A deep learning method for generating pseudo-CT by cone beam CT in radiotherapy / 中华放射肿瘤学杂志

Objective:To investigate the pseudo-CT generation from cone beam CT (CBCT) by a deep learning method for the clinical need of adaptive radiotherapy.Methods:CBCT data from 74 prostate cancer patients collected by Varian On-Board Imager and their simulated positioning CT images were used for this study. The deformable registration was implemented by MIM software. And the data were randomly divided into the training set ( n=59) and test set ( n=15). U-net, Pix2PixGAN and CycleGAN were employed to learn the mapping from CBCT to simulated positioning CT. The evaluation indexes included mean absolute error (MAE), structural similarity index (SSIM) and peak signal to noise ratio (PSNR), with the deformed CT chosen as the reference. In addition, the quality of image was analyzed separately, including soft tissue resolution, image noise and artifacts, etc. Results:The MAE of images generated by U-net, Pix2PixGAN and CycleGAN were (29.4±16.1) HU, (37.1±14.4) HU and (34.3±17.3) HU, respectively. In terms of image quality, the images generated by U-net and Pix2PixGAN had excessive blur, resulting in image distortion; while the images generated by CycleGAN retained the CBCT image structure and improved the image quality.Conclusion:CycleGAN is able to effectively improve the quality of CBCT images, and has potential to be used in adaptive radiotherapy.

Predicting respiratory motion using an Informer deep learning network / 中华放射医学与防护杂志

Objective:To investigate a time series deep learning model for respiratory motion prediction.Methods:Eighty pieces of respiratory motion data from lung cancer patients were used in this study. They were divided into a training set and a test set at a ratio of 8∶2. The Informer deep learning network was employed to predict the respiratory motions with a latency of about 600 ms. The model performance was evaluated based on normalized root mean square errors (nRMSEs) and relative root mean square errors (rRMSEs).Results:The Informer model outperformed the conventional multilayer perceptron (MLP) and long short-term memory (LSTM) models. The Informer model yielded an average nRMSE and rRMSE of 0.270 and 0.365, respectively, at a prediction time of 423 ms, and 0.380 and 0.379, respectively, at a prediction time of 615 ms.Conclusions:The Informer model performs well in the case of a longer prediction time and has potential application value for improving the effects of the real-time tracking technology.

Short-term MRI texture features of medial femoral cartilage after anterior cruciate ligament reconstruction / 中华创伤杂志

Objective:To investigate MRI texture features reflecting short-term medial femoral cartilage variations after anterior cruciate ligament (ACL) reconstruction.Methods:A retrospective case series analysis was conducted referring to the data of 20 patients who received ACL reconstruction at First Affiliated Hospital of Fujian Medical University from January 2017 to January 2020 including 17 males and 3 females, aged 23-43 years [(32.1±5.4)years]. The patients were evaluated before operation and at 1, 4, 12, 24, 48 weeks after operation: (1) the region of interest (ROI) in medial femoral cartilage was divided into ROI1 (meniscus anterior horn cartilage), ROI2 (meniscus body cartilage) and ROI3 (meniscus posterior horn cartilage) according to MRI image acquisition of the affected knee. The texture feature parameters were extracted for screening and analyzing to obtain the parameters reflecting sensitivity to cartilage variations; (2) the level of urinary C-telopeptide of type II collagen (uCTX-II) was measured by ELISA. At different time points, differences in texture feature parameters were compared to screen the parameters most sensitive to cartilage variations, and uCTX-II level were compared as well. Then, Pearson correlation analysis was used to determine the correlation between texture feature parameters most sensitive to cartilage variations and uCTX-II level.Results:After screening and analyzing, the horizontal direction of gray level non-uniformity (Horzl_GlevNonU) and Skewness of histogram (Skewness) of ROI3 were selected as sensitive texture feature parameters reflecting sensitivity to cartilage variations. There were significant difference in the Horzl_GlevNonU and Skewness of ROI3 before operation and at 1, 4, 12, 24, 48 weeks after operation (all P<0.05). Meanwhile, the Horzl_GlevNonU and Skewness of ROI3 were gradually increased over time, with a more significant tendency in the Horzl_GlevNonU. The Horzl_GlevNonU of ROI3 was similar at 4 weeks and 12 weeks after operation ( P>0.05), with significant differences at other time points (all P<0.05). The Skewness of ROI3 was significantly different at 1 week and 48 weeks after operation ( P<0.05), with insignificant differences at other time points (all P>0.05). The Horzl_GlevNonU of ROI3 was selected as the most sensitive texture feature parameter reflecting cartilage variations. Significant difference was reported in uCTX-II level before operation and at 1, 4, 12, 24, 48 weeks after operation (all P<0.05). After Pearson correlation analysis, the Horzl_GlevNonU of ROI3 was positively correlated with uCTX-II level before operation and at 1, 4, 12, 24, 48 weeks after operation ( r=0.554, 0.596, 0.550, 0.632, 0.756, 0.514, P<0.05 or 0.01). Conclusion:The Horzl_GlevNonU of ROI3 is the most sensitive texture feature parameter that reflects the early variations of medial femoral cartilage after ACL reconstruction and is positively correlated with uCTX-II level.

