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Purpose: Patient setup errors have been a primary concern impacting the dose delivery accuracy in radiation therapy. A robust treatment plan might mitigate the effects of patient setup errors. In this reported study, we aimed to evaluate the impact of translational and rotational errors on the robustness of linac‐based, single‐isocenter, coplanar, and non‐coplanar volumetric modulated arc therapy treatment plans for multiple brain metastases. Methods: Fifteen patients were retrospectively selected for this study with a combined total of 49 gross tumor volumes (GTVs). Single‐isocenter coplanar and non‐coplanar plans were generated first with a prescribed dose of 40 Gy in 5 fractions or 42 Gy in 7 fractions to cover 95% of planning target volume (PTV). Next, four setup errors (+1 and +2 mm translation, and +1° and +2° rotation) were applied individually to generate modified plans. Different plan quality evaluation metrics were compared between coplanar and non‐coplanar plans. 3D gamma analysis (3%/2 mm) was performed to compare the modified plans (+2 mm and +2° only) and the original plans. Paired t‐test was conducted for statistical analysis. Results: After applying setup errors, variations of all plan evaluation metrics were similar (p > 0.05). The worst case for V100% to GTV was 92.07% ± 6.13% in the case of +2 mm translational error. 3D gamma pass rates were > 90% for both coplanar (+2 mm and +2°) and the +2 mm non‐coplanar groups but was 87.40% ± 6.89% for the +2° non‐coplanar group. Conclusion: Translational errors have a greater impact on PTV and GTV dose coverage for both planning methods. Rotational errors have a greater negative impact on gamma pass rates of non‐coplanar plans. Plan evaluation metrics after applying setup errors showed that both coplanar and non‐coplanar plans were robust and clinically acceptable. [ABSTRACT FROM AUTHOR]

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]

This study introduces the MKM_B model, an approach derived from the MKM model, designed to evaluate the biological effectiveness of Boron Neutron Capture Therapy (BNCT) in the face of challenges from varying microscopic boron distributions. The model introduces a boron compensation factor, allowing for the assessment of compound Biological Effectiveness (CBE) values for different boron distributions. Utilizing the TOPAS simulation platform, the lineal energy spectrum of particles in BNCT was simulated, and the sensitivity of the MKM_B model to parameter variations and the influence of cell size on the model were thoroughly investigated. The CBE values for 10B-boronphenylalanine (BPA) and 10B-sodium (BSH) were determined to be 3.70 and 1.75, respectively. These calculations were based on using the nucleus radius of 2.5 μm and the cell radius of 5 μm while considering a 50% surviving fraction. It was observed that as cell size decreased, the CBE values for both BPA and BSH increased. Additionally, the model parameter rd was identified as having the most significant impact on CBE, with other parameters showing moderate effects. The development of the MKM_B model enables the accurate prediction of CBE under different boron distributions in BNCT. This model offers a promising approach to optimize treatment planning by providing increased accuracy in biological effectiveness., (© The Author(s) 2024. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)