S1977
Physics - Dose prediction/calculation, optimisation and applications for photon and electron planning
ESTRO 2026
10.1016/j.radonc.2019.08.001 Available at: https://www.ncbi.nlm.nih.gov/pubmed/31526670. Keywords: Automated planning, Oesophageal Radiotherapy
For patients in the 60Gy/25# arm, automated plans showed a statistically significant reduction in heart (Dmean p<0.001; V30Gy p<0.001), lung (Dmean p<0.001; V20Gy p<0.001; V5Gy p=0.001) and liver (Dmean p=0.040; V30Gy p=0.004) doses compared to the originally-submitted plans.
Digital Poster 4073 Clinical validation of a Monte Carlo based secondary plan verification tool: optimizing an MLC parameter using an analytical approach Ayman El Ouati, Valeria Trojani, Andrea Botti, Francesco Braglia, Mauro Iori, Giulia Paolani, Roberto Sghedoni, Laura Verzellesi, Elisabetta Cagni Medical Physics Unit, Azienda USL-IRCCS di Reggio Emilia, Reggio Emilia, Italy Purpose/Objective: In RadCalc® (RC), one user-tunable parameter for Monte Carlo (RC-MC) secondary-check algorithm is the MLC Additional Radiation to Light Field Offset (ARLFO). No methods are suggested in RC guidelines to optimize this parameter. This study aimed at defining and clinically validating an analytical method to find the optimal ARLFO value, maximizing the agreement between MC calculations and phantom-based dose measurements. Material/Methods: The experimental tests were performed on a Varian- TrueBeam® linac equipped with a Millennium-120 MLC using 6X, 6FFF, 10X, and 10FFF beam energies. Seven IMRT fields with dynamic MLC apertures of 2, 4, 6, 10, 14, 16, and 20 mm were delivered into a water- tank phantom and measured with a Farmer ionization chamber, following the standard protocol recommended by Varian [1]. The plans were simulated in TPS (Eclipse v.13.7), imported into RC, and recalculated with MC varying the ARLFO, starting from its default value of 0 cm. For each field, the relationship between the percentage dose difference ( Δ D%) in the Farmer volume and ARLFO was determined; the optimal ARLFO value was extrapolated as that corresponding to 0% dose difference. A single optimal ARLFO per beam energy was obtained through weighted averages for each MLC apertures, using weights given by the product of the absolute value of the mean Δ D% (w1) and the median frequency of each MLC aperture in the clinical setting (w2); w2 was derived from 19-58 clinical RP- DICOM plans (training set) for each energy (Fig.1A). The method was tested on a validation set of 14-20 clinical plans per energy, comparing RC-MC calculations using both default and optimal ARLFOs against measurements performed on an Octavius-4D phantom with PTW 1500 matrix. Dose distributions were compared assessing the Gamma Passing Rate (GPR) with a 2%/2mm, local evaluation, 10% dose
Similarly, automated plans for patients in the 50Gy/25# arm showed statistically significant reductions in heart (Dmean p<0.001; V30Gy p<0.001), lung (Dmean p<0.001; V20Gy p<0.001; V5Gy p=0.001) and liver (Dmean p=0.040) doses compared to the originally-submitted plans. No statistically significant difference in liver V30Gy was observed for patients in the 50Gy arm.Reductions in the dose received by the Stomach_excl_PTV (V50Gy) were observed for automated plans in both the 60Gy/25# (p<0.001) and 50Gy/25# (p=0.019) arms. There were insufficient numbers of patients in either arm where disease extended inferiorly enough for kidneys to be coplanar with PTV_5000 to perform a valid comparison.For both arms, a significant increase in the maximum dose received by the SpinalCord_PRV was observed for automated planning (p<0.001 for both arms).Blinded clinical review found on average 9/10 automated plans were clinically acceptable without any suggestions for improvement, compared to 6/10 for the originally- submitted plans. Automated plans were preferred on 12 occasions, compared to 5 for the original plans. Conclusion: The PB-AIO method described in this study produces high-quality, clinically acceptable oesophageal radiotherapy plans which are often dosimetrically superior and more frequently preferred to those created using manual inverse-planning methods. References: Wheeler, P. A., et al. (2019a). ‘Utilisation of pareto navigation techniques to calibrate a fully automated radiotherapy treatment planning solution’ Phys
Imaging Radiat Oncol, 10 pp. 41-48. DOI: 10.1016/j.phro.2019.04.005 Available at:
https://www.ncbi.nlm.nih.gov/pubmed/33458267.Whe eler, P. A., et al. (2019b). ‘Evaluating the application of pareto navigation guided automated radiotherapy treatment planning to prostate cancer’ Radiother Oncol, 141 pp. 220-226. DOI:
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