Noise reduction in low-dose computed tomography with noise equivalent image and deep learning / 中华放射医学与防护杂志

Objective:To investigate the method of simulating low-dose CT (LDCT) images using routine dose level scanning mode to generate LDCT images with correspondence to the routine dose CT (RDCT) images in the training sets for deep learning model, which would be used for LDCT noise reduction.Methods:The CT images reconstructed by different algorithms in Philips CT Big Core had different noise levels, where the noise was larger with iDose 4 algorithm and lower with IMR(knowledge-based iterative model reconstruction)algorithm. A new method of replacing LDCT image with noise equivalent reconstructed image was proposed. The uniform module of CTP712 was scanned with the exposure of 250 mAs for RDCT, 35 mAs for LDCT. The images were reconstructed using IMR algorithm for LDCT images and iDose 4 algorithm at multiple noise reduction levels for RDCT images, respectively. The noise distribution of each image set was analyzed to find the noise equivalent images of LDCT. Then, RDCT images, those selected images were used for training cycle-consistent adversarial networks (CycleGAN)model, and the noise reduction ability of the proposed method on real LDCT images of phantom was tested. Results:The RDCT images generated with iDose 4 level 1 could substitute the LDCT images reconstructed with IMR algorithm. The radiation dose was reduced by 86% in low dose scanning. Using CycleGAN model, the noise reduction degree was 45% for uniform module, and 50%, 13%, 7% for CIRS-SBRT 038 phantom in the specific regions of brain, spinal cord, bone, respectively. Conclusions:Equivalent noise level reconstructed images could potentially serve as the alternative of LDCT images for deep learning network training to avoid additional radiation dose. The generated CT images had substantially reduced noise relative to that of LDCT.

Prediction of nuclear grade of renal clear cell carcinoma based on MRI texture analysis in combination with imaging features / 中华放射学杂志

