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Backgroud and objectives: The implementation of patient-specific quality assurance (PSQA) has become a crucial aspect of the radiation therapy process. Machine learning models have demonstrated their potential as virtual QA tools, accurately predicting the gamma passing rate (GPR) of volumetric modulated arc therapy (VMAT)plans, thereby ensuring safe and efficient treatment for patients. However, there is limited multi-center research dedicated to predicting the GPR. In this study, a dosiomics-based machine learning approach was employed to construct a prediction model for classifying GPR in multiple radiotherapy institutions. Additionally, the model's performance was compared by evaluating the impact of two distinct feature selection methods. Methods: A retrospective data collection was conducted on 572 VMAT patients across three radiotherapy institutions. Utilizing a three-dimensional dose verification technique grounded in real-time measurements, y analysis was conducted according to the criteria of 3%/2 mm and 2%/2 mm, employing a dose threshold of 10% along with absolute dose and global normalization mode. Dosiomics features were extracted from the dose files, and distinct subsets of features were selected as inputs for the model using the random forest (RF) and RF combined with SHapley Additive exPlanations (SHAP) methods. The data underwent training using the extreme gradient boosting (XGBoost) algorithm, and the model's classification performance was assessed through F1-score and area under the curve (AUC) values. Results: The model exhibited optimal performance under the 3%/2 mm criteria, utilizing a subset of 20 features and attaining an AUC value of 0.88 and an F1score of 0.89. Similarly, under the 2%/2 mm criteria, the model demonstrated superior performance with a subset of 10 features, resulting in an AUC value of 0.91 and an F1-score of 0.89. The feature selection methods of RF and RF + SHAP have achieved good model performance by selecting as few features as possible. Conclusion: Based on the multi-center PSQA results, it is possible to utilize dosiomics features extracted from dose files to construct a machine learning predictive model. This model demonstrates excellent discriminative abilities, thus promoting the progress of gamma passing rate prognostic models in clinical application and implementation. Furthermore, it holds potential in providing patients with secure and efficient personalized QA management, while also reducing the workload of medical physicists. [ABSTRACT FROM AUTHOR]

Objective: A newly developed GafchromicTM EBT4 film model was released recently. According to the vendor‐stated specifications, this new model maintains major features of its more recent predecessor, EBT3, but has a great improvement in the signal‐to‐noise ratio (SNR). The purpose of this study was to fully characterize the EBT4 model by testing its dose response dependence on beam energy using clinical photon and electron beams and validating its improvement in SNR. Methods: In the beam energy dependence experiments, 6 MV and 15 MV photon beams, 6 MeV, 12 MeV and 20 MeV electron beams, 120 kVp and two 250 kVp x‐ray beams (with different half‐layer values) were selected, representing different clinical beam energies and radiation types. In the noise and SNR analyses, only the 6 MV photon beam was selected as the representative to compare between EBT4 and EBT3 models. Results: The energy dependence test results showed that there was a slight difference (<2% at most dose levels) in the dose response among the clinical MV photons and MeV electrons. However, a large deviation (>10%) in the dose response was found for the 120 kVp x‐ray beam. For the two 250 kVp x‐ray beams, the difference in the dose response was moderate (<5%), but not negligible. The noise and SNR results showed a reduced noise and strengthened SNR in EBT4. Conclusion: We conclude that the EBT4 film has a minimal energy dependence for MV photon and MeV electron beams but has a notable energy dependence for kilovoltage x‐ray beams, and the vendor‐stated features of the reduced noise and improved SNR in EBT4 are validated. [ABSTRACT FROM AUTHOR]

