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In order to deliver accurate and safe treatment to cancer patients in radiation therapy using advanced techniques such as intensity modulated radiation therapy (IMRT) and volumetric‐arc radiation therapy (VMAT), patient specific quality assurance (QA) should be performed before treatment. IMRT/VMAT dose measurements in a phantom using various devices have been clinically adopted as standard method for QA. This approach allows the verification of the accuracy of the dose calculation, data transfer, and the delivery system. However, patient‐specific QA procedures are expensive and require significant time and effort by the physicists. Over the past 5 years, machine learning (ML) and deep learning (DL) algorithms for predictions of IMRT/VMAT QA outcome have been investigated. Various ML and DL models have shown promising prediction accuracy and a high potential as time‐efficient virtual QA tool. In this paper, we review the ML and DL based models that were developed for patient specific IMRT and VMAT QA outcome predictions from algorithmic and clinical applicability perspectives. We focus on comparing the algorithms, the dataset sizes, the input parameters and features, the QA outcome prediction approaches, the validation, the performance, the clinical applicability, and the potential clinical impact. In addition, we discuss the present challenges as well as the future directions in the implementation of these models. To the best of our knowledge, this is the first review on the application of ML and DL based models in IMRT/VMAT QA predictions.

We developed a transformer-based multimodal neural network to predict the gamma passing rate (GPR) in stereotactic body radiation therapy (SBRT) patient-specific quality assurance. Using 1265 SBRT beams from two institutions, the model incorporated portal dose prediction fluence maps with beam complexity descriptors such as modulation complexity score and monitor units. A multi-scale visual-textual transformer, integrating a ViT encoder and feedforward network through a fusion head, was compared with state-of-the-art CNNs across nine gamma criteria. Our approach consistently achieved the lowest root mean squared error (RMSE) and mean absolute error (MAE), with values ranging from 0.785% to 4.258% and 0.418% to 3.197%, respectively, and ablation studies highlighted the necessity of multimodal fusion and multi-scale design. These results demonstrate superior predictive accuracy and generalizability, underscoring the potential of transformer-based multimodal learning to enhance treatment optimization and clinical QA efficiency.

Purpose This study aimed to improve the safety and accuracy of radiotherapy by establishing tolerance (TL) and action (AL) limits for the gamma index in patient‐specific quality assurance (PSQA) for intensity‐modulated radiation therapy (IMRT) and volumetric‐modulated arc therapy (VMAT) using SunCHECK software, as per AAPM TG‐218 report recommendations. Methods The study included 125 patients divided into six groups by treatment regions (H&N, thoracic and pelvic) and techniques (VMAT, IMRT). SunCHECK was used to calculate the gamma passing rate (%GP) and dose error (%DE) for each patient, for the planning target volume and organs at risk (OARs). The TL and AL were then determined for each group according to TG‐218 recommendations. We conducted a comprehensive analysis to compare %DE among different groups and examined the relationship between %GP and %DE. Results The TL and AL of all groups were more stringent than the common standard as defined by the TG218 report. The TL and AL values of the groups differed significantly, and the values for the thoracic groups were lower for both VMAT and IMRT. The %DE of the parameters D95%, D90%, and Dmean in the planning target volume, and Dmean and Dmax in OARs were significantly different. The dose deviation of VMAT was larger than IMRT, especially in the thoracic group. A %GP and %DE correlation analysis showed a strong correlation for the planning target volume, but a weak correlation for the OARs. Additionally, a significant correlation existed between %GP of SunCHECK and Delta4. Conclusion The study established TL and AL values tailored to various anatomical regions and treatment techniques at our institution. Establishing PSQA workflows for VMAT and IMRT offers valuable clinical insights and guidance. We also suggest developing a standard combining clinically relevant metrics with %GP to evaluate PSQA results comprehensively.