Objective:To explore the application value of MRI texture analysis in combination with imaging features to predict the WHO/International Society of Urological Pathology (ISUP) nuclear grading in pre-operative patients with clear cell renal carcinoma (ccRCC).Methods:MRI images of 78 patients diagnosed as ccRCC by surgical pathology from July 2016 to July 2020 in First Affiliated Hospital of Fujian Medical University were retrospectively analyzed. According to the WHO/ISUP grading system, the patients were divided into low grade group (49 cases, grade Ⅰ in 2 cases and grade Ⅱ in 47 cases) and high grade group (29 cases, grade Ⅲ in 25 cases and grade Ⅳ in 4 cases), and then were assigned to training set ( n= 63) and validation set ( n=15) in a ratio of 7∶3 using random indicator method. MRI radiological features were evaluated and MRI imaging texture features were extracted. The largest-diameter slice of lesion on cross-sectional images was selected and ROIs were drawn on T 2WI and corticomedullary phase (CMP) images, respectively. Quantitative texture analysis software MaZda was used to extract texture features, including gray-scale histogram, co-occurrence matrix, run-length matrix, gradient, autoregressive model and wavelet transform. The extracted texture features were preliminarily selected by the combination of Fisher, probability of classification errorand average correlation coefficient, and interaction information, and then the reduced texture parameters or imaging features were tested by the independent sample t test, Mann-Whitney U test or χ 2 test. Parameters with statistically significant differences were used to construct a multi-factors binary logistic regression model and the ROC curve was used to analyze its effectiveness in predicting high grade ccRCC. Results:In training set, there were significant differences intumor length, shape and margin, enhancement degree of CMP, vein thrombosis and 47 texture features between the low and high grade ccRCC groups. In the training set, 7 multi-factors binary logistic regression model were constructed, including radiological features model (M1), T 2WI texture features model (M2), CMP image texture features model (M3) and combination radiological features of T 2WI texture features model (M4), combination radiological features of CMP images texture features model (M5), combination T 2WI texture features of CMP images texture features model (M6) and combination of all features model (M7). The area under ROC curve of M7 in predicting nuclear grading of ccRCC was the largest, which were 0.901 (95% CI 0.828-0.974) and 0.820 (95% CI 0.564-0.974) in the training set and validation set, respectively. Conclusion:MRI texture analysis combined with imaging features is hopeful to be an effective preoperative noninvasive method in predicting WHO/ISUP grading of ccRCC.

Study on Electromagnetic Interference Risk of Active Implantable Medical Devices in Multiple Unit Environment / 中国医疗器械杂志

Concerning on the safety risks caused by electromagnetic interference of patients implanted with high-risk active implantable medical devices in the environment of domestic MUs, this study evaluates and focuses on the requirements of electromagnetic compatibility in domestic and international standards for rail transit vehicles, the main mechanism of risks caused by EMI, the actual measurement of environmental data in MUs and the working performance of various active implantables in the compartment. The test results shows that all kinds of active implantable medical device samples works normally in the CRH2A EMU in China, and there is still a large margin between the measured radiation emission in MU and the limit required by the standards.

Quality control of VMAT planning using artificial neural network models for nasopharyngeal carcinoma / 中华放射医学与防护杂志

Objective@#To train individualized three-dimensional (3D) dose prediction models for radiotherapy planning, and use the models to establish a planning quality control method .@*Methods@#A total of 99 cases diagnosed as early nasopharyngeal carcinoma (NPC) were analyzed retrospectively, who received simultaneous integrated boost (SIB) with volumetric modulated arc therapy (VMAT). Seven geometric features were extracted, including the minimum distance features from each organs at risk (OARs) to planning target volume (PTV), boost targets and outline, as well as four coordinate position characteristics.89 cases were trained and 10 cases were tested based on 3D dose distribution prediction models using artificial neural network (ANN). A planning quality control method were established based on the prediction models. The dosimetric parameters including D2%, D25%, D50%, D75% and mean dose (MD) of each OAR were used as quality control indicators, and the passing criteria was defined as that the dosimetric difference between manual planning and the predicted dose should be less than 10%. The quality control method was tested with 10 plans designed by a junior physicist.@*Results@#There was no significant discrepancy between the model predicted dose and the result of expert plan in the main dosimetric indexes of 18 OARs. The dose differences of D2%, D25%, D50%, D75% and MD were all controlled within 1.2 Gy.All the 10 plans designed by a junior physicist reached the general clinical dose requirements, while by using our proposes quality control method, one of these plans was observed not optimal enough and some dosimetric parameters of spinal cord, spinal cord PRV, brainstem and brainstem PRV could be improved. After re-optimizing this plan according to the predicted values of the model, the D2% of spinal cord and brainstem decreased by 8.4 Gy and 5.8 Gy, respectively.@*Conclusions@#This study proposes a simple and convenient quality control method for radiotherapy planning. This method could overcome the disadvantage of unified dose constrains without considering patient-specific conditions, and improve the quality and stability of individualized radiotherapy planning.

