Objective To fabricate wall-less vascular tissue mimicking materials(TMM)with different tube diameters that match the hemodynamic parameters of human carotid arteries,and to investigate their hemodynamic characteristics.Methods TMM with different diameters and blood mimicking fluids containing scattering particles were fabricated.The variation laws of hemodynamic parameters under different flow velocities and TMM phantom diameters were verified.Key hemodynamic parameters including peak systolic velocity(PSV),end-diastolic velocity(EDV),and resistance index were measured using Doppler ultrasound,and their clinical application value in carotid artery diseases was evaluated.Results The fabricated samples exhibited a sound velocity of(1506.2±0.1)m/s and an attenuation of(0.76±0.01)dB/cm,and the vascular diameters were 4.0 and 6.0 mm,which corresponded to the normal clinical range of the external and internal carotid arteries,respectively.For the 4.0 mm TMM,both PSV and EDV were linearly correlated with flow velocity(R2=0.77,P<0.001;R2=0.74,P=0.001),and Pearson correlation analysis confirmed strong positive correlations(r=0.89,95%CI:0.82-0.93;r=0.94,95%CI:0.90-0.97,all P<0.001).For the 6.0 mm TMM,PSV and EDV also demonstrated significant linear correlations with flow velocity(R2=0.70,P=0.001;R2=0.61,P=0.005),with Pearson correlation analysis revealing strong positive correlations(r=0.86,95%CI:0.78-0.91;r=0.79,95%CI:0.68-0.87).All the data were consistent with hemodynamic parameters and followed the variation law of hemodynamic parameters.Conclusion The fabricated TMM and blood mimicking fluids meet the requirements for clinical ultrasound research on hemodynamics,and their material ratios can be used as a reference for the subsequent researches with diverse objectives.

The radiation-free and unmarked body surface monitoring technology is developed for reducing the additional radiation dose generated by positioning error verification during radiotherapy positioning,and further reducing the positioning error and monitoring the displacement deviation of patients during radiotherapy in real time.At present,the widely used optical surface guided radiotherapy technology is also a type of radiotherapy guided by body surface monitoring.The system mainly uses optical imaging equipment as a tool to complete body surface scanning,three-dimensional reconstruction,real-time monitoring,etc.,thereby assisting doctors to carry out radiotherapy more accurately.Herein the study elaborates on the methods,technologies and research results of guided radiotherapy from the aspects of body surface markers,three-dimensional surface imaging systems and mobile devices,and provides prospects for future researches.

Objective To establish a reliable prediction model for radiation pneumonitis(RP)based on multi-regional radiomics analysis of localizable CT images.Methods A retrospective analysis was conducted on 185 patients who received radiotherapy from January 2021 to June 2023 in the Department of Radiotherapy,Xuzhou Cancer Hospital.Patients were classified as having RP or not based on imaging combined with clinical diagnosis.Three regions of interest(ROI)were defined in the localizable CT images:Lung,Lung-PTV and PTV,and their radiomics features were extracted.After feature screening using methods such as Mann-Whitney Utest,recursive feature elimination,and Lasso,a prediction model was established using support vector machine classification algorithm.The model performance was validated using 6 evaluation metrics:the area under the receiver operating characteristic curve(AUC),accuracy,specificity,sensitivity,positive predictive value,and negative predictive value.Results The prediction model consisted of 7 radiomics features.The clinical model of target-to-lung ratio,PTV model,Lung model,and Lung-PTV model achieved AUC values of 0.535,0.801,0.672,and 0.706 in the test set,respectively.The AUC value and accuracy of PTV model reached 0.843 and 0.775 in the training set,while 0.801 and 0.750 in the test set.PTV model was superior to Lung model,Lung-PTV model,and clinical model in predictive performance.The AUC values of the combined PTV+(Lung-PTV)model in the training and test sets were 0.867 and 0.806,respectively,higher than those of PTV model and Lung-PTV model.Conclusion The predictive ability of the prediction models constructed from radiomics features in different ROI for symptomatic RP varies.The radiomics prediction model using PTV as ROI exhibits superior predictive performance,and the combined multi-regional radiomics model can further improve the predictive ability for RP.