Background: Quality assurance (QA) programs are designed to improve the quality and safety of radiation treatments, including patient-specific QA (PSQA). The objective of this study was to investigate the conditions in which pretreatment PSQA is performed, to evaluate the root cause of the implementation of more complex techniques, and to identify areas for potential improvement. Materials/Methods: The Octavius 4D (O4D) system accuracy was evaluated using an O4D homogeneous phantom for different field sizes. Tests of the system response to dose linearity, field sizes, and PDD differences were performed against calculated doses for a 6 MV photon beam. The pretreatment verification of 40 VMAT plans was performed using the PTW VeriSoft software (version 8.0.1) for local and global 3D gamma analysis. The reconstructed 3D dose was compared to the calculated dose using 2%/2 mm and 3%/3 mm, 20% of the low-dose threshold, and 95% of the gamma passing rate (%GP) tolerance level. The sensitivity of the O4D system in detecting VMAT delivery and setup errors has been investigated by measuring the variation in %GP values before and after the simulated errors. Results: The O4D system reported good agreement for linearity, field size, and PDD differences with TPS dose, being within ±2% tolerance. The output factors were consistent between the ionization chamber and the O4D detector down to a 4 × 4 cm2 field size with a maximum deviation less than 1%. The introduction of deliberate errors caused a decrease in %GP values. In most scenarios, the %GP value of the simulated errors was detected with 2%/2 mm. Conclusion: The results indicate that the O4D system is sensitive enough to detect delivery and setup errors with the restrictive global criterion of 2%/2 mm for routine pretreatment verification.

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), R2, 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. R2 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.

Background In vivo transit dosimetry using an electronic portal imaging device (EPID‐IVTD) is an important tool for verifying the accuracy of radiation therapy treatments. Despite its potential, the implementation of EPID‐IVTD in breast intensity modulated radiation therapy (IMRT) treatments has not yet been standardized, limiting its clinical adoption. A standardized EPID‐IVTD method could enhance treatment accuracy and improve patient safety in routine clinical practice. Purpose This study aims to develop a method for EPID‐IVTD for whole breast IMRT treatment. Methods Gamma passing rates (GPRs) analysis was the basis of the work conducted on a dataset of 50 patients. The first phase of the work focused on the identification of the reference fraction. In the second phase a method for performing EPID‐IVTD was implemented. Lower‐tolerance and ‐action limits (l‐TL and l‐AL), as introduced by AAPM TG 218, were employed to determine the reference fraction and used as alert and alarm thresholds, respectively, in EPID‐IVTD monitoring. Results The first treatment fraction demonstrated the best dosimetric agreement with the theoretical plan and was therefore used as the reference in the second phase of the study. EPID‐IVTD results showed that 75% of the GPRs ranged from 97.5% to 99.9%, 93.83% were above the l‐TL, 4.31% fell between l‐TL and l‐AL, and 1.86% were below l‐AL. Conclusions A method for the implementation of an effective EPID‐IVTD in whole breast IMRT treatment was developed and is now routinely applied at our center, enabling efficient monitoring in clinical practice.

Background We investigate the gamma passing rate (GPR) consistency when applying different types of gamma analyses, linacs, and dosimeters for volumetric modulated arc therapy (VMAT). Methods A total of 240 VMAT plans for various treatment sites, which were generated with Trilogy (140 plans) and TrueBeam STx (100 plans), were retrospectively selected. For each VMAT plan, planar dose distributions were measured with both MapCHECK2 and ArcCHECK dosimeters. During the planar dose distribution measurements, the actual multileaf collimator (MLC) positions, gantry angles, and delivered monitor units were recorded and compared to the values in the original VMAT plans to calculate mechanical errors. For each VMAT plan, both the global and local gamma analyses were performed with 3%/3 mm, 2%/2 mm, 2%/1 mm, 1%/2 mm, and 1%/1 mm. The Pearson correlation coefficients ( r ) were calculated 1) between the global and the local GPRs, 2) between GPRs with the MapCHECK2 and the ArcCHECK dosimeters, 3) and between GPRs and the mechanical errors during the VMAT delivery. Results For the MapCHECK2 measurements, strong correlations between the global and local GPRs were observed only with 1%/2 mm and 1%/1 mm ( r  > 0.8 with p  < 0.001), while weak or no correlations were observed for the ArcCHECK measurement. Between the MapCHECK2 and ArcCHECK measurements, the global GPRs showed no correlations (all with p  > 0.05), while the local GPRs showed moderate correlations only with 2%/1 mm and 1%/1 mm for TrueBeam STx ( r  > 0.5 with p  < 0.001). Both the global and local GPRs always showed weak or no correlations with the MLC positional errors except for the GPRs of MapCHECK2 with 1%/2 mm and 1%/1 mm for TrueBeam STx and the GPR of ArcCHECK with 1%/2 mm for Trilogy ( r  < − 0.5 with p  < 0.001). Conclusions The GPRs varied according to the types of gamma analyses, dosimeters, and linacs. Therefore, each institution should carefully establish their own gamma analysis protocol by determining the type of gamma index analysis and the gamma criterion with their own linac and their own dosimeter.

