical dose calculation (ADC) used in commercial TPS to model the dose distribution especially for heterogeneous sites, such as lung and head/neck patients. The purpose of this study was to provide a validated, fast, and open-source MC code, MCsquare, to assess the impact of approximations in ADC on clinical pencil beam scanning (PBS) plans covering various sites. METHODS: First, MCsquare was validated using tissue-mimicking IROC lung phantom measurements as well as benchmarked with the general purpose Monte Carlo TOPAS for patient dose calculation. Then a comparative analysis between MCsquare and ADC was performed for a total of 50 patients with 10 patients per site (including liver, pelvis, brain, head-and-neck, and lung). Differences among TOPAS, MCsquare, and ADC were evaluated using four dosimetric indices based on the dose-volume histogram (target Dmean, D95, homogeneity index, V95), a 3D gamma index analysis (using 3%/3 mm criteria), and estimations of tumor control probability (TCP). RESULTS: Comparison between MCsquare and TOPAS showed less than 1.8% difference for all of the dosimetric indices/TCP values and resulted in a 3D gamma index passing rate for voxels within the target in excess of 99%. When comparing ADC and MCsquare, the variances of all the indices were found to increase as the degree of tissue heterogeneity increased. In the case of lung, the D95s for ADC were found to differ by as much as 6.5% from the corresponding MCsquare statistic. The median gamma index passing rate for voxels within the target volume decreased from 99.3% for liver to 75.8% for lung. Resulting TCP differences can be large for lung (≤10.5%) and head-and-neck (≤6.2%), while smaller for brain, pelvis and liver (≤1.5%). CONCLUSIONS: Given the differences found in the analysis, accurate dose calculation algorithms such as Monte Carlo simulations are needed for proton therapy, especially for disease sites with high heterogeneity, such as head-and-neck and lung. The establishment of MCsquare can facilitate patient plan reviews at any institution and can potentially provide unbiased comparison in clinical trials given its accuracy, speed and open-source availability.
Authors: Bruce Faddegon; José Ramos-Méndez; Jan Schuemann; Aimee McNamara; Jungwook Shin; Joseph Perl; Harald Paganetti Journal: Phys Med Date: 2020-04-03 Impact factor: 2.685
Authors: Samaneh Kazemifar; Ana M Barragán Montero; Kevin Souris; Sara T Rivas; Robert Timmerman; Yang K Park; Steve Jiang; Xavier Geets; Edmond Sterpin; Amir Owrangi Journal: J Appl Clin Med Phys Date: 2020-03-26 Impact factor: 2.102
Authors: Liyong Lin; Paige A Taylor; Jiajian Shen; Jatinder Saini; Minglei Kang; Charles B Simone; Jeffrey D Bradley; Zuofeng Li; Ying Xiao Journal: Int J Part Ther Date: 2021-05-25
Authors: Andries N Schreuder; Daniel S Bridges; Lauren Rigsby; Marc Blakey; Martin Janson; Samantha G Hedrick; John B Wilkinson Journal: J Appl Clin Med Phys Date: 2019-09-21 Impact factor: 2.102