Objective: To determine the effect of dose calculation accuracy on inverse linear energy transfer (LET) optimization for intensity‐modulated proton therapy, and to determine whether adding more beams would improve the plan robustness to different dose calculation engines. Methods: Two sets of intensity‐modulated proton therapy plans using two, four, six, and nine beams were created for 10 prostate cancer patients: one set was optimized with dose constraints (DoseOpt) using the pencil beam (PB) algorithm, and the other set was optimized with additional LET constraints (LETOpt) using the Monte Carlo (MC) algorithm. Dose distributions of DoseOpt plans were then recalculated using the MC algorithm, and the LETOpt plans were recalculated using the PB algorithm. Dosimetric indices of targets and critical organs were compared between the PB and MC algorithms for both sets of plans. Results: For DoseOpt plans, dose differences between the PB and MC algorithms were minimal. However, the maximum dose differences in LETOpt plans were 11.11% and 15.85% in the dose covering 98% and 2% (D2) of the clinical target volume, respectively. Furthermore, the dose to 1 cc of the bladder differed by 11.42 Gy (relative biological effectiveness). Adding more beams reduced the discrepancy in target coverage, but the errors in D2 of the structure were increased with the number of beams. Conclusion: High modulation of LET requires high dose calculation accuracy during the optimization and final dose calculation in the inverse treatment planning for intensity‐modulated proton therapy, and adding more beams did not improve the plan robustness to different dose calculation algorithms. [ABSTRACT FROM AUTHOR]

Previously, a synchrotron-based horizontal proton beamline (87.2 MeV) was successfully commissioned to deliver radiation doses in FLASH and conventional dose rate modes to small fields and volumes. In this study, we developed a strategy to increase the effective radiation field size using a custom robotic motion platform to automatically shift the positions of biological samples. The beam was first broadened with a thin tungsten scatterer and shaped by customized brass collimators for irradiating cell/organoid cultures in 96-well plates (a 7-mm-diameter circle) or for irradiating mice (1-cm 2 square). Motion patterns of the robotic platform were written in G-code, with 9-mm spot spacing used for the 96-well plates and 10.6-mm spacing for the mice. The accuracy of target positioning was verified with a self-leveling laser system. The dose delivered in the experimental conditions was validated with EBT-XD film attached to the 96-well plate or the back of the mouse. Our film-measured dose profiles matched Monte Carlo calculations well (1D gamma pass rate >95%). The FLASH dose rates were 113.7 Gy/s for cell/organoid irradiation and 191.3 Gy/s for mouse irradiation. These promising results indicate that this robotic platform can be used to effectively increase the field size for preclinical experiments with proton FLASH., Competing Interests: Conflict of Interest Kiminori Iga is currently employed by Hitachi Ltd. Other authors declare no conflicts of interest.

EBT-XD model of Gafchromic ™ films has a broader optimal dynamic dose range, up to 40 Gy, compared to its predecessor models. This characteristic has made EBT-XD films suitable for high-dose applications such as stereotactic body radiotherapy and stereotactic radiosurgery, as well as ultra-high dose rate FLASH radiotherapy. The purpose of the current study was to characterize the dependence of EBT-XD film response on linear energy transfer (LET) and dose rate of therapeutic protons from a synchrotron. A clinical spot-scanning proton beam was used to study LET dependence at three dose-averaged LET (LET d ) values of 1.0 keV/µm, 3.6 keV/µm, and 7.6 keV/µm. A research proton beamline was used to study dose rate dependence at 150 Gy/second in the FLASH mode and 0.3 Gy/second in the non-FLASH mode. Film response data from LET d values of 0.9 keV/µm and 9.0 keV/µm of the proton FLASH beam were also compared. Film response data from a clinical 6 MV photon beam were used as a reference. Both gray value method and optical density (OD) method were used in film calibration. Calibration results using a specific OD calculation method and a generic OD calculation method were compared. The four-parameter NIH Rodbard function and three-parameter rational function were compared in fitting the calibration curves. Experimental results showed that the response of EBT-XD film is proton LET dependent but independent of dose rate. Goodness-of-fit analysis showed that using the NIH Rodbard function is superior for both protons and photons. Using the "specific OD + NIH Rodbard function" method for EBT-XD film calibration is recommended., Competing Interests: Conflicts of Interest Kiminori Iga is currently employed by Hitachi Ltd. Other authors declare no conflicts of interest.