Clinical application and evaluation of automatic segmentation model based on deep learning for breast cancer radiotherapy / 中华放射肿瘤学杂志

Objective In this study,the deep learning algorithm and the commercial planning system were integrated to establish and validate an automatic segmentation platform for clinical target volume (CTV) and organs at risk (OARs) in breast cancer patients.Methods A total of 400 patients with left and right breast cancer receiving radiotherapy after breast-conserving surgery in Cancer Hospital CAMS were enrolled in this study.A deep residual convolutional neural network was used to train CTV and OARs segmentation models.An end-to-end deep learning-based automatic segmentation platform (DLAS) was established.The accuracy of the DLAS platform delineation was verified using 42 left breast cancer and 40 right breast cancer patients.The overall Dice Similarity Coefficient (DSC) and the average Hausdorff Distance (AHD) were calculated.The relationship between the relative layer position and the DSC value of each layer (DSC_s) was calculated and analyzed layer-by-layer.Results The mean overall DSC and AHD of global CTV in left/right breast cancer patients were 0.87/0.88 and 9.38/8.71 mm.The average overall DSC and AHD range for all OARs in left/right breast cancer patients were ranged from 0.86 to 0.97 and 0.89 to 9.38 mm.The layer-by-layer analysis of CTV and OARs reached 0.90 or above,indicating that the doctors were only required to make slight or no modification,and the DSC_s ≥ 0.9 of CTV automatic delineation accounted for approximately 44.7％ of the layers.The automatic delineation range for OARs was 50.9％-89.6％.For DSC_s ＜ 0.7,the DSC_s values of CTV and the regions of interest other than the spinal cord were significantly decreased in the boundary regions on both sides (layer positions 0-0.2,and 0.8-1.0),and the level of decrease toward the edge was more pronounced.The spinal cord was delineated in a full-scale manner,and no significant decrease in DSC_s was observed in a particular area.Conclusions The end-to-end automatic segmentation platform based on deep learning can integrate the breast cancer segmentation model and achieve excellent automatic segmentation effect.In the boundary areas on both sides of the superior and inferior directions,the consistency of the delineation decreases more obviously,which needs to be further improved.

Quality control of VMAT planning using artificial neural network models for nasopharyngeal carcinoma / 中华放射医学与防护杂志

Objective To train individualized three-dimensional (3D) dose prediction models for radiotherapy planning,and use the models to establish a planning quality control method.Methods A total of 99 cases diagnosed as early nasopharyngeal carcinoma (NPC) were analyzed retrospectively,who received simultaneous integrated boost (SIB) with volumetric modulated arc therapy (VMAT).Seven geometric features were extracted,including the minimum distance features from each organs at risk (OARs) to planning target volume (PTV),boost targets and outline,as well as four coordinate position characteristics.89 cases were trained and 10 cases were tested based on 3D dose distribution prediction models using artificial neural network (ANN).A planning quality control method were established based on the prediction models.The dosimetric parameters including D2％,D25％,D50％,D75％ and mean dose (MD) of each OAR were used as quality control indicators,and the passing criteria was defined as that the dosimetric difference between manual planning and the predicted dose should be less than 10％.The quality control method was tested with 10 plans ()esigned by a junior physicist.Results There was no significant discrepancy between the model predicted dose and the result of expert plan in the main dosimetric indexes of 18 OARs.The dose differences of D2％,D25％,D50％,D75％ and MD were all controlled within 1.2 Gy.All the 10 plans designed by a junior physicist reached the general clinical dose requirements,while by using our proposes quality control method,one of these plans was observed not optimal enough and some dosimetric parameters of spinal cord,spinal cord PRV,brainstem and brainstem PRV could be improved.After re-optimizing this plan according to the predicted values of the model,the D2％ of spinal cord and brainstem decreased by 8.4 Gy and 5.8 Gy,respectively.Conclusions This study proposes a simple and convenient quality control method for radiotherapy planning.This method could overcome the disadvantage of unified dose constrains without considering patient-specific conditions,and improve the quality and stability of individualized radiotherapy planning.

