Proton therapy is a highly advanced form of radiation therapy that allows for precise tumour targeting while minimizing damage to surrounding healthy tissues. Its effectiveness is largely due to the physical properties of protons, particularly their Bragg peak, which enables concentrated energy deposition at specific depths. This precision is crucial, especially in Pencil Beam Scanning (PBS) techniques, but it also demands rigorous verification to ensure accurate treatment delivery. Patient-Specific Quality Assurance (PSQA) is, consequently, essential in proton therapy to ensure that the patient’s dose distribution is effectively delivered as it was planned on the Treatment Planning System (TPS). To meet this goal, an ideal PSQA method should check: • the correct data transfer from TPS to the Oncology Information System (OIS); • the deliverability of the plan (for instance checking that no collision occurs between the moving parts or ensuring machine’s delivery limits have been respected during planning); • the reliability of the TPS dose calculation algorithm. Traditional PSQA methods involve experimental techniques, where dose distributions are measured directly using physical tissue-equivalent phantoms. While effective, these methods can be laborintensive and may not fully address the aim of PSQA. Recently, virtual PSQA tools, which use independent dose calculations based on Monte Carlo (MC) algorithms, have emerged as more precise and efficient alternatives. These tools calculate dose distributions and allow for the in-silico verification of treatment plans giving the opportunity of reducing physical measurements, offering an attractive solution for modern proton therapy centers. The objective of this study is to clinically validate a commercial independent dose calculation software for PSQA purpose (myQA iON, IBA-Dosimetry) and to evaluate its potential to streamline and enhance the PSQA process at the European Institute of Oncology (IEO). Specifically, we aimed to assess the accuracy of this software in verifying proton therapy treatment plans and to determine whether it could effectively complement or replace traditional PSQA methods. The study was conducted in two phases: beam model validation and clinical validation. Beam model validation involved testing myQA iON against two types of phantoms: a water phantom and a customized heterogeneous phantom simulating a tissue-lung interface. For the water phantom, we designed 26 Verification Plans with varying beam configurations to test the accuracy of key dose parameters such as distal range and transverse dose profiles. Comparisons for these parameters were made between the dose distributions calculated by myQA iON, the TPS (RayStation, version 12A SP1) and physical measurements taken with devices like the Zebra detector and the MatriXX ONE (IBADosimetry) ionization chamber array. Additionally, comparisons between computed dose in myQA iON and RayStation were made using a 3D Gamma Passing Rate (GPR) with a global gamma index, applying 2% dose difference and a 2 mm dose-to-agreement (DTA) parameter. A Similar analysis was conducted with the heterogeneous phantom to evaluate myQA iON's ability to handle complex dose computations across heterogeneous materials, simulating the tissue-lung boundary commonly encountered in certain treatment sites. In the clinical validation phase, 89 proton therapy plans for various anatomical sites (brain, thorax, pelvis, breast, and head and neck) were analyzed. Our current PSQA method compares a planned and a measured planar dose distributions for each beam using a 2D GPR (global gamma index, 3% dose difference, and 3 mm DTA). For this study, we recalculated the beams of selected clinical plans in myQA iON and compared the resulting dose distributions with the planned ones using a 3D GPR (with the same parameters used for the beam model validation). We then compared the resulting 3D GPR values with the 2D GPR distributions from routine PSQA, analyzing when higher 3D GPR values are obtained compared to 2D GPR, or vice versa, offering valuable insights for selecting an appropriate GPR threshold for passing or failing PSQA assessments using myQA iON. The results showed that myQA iON demonstrated strong performance in both beam model and clinical validations. In the water phantom tests, the tool achieved high 3D GPRs with mean values of 98.4% for plans in which a pre-absorber was used and 99.5% for those without any passive element on the beam line. Although there was a slight overestimation of distal range values in myQA iON compared to RayStation, these differences were consistent with the uncertainties of this study. Transverse dose profiles also showed good agreement, with differences in Full Width at Half Maximum (FWHM) of less than 2 mm and relative dose deviations below ±2%. The validation using the heterogeneous phantom highlighted some challenges. Lower GPRs were observed when the beam passed through the water equivalent - cork interface (the average GPR is 97.0%, while corresponding value for computation without water equivalent - cork interface is 99.5%). Thisindicates that the accuracy of myQA iON may be lower when the dose is calculated in regions with high heterogeneities Similarly, while most clinical plans demonstrated good agreement, especially for brain, head and neck, and thorax sites, lower 3D GPRs were observed in breast treatment plans, likely due to the beam encountering a tissue-lung interface after passing through the target (the median 3D GPR for breast plans is 95.5%, while for other treatment site the 3D GPR medians are greater or equal to 98.7%). In the discussion, space for improvement in myQA iON were highlighted. These include refining MC parameters for better performance across heterogeneous interfaces, testing the CT calibration curve, analysing off-axis dose distributions, expanding clinical validation to include more treatment sites, and incorporating machine delivery log-files into dose calculations could allow for real-time verification of treatment delivery. In conclusion, myQA iON shows good potential as a PSQA tool for proton therapy, offering accurate and efficient dose verification across a wide range of treatment plans. However, further studies are needed to address its limitations in handling complex anatomical regions and to ensure its safe integration into clinical practice.

Clinical validation of a Monte Carlo based commercial platform for patient-specific quality assurance in Proton Therapy / G. CASTIGLIONE MINISCHETTI. - (2024 Nov 14).

