Objective A quality control(QC)system based on the electronic portal imaging device(EPID)system was used to realize the Multi-Leaf Collimator(MLC)position verification and dose verification functions on Primus and VenusX accelerators.Methods The MLC positions were calculated by the maximum gradient method of gray values to evaluate the deviation.The dose of images acquired by EPID were reconstructed using the algorithm combining dose calibration and dose calculation.The dose data obtained by EPID and two-dimensional matrix(MapCheck/PTW)were compared with the dose calculated by Pinnacle/TiGRT TPS for γ passing rate analysis.Results The position error of VenusX MLC was less than 1 mm.The position error of Primus MLC was significantly reduced after being recalibrated under the instructions of EPID.For the dose reconstructed by EPID,the average γ passing rates of Primus were 98.86%and 91.39%under the criteria of 3%/3 mm,10%threshold and 2%/2 mm,10%threshold,respectively.The average γ passing rates of VenusX were 98.49%and 91.11%,respectively.Conclusion The EPID-based accelerator quality control system can improve the efficiency of accelerator quality control and reduce the workload of physicists.

Objective:To investigate the prediction of the tumor proliferation antigen(Ki-67) expression status in breast cancer patients based on ultrasound radiomics combined with clinicopathologic features.Methods:Breast cancer patients who underwent 2D ultrasound and Ki-67 examination from January 2018 to February 2022 in Changzhou Second People′s Hospital, Nanjing Medical University were retrospectively analyzed. Among them, 427 patients from Chengzhong campus were randomly divided into training and validation sets in the ratio of 8∶2, and 229 patients from Yanghu campus were used as an independent external test set. Radiomics features were extracted from the region of interest of 2D ultrasound images, and the Mann-Whitney U test, recursive feature elimination, and minimum absolute shrinkage and selection operators were used to perform feature dimensionality reduction and to establish a radiomics score(Rad-score). Subsequently, single/multifactor logistic regression regression analyses were used to construct a joint prediction model based on Rad-score and clinicopathological features. Model performance and utility were assessed using the subject operating characteristic area under the curve (AUC), calibration curve, and decision curve analyses. Results:The AUCs of the joint model for predicting Ki-67 expression status in breast cancer in the training, validation, and test sets were 0.858, 0.797, and 0.802, respectively, which were superior to those of the radiomics (0.772, 0.731, and 0.713) and clinical models (0.738, 0.750, and 0.707). Calibration curve and decision curve analyses indicated that the joint model had good calibration and clinical value.Conclusions:A joint model based on ultrasound radiomics and clinicopathological features can effectively predict the Ki-67 expression status of breast cancer, which is expected to become a non-invasive tool for Ki-67 detection and provide clinicians with an important auxiliary diagnostic and therapeutic decision-making basis.

Objective:To evaluate the feasibility of 3D reconstruction techniques based on multi-depth cameras for daily patient positioning in radiotherapy.Methods:Through region of interest (ROI) extraction, filtering, registration, splicing and other processes, multi-depth cameras (Intel RealSense D435i) were used to fuse point clouds in real-time manner to obtain the real optical 3D surface of patients. The reconstructed surface was matched with the external contour of the localization CT to complete the positioning. In this article, the feasibility of the system was validated by using multiple models. Clinical feasibility of 5 patients with head and neck radiotherapy, 10 cases of chest radiotherapy and 5 cases of pelvic radiotherapy was also validated. The data of each group were analyzed by paired t-test. Results:The system running time was 0.475 s, which met the requirement of real-time monitoring. The six-dimensional registration errors in the model experiment were (1.00±0.74) mm, (1.69±0.69) mm, (1.36±0.87) mm, 0.15°±0.14°, 0.25°±0.20°, 0.13°±0.13° in the x, y, z, rotational, pitch and roll directions, respectively. In the actual patient positioning, the mean positioning errors were (0.77±0.51) mm, (1.24±0.67) mm, (0.94±0.76) mm, 0.61°±0.41°, 0.69°±0.55°, and 0.52°±0.35° in the x, y, z, rotational, pitch and roll directions, respectively. The translational error was less than 2.8 mm, and the positioning error was the largest in the pelvic region. Conclusions:Real-time 3D reconstruction techniques based on multi-depth cameras is applicable for patient positioning during radiotherapy. The method is accurate in positioning and can detect the small movement of the patient's position, which meets the requirements of radiotherapy.

A dose reconstruction algorithm for electrionic portal imaging device(EPID)based on calibration and calculation is developed.The raw data of EPID in continuous acquisition mode are corrected for dark field and gain,and the gray level features of bright field are used to determine the field boundary.Subsequently,MU calibration,off-axis calibration and field size calibration are performed on the EPID data,and dose reconstruction is carried out based on the calibrated superimposed flux and the Monte Carlo model of the linac head.Nine cases of IMRT plans are selected for verification and measurement using EPID and MapCheck separately,and the passing rates between the two tools are compared under different gamma criteria(3%/3 mm and 2%/2 mm).For a planned case,the average passing rates of multiple cases verified by MapCheck under the two criteria were 99.02%±1.28%and 90.84%±4.49%,and the average passing rates of the EPID reconstruction models were 98.86%±1.19%and 91.39%±4.80%.Compared with MapCheck,the EPID reconstruction algorithm based on calibration and calculation has no significant difference in the passing rate of IMRT plan verification(P＞0.05),which meets the clinical requirements of dose verification.