The radiation-free and unmarked body surface monitoring technology is developed for reducing the additional radiation dose generated by positioning error verification during radiotherapy positioning,and further reducing the positioning error and monitoring the displacement deviation of patients during radiotherapy in real time.At present,the widely used optical surface guided radiotherapy technology is also a type of radiotherapy guided by body surface monitoring.The system mainly uses optical imaging equipment as a tool to complete body surface scanning,three-dimensional reconstruction,real-time monitoring,etc.,thereby assisting doctors to carry out radiotherapy more accurately.Herein the study elaborates on the methods,technologies and research results of guided radiotherapy from the aspects of body surface markers,three-dimensional surface imaging systems and mobile devices,and provides prospects for future researches.

Objective To establish a reliable prediction model for radiation pneumonitis(RP)based on multi-regional radiomics analysis of localizable CT images.Methods A retrospective analysis was conducted on 185 patients who received radiotherapy from January 2021 to June 2023 in the Department of Radiotherapy,Xuzhou Cancer Hospital.Patients were classified as having RP or not based on imaging combined with clinical diagnosis.Three regions of interest(ROI)were defined in the localizable CT images:Lung,Lung-PTV and PTV,and their radiomics features were extracted.After feature screening using methods such as Mann-Whitney Utest,recursive feature elimination,and Lasso,a prediction model was established using support vector machine classification algorithm.The model performance was validated using 6 evaluation metrics:the area under the receiver operating characteristic curve(AUC),accuracy,specificity,sensitivity,positive predictive value,and negative predictive value.Results The prediction model consisted of 7 radiomics features.The clinical model of target-to-lung ratio,PTV model,Lung model,and Lung-PTV model achieved AUC values of 0.535,0.801,0.672,and 0.706 in the test set,respectively.The AUC value and accuracy of PTV model reached 0.843 and 0.775 in the training set,while 0.801 and 0.750 in the test set.PTV model was superior to Lung model,Lung-PTV model,and clinical model in predictive performance.The AUC values of the combined PTV+(Lung-PTV)model in the training and test sets were 0.867 and 0.806,respectively,higher than those of PTV model and Lung-PTV model.Conclusion The predictive ability of the prediction models constructed from radiomics features in different ROI for symptomatic RP varies.The radiomics prediction model using PTV as ROI exhibits superior predictive performance,and the combined multi-regional radiomics model can further improve the predictive ability for RP.

Objective To fabricate wall-less vascular tissue mimicking materials(TMM)with different tube diameters that match the hemodynamic parameters of human carotid arteries,and to investigate their hemodynamic characteristics.Methods TMM with different diameters and blood mimicking fluids containing scattering particles were fabricated.The variation laws of hemodynamic parameters under different flow velocities and TMM phantom diameters were verified.Key hemodynamic parameters including peak systolic velocity(PSV),end-diastolic velocity(EDV),and resistance index were measured using Doppler ultrasound,and their clinical application value in carotid artery diseases was evaluated.Results The fabricated samples exhibited a sound velocity of(1506.2±0.1)m/s and an attenuation of(0.76±0.01)dB/cm,and the vascular diameters were 4.0 and 6.0 mm,which corresponded to the normal clinical range of the external and internal carotid arteries,respectively.For the 4.0 mm TMM,both PSV and EDV were linearly correlated with flow velocity(R2=0.77,P<0.001;R2=0.74,P=0.001),and Pearson correlation analysis confirmed strong positive correlations(r=0.89,95%CI:0.82-0.93;r=0.94,95%CI:0.90-0.97,all P<0.001).For the 6.0 mm TMM,PSV and EDV also demonstrated significant linear correlations with flow velocity(R2=0.70,P=0.001;R2=0.61,P=0.005),with Pearson correlation analysis revealing strong positive correlations(r=0.86,95%CI:0.78-0.91;r=0.79,95%CI:0.68-0.87).All the data were consistent with hemodynamic parameters and followed the variation law of hemodynamic parameters.Conclusion The fabricated TMM and blood mimicking fluids meet the requirements for clinical ultrasound research on hemodynamics,and their material ratios can be used as a reference for the subsequent researches with diverse objectives.

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.

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.