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Background and purpose: Treatment record contains most of information related to treatment plan delivery in radiation therapy. Reviewing treatment record is an important quality assurance (QA) task for safety and quality of patient treatments. This task is usually performed by senior medical physicists. However, it is time-consuming, tedious, and error-prone. To assist this process, a treatment record review system (TRRS) is developed to automatically review items related to treatment delivery record. Methods: The treatment record is firstly extracted from oncology information system (OIS). Based on the daily patient treatment information, the original plan from the treatment planning system is identified. Then the original plan and the delivered plan are correlated. Eight review categories (parameter consistency, treatment completeness, treatment progression, image guidance, override, treatment couch, documentation, and treatment mode) are created. Tailored rules are designed for various review items to automate the review process. As a result, for each daily treatment record, a reviewed flag (pass, failure, warning, and N/A) is assigned by the TRRS. Finally, this system is evaluated by 6 months patient treatment records collected in our institute and compared to the manual process on the same data. Results: TRRS processed a total of 76,651 treatment fractions from 4230 patients with an average of 574 treatments per day. The percentage of the detected anomalies among the total records was 0.76%. The average processing time was 3.9 s and 282 s per treatment record for the automatic and manual processes, respectively. Comparing with the manual process, the time efficiency of TRRS is improved by a factor of 72. The average numbers of anomalies detected by the automatic and manual processes are 21 and 13 per day, respectively. TRRS detects 61.5% more anomalies than those of the manual process. Conclusion: TRRS is not only efficient in processing a large amount of treatment records on a daily basis but also effective in finding more anomalies than those of physics weekly check. The application of the TRRS could significantly reduce the workload of the review physicists and let them focus on more important works related to patient safety. [ABSTRACT FROM AUTHOR]

Background: The multi‐leaf collimator (MLC) is an advanced device utilized for beam shaping and intensity modulation in radiotherapy. With the framework of the contemporary single‐layer MLC featuring a rounded leaf tip, the leaf tip transmission and leakage exert a considerable influence on radiotherapy. Purpose: To scale down the leakage and transmission from the leaf gap when the opposite leaves are closed. Methods: This study proposes a new design of MLC, named dynamic leaf machine (DLM), specifically engineered to diminish the transmission and leakage from the MLC leaf tip. The DLM incorporates an innovative leaf configuration that involves a combination of fixed and movable layers in a single MLC leaf named fixed and movable (FM) layers leaf, which is advantageous in dealing with transmission and leakage from the leaf tip by employing a staggered arrangement of the fixed and movable layer when the opposite leaves are closed. Subsequently, the DLM is assembled on the Elekta VERSA HD accelerator to assess its performance, focusing on aspects of mechanical characters, transmission, leakage, and penumbra. Results: In comparison to conventional MLC leaves, the FM leaf design in the DLM has achieved a remarkable reduction in the leakage between opposed closed leaves, decreasing it from 48.41% to 0.41%. Additionally, the transmission between the adjacent leaves has been measured at 0.59%, and the penumbra in 16 mm × 32 mm field is 2.82 mm, aligning with the performance of conventional MLC. Conclusions: The DLM with FM leaf performs a significant reduction in transmission from the opposite leaf tip in comparison to conventional MLC while maintaining minimal penumbra, effectively mitigating the transmission and leakage problem between opposing leaves, thereby enhancing the effect of radiotherapy. [ABSTRACT FROM AUTHOR]

Background Purpose Methods Results Conclusions Respiratory motion during radiotherapy (RT) may reduce the therapeutic effect and increase the dose received by organs at risk. This can be addressed by real‐time tracking, where respiration motion prediction is currently required to compensate for system latency in RT systems. Notably, for the prediction of future images in image‐guided adaptive RT systems, the use of deep learning has been considered.This study proposed a modified generative adversarial network (GAN) for predicting cine‐MR images in real time.Sagittal cine magnetic resonance (cine‐MR) images of 15 patients with liver cancer who received RT were collected. The image series length of each patient was 300, and each series was divided into training, validation, and test sets. The datasets were further divided using a sliding window size of 10 and a stride of 1. A pix2pix GAN with the generator replaced by convolutional long short‐term memory (ConvLSTM) was proposed herein. A five‐frame cine‐MR image series was inputted into the network, which predicted the next five frames. The proposed network was compared with three advanced networks: ConvLSTM, Eidetic 3D LSTM (E3D–LSTM), and SwinLSTM. Personalized models were trained for each patient. The peak signal‐to‐noise ratio (PSNR), structural similarity index measure (SSIM), visual information fidelity (VIF), Pearson correlation coefficient (Pearson corr), and respiratory motion accuracy of the predicted images were used to evaluate the methods.The proposed network demonstrated optimal performance in the four networks across various indicators. The proposed method provided better SSIM values than ConvLSTM at time steps 1, 2, 3, and 4, and outperformed E3DLSTM at all time steps. In terms of the VIF, the proposed method outperformed E3D–LSTM at all time steps and SwinLSTM at time steps 2, 3, 4, and 5. The proposed method was not significantly different from other methods in terms of Pearson correlation values except that it outperformed E3DLSTM at time step 1. In terms of the Pearson corr, the proposed method consistently achieves better values, especially in the high‐frequency components. Low average landmark tracking errors were provided by the proposed method at time steps 4 and 5 (2.42 ± 0.91 and 2.44 ± 0.96 mm, respectively).The GAN–ConvLSTM network can generate high‐acutance real‐time cine‐MR images and predict respiratory motion with better accuracy. [ABSTRACT FROM AUTHOR]

Background: Volumetric modulated arc therapy (VMAT) is a popular radiotherapy technique in the clinic. As consisting of hundreds of control points in a VMAT plan it is more complex and time consuming than those conventional treatment modalities, such as intensity modulated radiation therapy. To improve the efficiency and accuracy of its quality assurance procedure, a novel automated anomaly detection method was proposed., Methods: The anomaly detection model was the Vanilla AutoEncoder (AE). The input was the aperture-based feature maps extracted from the VMAT treatment plan. The output was the reconstruction error in measuring the original and reconstructed feature maps via the low-dimensional latent variables in the bottleneck of the AE model. The AE model was first trained with feature maps extracted from regular plans, and a detection threshold alpha (α) over the distribution of reconstruction errors was then determined. If a larger reconstruction error was obtained for the testing plan, it was considered an anomaly. The data of VMAT plans of 677 patients undergoing lung cancer radiotherapy were collected and tested. The proposed AE was compared with four other classic detection models (principal components analysis, isolation forest, local outlier factor, and hierarchical density-based spatial clustering of applications with noise) using the testing set. To evaluate its reliability, two types of perturbation factors [leaf offset and monitor unit (MU)] were assessed., Results: Among the five tested models, the AE model achieved the best performance with the area under the receiver operating characteristic curve equal to 0.943. The accuracy and precision of the AE model were 0.769 and 0.407, respectively, which were the highest among the five models. Additionally, in terms of reliability, the AE model was more sensitive in detecting leaf offset and less sensitive in detecting MU variation., Conclusions: In the automatic physics review of radiotherapy plans, the application of two-dimensional aperture-based feature maps to detect irregular VMAT plans via the AE model is both viable and effective for lung cancer patients., Competing Interests: Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-24-1398/coif). The authors have no conflicts of interest to declare., (2025 AME Publishing Company. All rights reserved.)