Medical image is a powerful tool to assist doctors in the diagnosis and treatment planning.Nowadays,the segmentation of medical images is no longer limited to manual segmentation methods.Traditional methods and deep learning methods have been used to achieve more accurate results in medical image segmentation.Herein some innovative medical image segmentation methods in recent years are reviewed.By elaborating on the innovations of deep learning methods(SAM,SegNet,Mask R-CNN,and U-NET)and traditional methods(active contour model and threshold segmentation model),the differences and similarities between them are compared.The summary of medical image segmentation methods and the prospect is expected to help researchers better grasp and familiarize themselves with research status and development trend.

During radiotherapy of lung tumors, patients′respiration will lead to tumor displacement, making it difficult to accurately expose target volumes to radiation. This will cause damage to the physiological structures of surrounding healthy tissues and reduce the efficacy. Therefore, it is critical to accurately predict the motion trajectories of lung tumors and adjust the positions of electron beams in a real-time manner. Currently, primary methods to predict the motion trajectories of lung tumors include marker-based and marker-free predictions. This review explores the advances in research on both prediction methods and analyzes their basic principles, application scenarios, current challenges, and future trends. It is expected to provide comparatively comprehensive insights for researchers and clinicians in related fields to facilitate the improvement and optimization of radiotherapy for lung tumors.

Brachytherapy is a kind of radiation therapy corresponding to external radiation therapy, i. It has been widely used because it can achieve a higher radiation dose to the lesion area and better protect to the organs at risk. However, tThe workflow of brachytherapy is time-consuming and may lead to patient discomfort, displacement of the applicator or interstitial needle, and organ changes. In recent years, deep learning technology has achieved significant success in the medical field, offering new avenues for the automation of brachytherapy, improvement of radiotherapy precision, and ensuring the safety and effectiveness of radiotherapy plans. This review summarizes the research progress of deep learning in the context of brachytherapy segmentation, image registration, applicator reconstruction, dose prediction and planning optimization, and quality assurance for clinical research reference.

Radiation therapy (RT), as a crucial part of current cancer treatment, has caused great concern since it brings therapeutic efficacy along with the risk of chronic complications. With an increase in age, patients treated with RT are subjected to a high incidence of vascular diseases in the neck and peripheral heart primarily due to the artery stenosis induced by radiation-caused vascular injury. To gain a deeper understanding of artery stenosis, its hazards, clinical presentation, pathogenesis, preventive recommendations, and treatment method was reviewed.

Objective:To construct a cycle dual-task network based on cycleGAN to implement 3D CT synthesis from single-view projection for adaptive radiotherapy of thoracic tumor and then evaluate image quality and dose accuracy.Methods:A total of 45 thoracic tumor patients admitted to the Affiliated Changzhou No.2 People's Hospital of Nanjing Medical University were collected, and 991 cases were also selected from public dataset as pretrained dataset. Multi-view projections were acquired by ASTRA algorithm. The public dataset was divided into a training set of 800 cases, a validation set of 160 cases and a test set of 31 cases. The dataset obtained from patients in our hospital was divided into a training set of 40 cases and a test set of 5 cases. The network included synthetic CT model and multi-view projection prediction model and achieved the dual-task training. The final test only used the synthetic CT model to acquire the predicted CT images and deliver image quality [mean absolute error (MAE), peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM)] and dose evaluation.Results:Image quality evaluation metrics for synthetic CT showed high image synthesis accuracy with MAE of 0.05±0.01, PSNR of 19.08±1.69, SSIM of 0.75±0.04, respectively. The dose distribution calculated on synthetic CT was also close to the actual dose distribution. The mean 3%/3 mm γ pass rate for synthetic CT was 93.1%.Conclusions:A dual-task cycle network modified on cycleGAN has been implemented to rapidly and accurately predict 3D CT from single-view projection, which can be applied to the workflow of adaptive radiotherapy for thoracic cancer. Both image generation quality and dosimetric evaluation demonstrate that synthetic CT can meet the clinical requirements for radiotherapy.

In the current clinical diagnosis, medical images have become an important basis for diagnosis, and different modes of medical images provide different tissue information and functional information. Single-mode images can only provide single diagnostic information, by which difficult and complicated diseases cannot be diagnosed, and comprehensive and accurate diagnostic results can be obtained only with the help of multiple diagnostic information. The multimodal fusion technology fuses multiple modes of medical images into single-mode images, and thus the single-mode images contain complementary information between multiple modes of images, so that sufficient information for clinical diagnosis can be obtained in a single image. In this paper, the multimodal medical image fusion methods are sorted into two types, namely the traditional fusion method and the fusion method based on deep learning.