Objective: To explore how to combine mind mapping with computer technology in computer teaching. Methods: According to the natural division method, 39 students in the first class used the interactive teaching method of mind map, 36 students in the second class used the interactive teaching method of brainstorming to compare the passing rate of the final examination between the two classes. Results: The pass rate of class one was significantly higher than that of class two, especially the pass rate of female students in class one was 52.4% higher than that of class two. Conclusion: Mind mapping is more suitable for computer teaching in colleges and universities. We should combine mind mapping with computer technology to improve teaching quality.

Background Anatomical changes during thoracic radiation therapy can alter dose delivery and compromise accuracy. Electronic portal imaging device (EPID)‐based in vivo dosimetry (IVD) enables noninvasive monitoring of these changes during treatment. Purpose This study quantitatively evaluated the impact of EPID‐based IVD in volumetric modulated arc therapy (VMAT) and explored the potential of IVD for guiding timely replanning. Methods Eleven patients undergoing thoracic VMAT were retrospectively analyzed: six required replanning as a result of anatomical changes (replanning group), and five did not (normal group). For each fraction, cumulative EPID transit images were acquired, and the global gamma passing rate (GPR) was calculated using six criteria. The difference between treatment GPR and patient‐specific QA GPR (ΔGPR) was used to reduce plan‐specific bias. For four patients in the replanning group (excluding two patients who were excluded due to changes in treatment machines or immobilization), the original plans were recalculated on post‐replanning CT images to estimate dose changes in the planning target volume (PTV) and organs at risks (OARs). Results Across all criteria, the normal group demonstrated higher GPR and ΔGPR values. The 3%/3 mm criterion best discriminated between groups, with a per‐fraction GPR of 98.2% ± 1.1% (normal) versus 90.5% ± 7.1% (replanning). GPR improved after replanning but remained below the levels observed in the normal group. Dose simulations indicated that continuing treatment without replanning increased the PTV dose (D50% up to + 3.5%) and OAR doses, with increases exceeding 5 Gy in some cases. Conclusions EPID‐based IVD detected reduced GPR values in replanning cases, identifying the 3%/3 mm criterion as optimal. A per‐fraction GPR of < 97% under this criterion may indicate notable anatomical changes. Continuing treatment without replanning tended to increase doses delivered to the target and normal tissues. These findings support the clinical utility of IVD and provide quantitative criteria for replanning decisions beyond CT or contour‐based assessments.

Objective Measurement‐based patient specific quality assurance (PSQA) is an increasingly debated topic among medical physicists. Developments like online adaptive radiotherapy and same‐day stereotactic treatments limit the time to do measurement‐based PSQA. Herein, we develop a predictive machine learning model to supplement PSQA by predicting the gamma passing rate (GPR) per stereotactic arc. This streamlines PSQA, providing planners the insight to replan potentially sub‐optimal plans, to mitigate machine time inefficiencies. Methods 122 patients that had previously received HyperArc stereotactic radiosurgery/radiotherapy on a TrueBeam LINAC (Millenium 120 MLCs, 6MV‐FFF) were used to generate a long short‐term memory (LSTM) recurrent neural network to predict the GPR for a 2%/2 mm criteria. GPRs were discretized into three classes: Ideal (≥95%), Investigate [85%–95%), and Replan (<85%). In total, 468 VMAT arcs were used for this model with a class distribution of 370 (Ideal), 65 (Investigate), and 33 (Replan). To counteract the imbalanced data, the minority classes were over‐sampled using synthetic minority over‐sampling technique to generate a balanced dataset. The LSTM model was trained in Python with an 80‐20 training‐testing stratified split. Individual class sensitivity and specificity were recorded following a one versus all method. The final model was deployed clinically through Eclipse Scripting. Results The model demonstrated the following (sensitivity, specificity) for the testing data: Ideal (78.4%, 87.2%), Investigate (75.7%, 89.9%), and Replan (93.2%, 96.6%). The primary focus of this model is to identify failing beams and allow the planner to address this prior to running the PSQA, as such the Replan class was the most important for evaluation. A sensitivity of 93.2% indicates that the model will identify 93.2% of HyperArc plans that need to be replanned with a very high certainty due to the 96.6% specificity. Conclusions The predictive GPR model developed within this research enables HyperArc planners to immediately assess the GPR for each stereotactic arc and preemptively replan potentially failing arcs to optimize the PSQA machine time.