Using intensity-modulated radiosurgery (IMRS) with single isocenter for the treatment of multiple brain lesions has gained acceptance in recent years. One of the challenges of this technique is conducting a patient-specific quality assurance (QA), involving accurate gamma passing rate (GPR) calculations for small and wide spread-out targets. We evaluated effects of parameters such as dose grid and energy on GPR using our clinical IMRS plans. Methods: Ten patients with total of 40 volumetric modulated arc therapy (VMAT) plans were created in Raystation (V.8A) treatment planning system (TPS) for the Varian Edge Linac using 6 and 10 flattening filter-free (FFF) beams and planned dose grids of 1 mm and 2 mm resulting in four plans with 6-10 targets per patient. All parameters and objectives except dose grid and energy were kept the same in all plans. Next, patient-specific QAs were measured evaluating GPR with 10% threshold, 3%/3 mm objective, and an acceptance criterion of 95%. Modulation factors (MF) and confidence intervals were calculated. Two modes of measurements, standard density (SD) and high density (HD), were used. Results: Generally, plans computed with 1 mm dose grid have higher GPRs than those with 2 mm dose grid for both energies used. The GPRs of 6 FFF plans were higher than those of 10 FFF plans. GPR showed no noticeable difference between HD and SD measurements. Negative correlation between MF and GPR was observed. The HD pass rates fall within the confidence interval of SD. Conclusion: Calculated dose grid should be less than or equal to one-third of distance to agreement, thus 1 mm planned dose grid is recommended to reduce artifacts in gamma calculation. GPR of SD and HD measurement modes is almost the same, which indicates that SD mode is clinically preferable for performing patient-specific QAs. According to our results, using 6 FFF beams with 1 mm planned dose grid is more accurate and reliable for dose calculation of IMRS plans.

Purpose This study aims to develop an algorithm to predict gamma passing rate (GPR) in the volumetric‐modulated arc therapy (VMAT) technique. Materials and methods A total of 118 clinical VMAT plans, including 28 mediastina, 25 head and neck, 40 brains intensity‐modulated radiosurgery, and 25 prostate cases, were created in RayStation treatment planning system for Edge and TrueBeam linacs. In‐house scripts were developed to compute Modulation indices such as plan‐averaged beam area (PA), plan‐averaged beam irregularity (PI), total monitor unit (MU), leaf travel/arc length, mean dose rate variation, and mean gantry speed variation. Pretreatment verifications were performed on ArcCHECK phantom with SNC software. GPR was calculated with 3%/2 mm and 10% threshold. The dataset was randomly split into a training (70%) and a test (30%) dataset. A random forest regression (RFR) model and support vector regression (SVR) with linear kernel were trained to predict GPR using the complexity metrics as input. The prediction performance was evaluated by calculating the mean absolute error (MAE), R 2 , and root mean square error (RMSE). Results RMSEs at γ 3%/2 mm for RFR and SVR were 1.407 ± 0.103 and 1.447 ± 0.121, respectively. MAE was 1.14 ± 0.084 for RFR and 1.101 ± 0.09 for SVR. R 2 was equal to 0.703 ± 0.027 and 0.689 ± 0.053 for RFR and SVR, respectively. GPR of 3%/2 mm with a 10% threshold can be predicted with an error smaller than 3% for 94% of plans using RFR and SVR models. The most important metrics that had the greatest impact on how accurately GPR can be predicted were determined to be the PA, PI, and total MU. Conclusion In terms of its prediction values and errors, SVR (linear) appeared to be comparable with RFR for this dataset. Based on our results, the PA, PI, and total MU calculations may be useful in guiding VMAT plan evaluation and ultimately reducing uncertainties in planning and radiation delivery.

Fungal infections caused by Candida albicans are a serious problem for immunocompromised individuals, including those undergoing radiotherapy for head and neck cancers. Targeted irradiation causes inflammatory dysregulation and damage to the oral mucosa that can be exacerbated by candidiasis. Post-irradiation the cytokine interleukin-17 (IL-17) protects the oral mucosae by promoting oral epithelial regeneration and balancing the oral immune cell populations, which leads to the eventual healing of the tissue. IL-17 signaling is also critical for the antifungal response during oropharyngeal candidiasis (OPC). Yet, the benefit of IL-17 during other forms of candidiasis, such as vulvovaginal candidiasis, is not straightforward. Therefore, it was important to determine the role of IL-17 during OPC associated with radiation-induced inflammatory damage. To answer this question, we exposed Il17ra−/− and wild-type mice to head-neck irradiation (HNI) and OPC to determine if the IL-17 signaling pathway was still protective against C. albicans. HNI increased susceptibility to OPC, and in Il17ra−/− mice, the mucosal damage and fungal burden were elevated compared to control mice. Intriguingly, neutrophil influx was increased in Il17ra−/− mice, yet these cells had reduced capacity to phagocytose C. albicans and failed to clear OPC compared to immunocompetent mice. These findings suggest that radiotherapy not only causes physical damage to the oral cavity but also skews immune mediators, leading to increased susceptibility to oropharyngeal candidiasis.

Objectives: Distinguishing between radiation necrosis (RN) and metastatic progression is extremely challenging due to their similarity in conventional imaging. This is crucial from a therapeutic point of view as this determines the outcome of the treatment. This study aims to establish an automated technique to differentiate RN from brain metastasis progression using radiomics with machine learning. Methods: Eighty-six patients with brain metastasis after they underwent stereotactic radiosurgery as primary treatment were selected. Discrete wavelets transform, Laplacian-of-Gaussian, Gradient, and Square were applied to magnetic resonance post-contrast T1-weighted images to extract radiomics features. After feature selection, dataset was randomly split into train/test (80%/20%) datasets. Random forest classification, logistic regression, and support vector classification were trained and subsequently validated using test set. The classification performance was measured by area under the curve (AUC) value of receiver operating characteristic curve, accuracy, sensitivity, and specificity. Results: The best performance was achieved using random forest classification with a Gradient filter (AUC=0.910±0.047, accuracy 0.8±0.071, sensitivity=0.796±0.055, specificity=0.922±0.059). For, support vector classification the best result obtains using wavelet_HHH with a high AUC of 0.890±0.89, accuracy of 0.777±0.062, sensitivity=0.701±0.084, and specificity=0.85±0.112. Logistic regression using wavelet_HHH provides a poor result with AUC=0.882±0.051, accuracy of 0.753±0.08, sensitivity=0.717±0.208, and specificity=0.816±0.123. Conclusion: This type of machine-learning approach can help accurately distinguish RN from recurrence in magnetic resonance imaging, without the need for biopsy. This has the potential to improve the therapeutic outcome.

To assure the accuracy and safety of radiation delivery, it is highly recommended to perform pretreatment verification for complex treatment methods such as intensity-modulated radiation therapy (IMRT) or volumetric-modulated arc therapy (VMAT) to detect any potential errors in the treatment planning process and machine deliverability. It is expected that a qualified medical physicist is aware of the underlying scientific principles of imaging and therapeutic processes to perform or supervise technical aspects of pretreatment procedures to ensure safe and effective delivery of the treatment. For this purpose, several guidelines have been published to help direct medical physicists to evaluate the accuracy of treatment planning system (TPS) in the calculation of radiation dose, and dosimetry equipment to avoid possible errors. This will require a clear understanding of abilities as well as the limitations of each TPS, the dosimetry equipment at hand, and the gamma index to perform a comprehensive pre-treatment verification.