A clinical study of 135 patients with cerebral vascular fenestrations / 中华神经科杂志

Objective To characterize the imaging features of cerebral vascular fenestrations and the clinical features of patients with cerebral infarction.Methods We retrospectively analyzed the magnetic resonance or CT imaging data of 135 cases of cerebral vascular fenestrations from January 2015 to July 2017 in the First Affiliated Hospital of Fujian Medical University.The location,morphology and the other associated vascular diseases were described.The patients who had cerebral infarction were also analyzed.Results One hundred and thirty-five fenestrations were noted at our institution,129 in arteries,and six in veins,the detection rate being 1.1％ (135/12 232).Basilar arteries were most common,which accounted for 53.3％ (72/129) of the fenestrations.Twenty-eight (20.7％) of these patients had other vascular malformations,with a total of 15 aneurysms,two moyamoya diseases,five venous malformations,two arteriovenous fistulas,and four cavernous hemangiomas.Nine patients had cerebral infarctions,of which five patients had fenestration-relevant cerebral infarctions,and no risk factors for cerebral infarctions were found except fenestration in one patient who was diagnosed with cryptogenic stroke.No acute cerebral infarctions or transient ischemic attack occurred in patients with fenestration-relevant cerebral infarctions who had long-term antiplatelet and statin therapy during follow-up.Conclusions Cerebral vascular fenestrations occur most frequently in the basilar artery and may combine with other malformations.Long-term antiplatelet and statin therapy can be used for cerebral infarctions patients with cerebral vascular fenestrations.

Selection, installation, and acceptance test of MRI simulator / 中华放射肿瘤学杂志

Magnetic resonance imaging (MRI) simulator (MRI-Sim) can provide superior images for radiotherapy.Due to the complexity of MRI technology and the safety problem caused by strong magnetic field, the acquisition and implementation of MRI simulation is more complicated than CT simulation.In order to ensure the introduction of MRI-Sim, this paper reviews the selection, installation, and acceptance test of MRI-Sim, including the selection of host and auxiliary equipment, installation site preparation, and safety precautions,as well as MRI-Sim acceptance test and commissioning.

Establishment of the NTCP model for radiation-induced lung injury in patients with non-small cell lung cancer / 中华放射肿瘤学杂志

Objective To examine the incidence of radiation-induced lung injury(RILI)after involved-field intensity-modulated radiation therapy(IMRT)in patients with locally advanced non-small cell lung cancer(NSCLC),and to evaluate the predictability of different models.Methods The clinical data of 242 inoperable or unresectable stage Ⅲ NSCLC patients treated in our hospital from 2007 to 2011 were reviewed. Grade 2 and grade 3 RILI that occurred within 6 months after IMRT were selected as outcome events in this study. The principal component analysis(PCA)model,Lyman-Kutcher-Burman(LKB)model,and mean lung dose(MLD)model were each used to establish a predictive model of normal tissue complication probability (NTCP)for evaluating the dosimetric parameters of IMRT. Results Four principal components were used in the PCA model. The areas under the receiver operating characteristic curve(AUCs)of grade 2 and grade 3 RILI were 0.652 and 0.611,respectively. For the LKB model, the fitted parameters were m=0.46, n=1.35, and D50=23.59 Gy for grade 2 RILI,and m=0.36,n=0.27,and D50=72.67 Gy for grade 3 RILI. The AUCs of grade 2 and grade 3 RILI in the LKB model were 0.607 and 0.585, respectively. For the MLD model, the estimated parameters were γ50=1.073 and D50=24.66 Gy for grade 2 RILI,and γ50=0.97 and D50=48.45 Gy for grade 3 RILI.The AUCs of grade 2 and grade 3 RILI in the MLD model were 0.604 and 0.569,respectively. Conclusions The use of large data set from a single patient population with the same mode of treatment is very important for improving model predictability and stability. Both the LKB model and PCA model can predict the probability of RILI,whereas the MLD model is less effective in predicting grade 3 RILI.

Study on the Detection Rate of Cell and Linearity Performance Detection Method in the CellSearch System / 中国医疗器械杂志

Research on CellSearch system's detection rate of cells and linearity performance detection method, so as to analyze the accurate and reasonable detection method to meet the CellSearch characteristics of the system.

Study on the performance of piezoelectric micro pump for insulin injection / 中国医疗器械杂志

In terms of performance of piezoelectric micro pump, this paper explores the piezoelectric ceramic plate in different wave driven micro pump flow rate. The conclusion is that the square wave voltage gets the biggest micro pump velocity. The velocity and pressure of the micro pump is almost linear relationship, and having nothing to do with the different inner diameter pipes. The piezoelectric micro pump's stability is not good and exists attenuation.