Clinical validation of a Monte Carlo based commercial platform for patient-specific quality assurance in Proton Therapy

G. CASTIGLIONE MINISCHETTI
2024

Abstract

Proton therapy is a highly advanced form of radiation therapy that allows for precise tumour targeting while minimizing damage to surrounding healthy tissues. Its effectiveness is largely due to the physical properties of protons, particularly their Bragg peak, which enables concentrated energy deposition at specific depths. This precision is crucial, especially in Pencil Beam Scanning (PBS) techniques, but it also demands rigorous verification to ensure accurate treatment delivery. Patient-Specific Quality Assurance (PSQA) is, consequently, essential in proton therapy to ensure that the patient’s dose distribution is effectively delivered as it was planned on the Treatment Planning System (TPS). To meet this goal, an ideal PSQA method should check: • the correct data transfer from TPS to the Oncology Information System (OIS); • the deliverability of the plan (for instance checking that no collision occurs between the moving parts or ensuring machine’s delivery limits have been respected during planning); • the reliability of the TPS dose calculation algorithm. Traditional PSQA methods involve experimental techniques, where dose distributions are measured directly using physical tissue-equivalent phantoms. While effective, these methods can be laborintensive and may not fully address the aim of PSQA. Recently, virtual PSQA tools, which use independent dose calculations based on Monte Carlo (MC) algorithms, have emerged as more precise and efficient alternatives. These tools calculate dose distributions and allow for the in-silico verification of treatment plans giving the opportunity of reducing physical measurements, offering an attractive solution for modern proton therapy centers. The objective of this study is to clinically validate a commercial independent dose calculation software for PSQA purpose (myQA iON, IBA-Dosimetry) and to evaluate its potential to streamline and enhance the PSQA process at the European Institute of Oncology (IEO). Specifically, we aimed to assess the accuracy of this software in verifying proton therapy treatment plans and to determine whether it could effectively complement or replace traditional PSQA methods. The study was conducted in two phases: beam model validation and clinical validation. Beam model validation involved testing myQA iON against two types of phantoms: a water phantom and a customized heterogeneous phantom simulating a tissue-lung interface. For the water phantom, we designed 26 Verification Plans with varying beam configurations to test the accuracy of key dose parameters such as distal range and transverse dose profiles. Comparisons for these parameters were made between the dose distributions calculated by myQA iON, the TPS (RayStation, version 12A SP1) and physical measurements taken with devices like the Zebra detector and the MatriXX ONE (IBADosimetry) ionization chamber array. Additionally, comparisons between computed dose in myQA iON and RayStation were made using a 3D Gamma Passing Rate (GPR) with a global gamma index, applying 2% dose difference and a 2 mm dose-to-agreement (DTA) parameter. A Similar analysis was conducted with the heterogeneous phantom to evaluate myQA iON's ability to handle complex dose computations across heterogeneous materials, simulating the tissue-lung boundary commonly encountered in certain treatment sites. In the clinical validation phase, 89 proton therapy plans for various anatomical sites (brain, thorax, pelvis, breast, and head and neck) were analyzed. Our current PSQA method compares a planned and a measured planar dose distributions for each beam using a 2D GPR (global gamma index, 3% dose difference, and 3 mm DTA). For this study, we recalculated the beams of selected clinical plans in myQA iON and compared the resulting dose distributions with the planned ones using a 3D GPR (with the same parameters used for the beam model validation). We then compared the resulting 3D GPR values with the 2D GPR distributions from routine PSQA, analyzing when higher 3D GPR values are obtained compared to 2D GPR, or vice versa, offering valuable insights for selecting an appropriate GPR threshold for passing or failing PSQA assessments using myQA iON. The results showed that myQA iON demonstrated strong performance in both beam model and clinical validations. In the water phantom tests, the tool achieved high 3D GPRs with mean values of 98.4% for plans in which a pre-absorber was used and 99.5% for those without any passive element on the beam line. Although there was a slight overestimation of distal range values in myQA iON compared to RayStation, these differences were consistent with the uncertainties of this study. Transverse dose profiles also showed good agreement, with differences in Full Width at Half Maximum (FWHM) of less than 2 mm and relative dose deviations below ±2%. The validation using the heterogeneous phantom highlighted some challenges. Lower GPRs were observed when the beam passed through the water equivalent - cork interface (the average GPR is 97.0%, while corresponding value for computation without water equivalent - cork interface is 99.5%). Thisindicates that the accuracy of myQA iON may be lower when the dose is calculated in regions with high heterogeneities Similarly, while most clinical plans demonstrated good agreement, especially for brain, head and neck, and thorax sites, lower 3D GPRs were observed in breast treatment plans, likely due to the beam encountering a tissue-lung interface after passing through the target (the median 3D GPR for breast plans is 95.5%, while for other treatment site the 3D GPR medians are greater or equal to 98.7%). In the discussion, space for improvement in myQA iON were highlighted. These include refining MC parameters for better performance across heterogeneous interfaces, testing the CT calibration curve, analysing off-axis dose distributions, expanding clinical validation to include more treatment sites, and incorporating machine delivery log-files into dose calculations could allow for real-time verification of treatment delivery. In conclusion, myQA iON shows good potential as a PSQA tool for proton therapy, offering accurate and efficient dose verification across a wide range of treatment plans. However, further studies are needed to address its limitations in handling complex anatomical regions and to ensure its safe integration into clinical practice.
LENARDI, CRISTINA
14-nov-2024
proton therapy, PSQA, monte carlo, independent dose calculation, clinical implementation, pencil beam scanning, MCsquare, myQA ion
Settore MEDS-22/A - Diagnostica per immagini e radioterapia
Tesi di specializzazione
Clinical validation of a Monte Carlo based commercial platform for patient-specific quality assurance in Proton Therapy / G. CASTIGLIONE MINISCHETTI. - (2024 Nov 14).
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/1117249
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