Development of a Monte Carlo based robustness calculation and evaluation tool. Issue 7 (4th May 2022)

• Record Type: Journal Article
• Authors: Loebner, Hannes A.; Volken, Werner; Mueller, Silvan; Bertholet, Jenny; Mackeprang, Paul‐Henry; Guyer, Gian; Aebersold, Daniel M.; Stampanoni, Marco F. M.; Manser, Peter; Fix, Michael K.
• Is Part Of: Medical physics. Volume 49:Issue 7(2022)
• Page Start: 4780
• Page End: 4793
• Publication Date: 2022-05-04
• Subjects: Monte Carlo -- plan evaluation -- robustness (to patient and machine‐related uncertainties); Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1002/mp.15683
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract: Background: Evaluating plan robustness is a key step in radiotherapy. Purpose: To develop a flexible Monte Carlo (MC)‐based robustness calculation and evaluation tool to assess and quantify dosimetric robustness of intensity‐modulated radiotherapy (IMRT) treatment plans by exploring the impact of systematic and random uncertainties resulting from patient setup, patient anatomy changes, and mechanical limitations of machine components. Methods: The robustness tool consists of two parts: the first part includes automated MC dose calculation of multiple user‐defined uncertainty scenarios to populate a robustness space. An uncertainty scenario is defined by a certain combination of uncertainties in patient setup, rigid intrafraction motion and in mechanical steering of the following machine components: angles of gantry, collimator, table‐yaw, table‐pitch, table‐roll, translational positions of jaws, multileaf‐collimator (MLC) banks, and single MLC leaves. The Swiss Monte Carlo Plan (SMCP) is integrated in this tool to serve as the backbone for the MC dose calculations incorporating the uncertainties. The calculated dose distributions serve as input for the second part of the tool, handling the quantitative evaluation of the dosimetric impact of the uncertainties. A graphical user interface (GUI) is developed to simultaneously evaluate the uncertainty scenarios according to user‐specified conditions based on dose‐volume histogram (DVH) parameters, fast and exact gamma analysis, and dose differences. Additionally, a robustness index (RI) is introduced with the aim to simultaneously evaluate and condense dosimetric robustness against multiple uncertainties into one number. The RI is defined as the ratio of scenarios passing the conditions on the dose distributions. Weighting of the scenarios in the robustness space is possible to consider their likelihood of occurrence. The robustness tool is applied on IMRT, a volumetric modulated arc therapy (VMAT), a dynamic trajectory radiotherapy (DTRT), and a dynamic mixed beam radiotherapy (DYMBER) plan for a brain case to evaluate the robustness to uncertainties of gantry‐, table‐, collimator angle, MLC, and intrafraction motion. Additionally, the robustness of the IMRT, VMAT, and DTRT plan against patient setup uncertainties are compared. The robustness tool is validated by Delta4 measurements for scenarios including all uncertainty types available. Results: The robustness tool performs simultaneous calculation of uncertainty scenarios, and the GUI enables their fast evaluation. For all evaluated plans and uncertainties, the planning target volume (PTV) margin prevented major clinical target volume (CTV) coverage deterioration (maximum observed standard deviation of D 98 % CTV $D98{\% _{{\rm{CTV}}}}$ was 1.3 Gy). OARs close to the PTV experienced larger dosimetric deviations (maximum observed standard deviation of D 2 % chiasma $D2{\% _{{\rm{chiasma}}}}$ was 14.5 Gy). Robustness comparison by RI evaluation against patient setup uncertainties revealed better dosimetric robustness of the VMAT and DTRT plans as compared to the IMRT plan. Delta4 validation measurements agreed with calculations by >96% gamma‐passing rate (3% global/2 mm). Conclusions: The robustness tool was successfully implemented. Calculation and evaluation of uncertainty scenarios with the robustness tool were demonstrated on a brain case. Effects of patient and machine‐specific uncertainties and the combination thereof on the dose distribution are evaluated in a user‐friendly GUI to quantitatively assess and compare treatment plans and their robustness.