Predictive factors for radiation-induced lung injury in locally advanced non-small cell lung cancer after intensity-modulated radiotherapy / 中华放射肿瘤学杂志

Objective To investigate the incidence of radiation?induced lung injury ( RILI ) in patients with locally advanced non?small cell lung cancer ( LA?NSCLC ) after involved?field intensity?modulated radiotherapy ( IMRT) and concurrent chemotherapy, and to figure out the predictive factors for RILI. Methods Two hundred and fifty?six patients with stage Ⅲ NSCLC who were treated without surgery in our hospital from January 2007 to December 2011 were enrolled as subjects. All patients received involved?field IMRT with a median dose of 60 Gy ( 50?70 Gy) in 30 fractions. In all patients, 109 patients (42.6%) received concurrent chemotherapy. The National Cancer Institute Common Terminology Criteria for Adverse Events Version 3. 0 was used to evaluate the RILI grade. The incidence of grade ≥2 RILI ( symptomatic RILI, SRILI ) within 6 months after radiotherapy served as the end point. The predictive factors for RILI were analyzed using logistic regression model. Results In all patients, 215 ( 84%) were male, and 41(16%) were female. The mean age at diagnosis was 59.2 years. Forty?three (16.7%) patients had grade ≥2 RILI. The mean duration between the incidence of RILI and the beginning of radiotherapy was 64 days ( 20?169 days) . Univariate analysis showed that smoking, peripheral or central tumor location, mean lung dose ( MLD) for both lungs, and V5?V20 for both lungs were suspected to be associated with the development of SRILI (P=0.108,0.106,0.030,0.049). Multivariate analysis showed the MLD and V5?V20 for both lungs were independent predictive factors for SRILI P=(0.048). Conclusions For patients with LA?NSCLC treated with involved?field IMRT, the MLD and the volume of low?dose region in dose volume histogram for both lungs are significantly correlated with the incidence of SRILI.

Predictive factors for acute symptomatic esophagitis in 256 patients with locally advanced non-small cell lung cancer treated with intensity-modulated radiation therapy / 中华放射肿瘤学杂志

Objective To explore the incidence and related predictive factors for acute symptomatic esophagitis in patients with locally advanced non?small cell lung cancer ( NSCLC ) treated with intensity?modulated radiation therapy ( IMRT) . Methods Data were collected retrospectively from 256 patients with inoperable or unresectable stage Ⅲ NSCLC treated in our hospital between January 2007 and December 2011. The radiotherapy target volume included primary lung cancer and lymphatic drainage area involved,with a median dose of 60 Gy in 30 fractions (50-70 Gy).Of all the patients,109 patients (42.6%) received concurrent chemotherapy. Grade ≥2 acute esophagitis ( AE ) ( symptomatic esophagitis ) which occurred during radiotherapy and within 3 months after completion of radiotherapy served as the outcome event. National Cancer Institute Common Terminology Criteria for Adverse Events version 3.0( NCI?CTCAE3.0) was used to evaluate the grade of AE. The logistic regression model was used to analyze the predictive factors. Results A total of 174 patients ( 68%) had treatment?related grade ≥2 AE;154 patients ( 60. 2%) had grade 2 AE and 20 patients (7.8%) had grade 3 AE.The median dose when grade≥2 AE occurred was 30 Gy (11?68 Gy).For grade≥2 AE,multivariate analysis showed that esophageal V5?V60,mean dose,and age were independent predictive factors (P=0.021,0,0.010).For grade ≥3 AE,multivariate analysis showed that esophageal V50?V60 ,concurrent chemotherapy,and body mass index ( BMI) were independent predictive factors ( P= 0.010,0.003,0.019 ) . Old age and higher BMI were the protective factors for grade≥2 and ≥3 AE, respectively. Conclusions For patients with locally advanced NSCLC treated with IMRT, esophageal V50—V60 and concurrent chemotherapy are predictive factors for grade ≥3 AE,and esophageal V50 has a high predictive value for both grade ≥2 and ≥3 AE.

Primary clinical study of hippocampal-sparing prophylactic cranial irradiation in limited-stage small-cell lung cancer / 中华放射肿瘤学杂志

Objective To preliminarily observe the clinical efficacy of hippocampal-sparing prophylactic cranial irradiation (HS-PCI) using helical tomotherapy (HT) in patients with limited-stage small-cell lung cancer (LS-SCLC) after chemoradiotherapy,and compare HT with intensity-modulated radiotherapy (IMRT) and volumetric modulated arc therapy (VMAT) in dose distribution.Methods From April to June,2014,six patients with LS-SCLC who had achieved a complete remission after chemoradiotherapy were assigned to HS-PCI using HT within a month after brain metastasis was ruled out using brain magnetic resonance imaging (MRI).After fusing CT images and MRI images,the hippocampus was contoured in the fusion images and hippocampal avoidance regions were created using a volumetric expansion of 3 mm around the hippocampus.A dose of 25 Gy in 10 fractions to 95％ of planning target volume (PTV) was prescribed in HT,IMRT,and VMAT.The clinical efficacy,adverse reactions,neurocognitive function,and brain metastasis were evaluated for HT.The dose distribution in PTV and hippocampus were compared between HT,IMRT,and VMAT.Results There were one patient with abdominal wall and abdominal lymph node metastases,one patient with local recurrence,and no patient with brain metastasis during the observation period.The numbers of patients with grade 1 and grade 2 headache,dizziness,and hair loss reactions were 3 and 1,3 and 1,and 4 and 2,respectively.There were no significant differences in the average score of the Mini-Mental State Examination before treatment and at 3 and 6 months after treatment (29.7,29.2,and 29.3 ; P =0.083,0.317,and 0.157).The mean dose to the hippocampus was 16.85 Gy for IMRT and 17.59 Gy for VMAT.For HT,the mean doses to the hippocampus and avoidance regions were reduced to 5.26 Gy and 6.21 Gy,respectively.The prescribed dose for HT was reduced by 79％ and 71％ compared with IMRT and VMAT,respectively.The average coverage rate of the prescribed dose was 94.48％ for HT.Conclusions HT achieves promising dose distribution and target coverage in sparing of the hippocampus.Moreover,HT dose not increase the incidence of adverse reactions.The change in neurocognitive function needs to be further studied with longterm observation and large-scale sampling.

Safty and acute toxicities of intraoperative electron radiotherapy for patients with abdominal tumors / 中华放射肿瘤学杂志

Objective To investigate the safety and acute toxicities of intraoperative electron radiotherapy for patients with abdominal tumors.Methods From May 2008 to August 2009, 52 patients with abdominal tumors were treated with intraoperative electron radiotherapy,including 14 patients with breast cancer,19 with pancreatic cancer,3 with cervical cancer, 4 with ovarian cancer, 6 with sarcoma, and 6 with other tumors.Fifteen patients were with recurrent tumors.The intraoperative radiotherapy was performed using Mobetron mobile electron accelerator, with total dose of 9 - 18 Gy.In all, 29, 4 and 19 patients received complete resection, palliative resection and surgical exploration, respectively.The complications during the operations and within 6 months after operations were graded according to Common Terminology Criteria for Adverse Events v3.0 (CTC 3.0).Results The median duration of surgery was 190 minutes.Intraoperative complications were observed in 5 patients, including 3 with hemorrhage, 1 with hypotension,and 1 with hypoxemia, all of which were treated conservatively.The median hospitalization time and time to take out stitches was 12 and 13 days, respectively.And the in-hospital mortality was 4% (2/52).Twentyfour patients suffered post-operative adverse events, including 3 postoperative infections.With a median follow-up time of 183 days, 20% of patients sufferred from grade 3 to 5 adverse events, with hematological toxicities being the most common complication, followed by bellyache.Grade 1 and 2 toxicities which were definitely associated with intraoperative radiotherapy was 28% and 4%, respectively.None of grade 3 to 5 complications were proved to be caused by intraoperative radiotherapy.Conclusions Intraoperative electron radiotherapy is well tolerable and could be widely used for patients with abdominal tumors, with a little longer time to take out stitches but without more morbidities and toxicities compared surgery alone.

Study on relationship between the beginning plan and the anatomy and dosimetry change during intensity modulated radiotherapy for esophageal carcinoma / 中华放射肿瘤学杂志

Objective To study the dynamic changes of anatomy and the dosimeter distribution those changes influenced. Methods Initially simplified intensity modulated radiation therapy (sIMRT)were performed to twenty-nine patients with phase Ⅲ - Ⅳa esophageal carcinoma from January 2007 to March 2009. The target volumes and involving organs were contoured on the primary spiral CT pictures.After sIMRT planning being finished, secondary CT scan was acquired to rectify the treatment center. For eleven patients at every other week and eighteen patients at the fourth week, spiral CT images were acquired according to the same treatment center, and thereafter fused with the first CT images. Firstly, the law of change and the best time of replanning were searched:the changed gross tumor volume (GTV), gross node volume (GTVnd), plan target volume (PTV) and normal organs (lung, spinal cord, heart and outline) on the fusion interface were modified by a single physician, the changes for each structure throughout treatment were measured by system software. Secondly, dose distributions were computed and evaluated for replanning CT using the same beams arrangement as the initial plan. Cumulative dose was estimated using weighted average and compared with the original plan. Results For eleven patients, The law of change:the volume of outlines and GTV gradually decreased, and the change come to peak in the fourth week. The conformal index for PTV gradually decreased, whereas the heterogeneous index gradually increased. For twenty-nine patients on the fourth week, the dose of GTV were more than 60 Gy. The dose of PTV-D95 and CTV-D99 decreased ( t = 1.49, P = 0. 147 and t = 2. 07, P = 0. 048 respectively). The dose of CTV-D99 in two patients deceased to 54 Gy or less. The cord-Dmax and lung V30 increased significantly ( t = - 2. 42, P = 0. 022 and t = -2. 26,P =0. 032). Conclusions During the course of sIMRT for esophageal cancer, the volume of GTV decreased and the change come to peak in the fourth week. It is the best time for evaluating the change of dose of target volume using CT-CT fusion. For some patients, revise of the treatment plan is needed to ensure adequate target volume dosage and safety of normal tissues.

Commissioning of Mobetron mobile intra-operattve radiotherapy accelerator / 中华放射肿瘤学杂志

Objective To commission a Mobetron intra-operative mobile accelerator and analyze the characteristics of its electron beams. Methods The dosimetrie characteristics of the electron beams genera-ted by Mobetron accelerator were measured and compared with those generated by conventional accelerator (Primus, Siemens). M oberton accelerator can generate electron beams of nominal energies of 4,6,9 and 12 MeV. The measurement items were as followings : percentage depth dose perpendicular to water phantom sur-face and beam profiles parallel to water phantom surface, output factors, applicator leakage, electron beam at-tenuation made by lead blocks,and machine output calibration. The measurement devices included a three-dimensional ( 3 D) water scanning phantom, an electrometer, a 0.6 cm3 Farmer ionization chamber, a parallel-plate ionization chamber and solid water slabs. During measurement, all applicators of different tilt angles and diameters were attached to the machine head,and their ends were adjusted to be tangent to the phantom surface. Results Except for the 12 MeV,skin dose for all energies was no more than 90%. The skin dose was higher for Mobetron accelerator electron beams than for regular electron beams. The Dmax depth in water for a 10 cm flat applicator were 0.7,1.3,2.0 and 2.2 cm for the 4 energies,respectively. The depths of 90% dose were 1.0,1.8,2.7 and 3.6 cm, respectively. The selected flat applicator was just 1 cm larger than the tumor bed. But for the beveled applicators,the field flatness and symmetry became worse,and con-sequently,the applicator size had to be selected based on the isodose distribution. The leakage dose at 1 cm outside the applicator was 1.2% ,5.1%, 10.0% and 9.1%, respectively. The lead thickness for full block was 1.5,3.0,4.5 and 6.0 mm,respectively. Conclusions Through the commissioning of Mobetron accel-erator, the machine characteristics are understood, and the data for clinical implementation and routine quality assurance are acquired.