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Objective:To study the improvement of normal tissue region of interest (ROI) segmentation based on clustering-based multi-Atlas segmentation method, thereby achieving better delineation of organs at risk.Methods:CT images of 100 patients with cervical cancer who had completed treatment in Zhejiang Cancer Hospital during 2019-2020 were selected as the Atlas database. According to the volume characteristic parameters of the organs at risk (bladder, rectum and outer contour), the Atlas database was divided into several subsets by k-means clustering algorithm. The image to be segmented was matched to the corresponding Atlas library for multi-Atlas segmentation. The dice similarity coefficient (DSC) was used to evaluate the segmentation results.Results:Using 30 patients as the test set, the sub-Atlas generated by different clustering methods were compared for the improvement of image segmentation results. Compared with general multi-Atlas segmentation methods, clustering-based multi-Atlas segmentation method significantly improve the segmentation accuracy for the bladder (DSC=0.83±0.09 vs. 0.69±0.15, P<0.001) and the rectum (0.7±0.07 vs. 0.56±0.16, P<0.001), but no statistical significance was observed for left and right femoral head (0.92±0.04, 0.91±0.02) and bone marrow (0.91±0.06). The average segmentation time of clustering-based multi-Atlas segmentation method was shorter than that of the general multi-Atlas segmentation method (2.7 min vs. 6.3 min). Conclusion:The clustering-based multi-Atlas segmentation method can not only reduce the number of Atlas images registered with the image to be segmented, but also can be expected to improve the segmentation effect and obtain higher accuracy.
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Objective:To develop a multi-scale fusion and attention mechanism based image automatic segmentation method of organs at risk (OAR) from head and neck carcinoma radiotherapy.Methods:We proposed a new OAR segmentation method for medical images of heads and necks based on the U-Net convolution neural network. Spatial and channel squeeze excitation (csSE) attention block were combined with the U-Net, aiming to enhance the feature expression ability. We also proposed a multi-scale block in the U-Net encoding stage to supplement characteristic information. Dice similarity coefficient (DSC) and 95% Hausdorff distance (HD) were used as evaluation criteria for deep learning performance.Results:The segmentation of 22 OAR in the head and neck was performed according to the medical image computing computer assisted intervention (MICCAI) StructSeg2019 dataset. The proposed method improved the average segmentation accuracy by 3%-6% compared with existing methods. The average DSC in the segmentation of 22 OAR in the head and neck was 78.90% and the average 95%HD was 6.23 mm.Conclusion:Automatic segmentation of OAR from the head and neck CT using multi-scale fusion and attention mechanism achieves high segmentation accuracy, which is promising for enhancing the accuracy and efficiency of radiotherapy in clinical practice.
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Objective:To explore the method of constructing automatic delineation model for clinical target volume (CTV) and partially organs at risk (OAR) of postoperative radiotherapy for prostate cancer based on convolutional neural network, aiming to improve the clinical work efficiency and the unity of target area delineation.Methods:Postoperative CT data of 117 prostate cancer patients manually delineated by one experienced clinician were retrospectively analyzed. A multi-class auto-delineation model was designed based on 3D UNet. Dice similarity coefficient (DSC), 95% Hausdorf distance (95%HD), and average surface distance (ASD) were used to evaluate the segmentation ability of the model. In addition, the segmentation results in the test set were evaluated by two senior physicians. And the CT data of 78 patients treated by other physicians were also collected for external validation of the model. The automatic segmentation of these 78 patients by CTV-UNet model was also evaluated by two physicians.Results:The mean DSC for tumor bed area (CTV1), pelvic lymph node drainage area (CTV2), bladder and rectum of CVT-UNet auto-segmentation model in the test set were 0.74, 0.82, 0.94 and 0.79, respectively. Both physicians' scoring results of the test set and the external validation showed more consensus on the delineation of CTV2 and OAR. However, the consensus of CTV1 delineation was less.Conclusions:The automatic delineation model based on convolutional neural network is feasible for CTV and related OAR of postoperative radiotherapy for prostate cancer. The automatic segmentation ability of tumor bed area still needs to be improved.
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Objective:To compare the effect of neoadjuvant chemotherapy vs. concurrent chemoradiotherapy on the target volume and organs at risk for locally advanced bulky (>4 cm) cervical cancer. Methods:From March 1, 2019 to June 30, 2021, 146 patients pathologically diagnosed with cervical cancer were selected and randomly divided into two groups using random number table method: the neoadjuvant chemotherapy (NACT) + concurrent chemoradiotherapy (CCRT) group ( n=73) and CCRT group ( n=73). Patients in the NACT+CCRT group received 2 cycles of paclitaxel combined with cisplatin NACT, followed by CCRT, the chemotherapy regimen was the same as NACT. In the CCRT group, CCRT was given. Statistical description of categorical data was expressed by rate. The measurement data between two groups were compared by Wilcoxon rank-sum test for comparison of two independent samples, and the rate or composition ratio of two groups was compared by χ2 test. Results:Before radiotherapy, GTV in the NACT+CCRT group was (31.95±25.96) cm 3, significantly lower than (71.54±33.59) cm 3 in the CCRT group ( P<0.01). Besides, CTV and PTV in the NACT+CCRT group were also significantly lower compared with those in the CCRT group (both P<0.05). In terms of target volume dosimetry, D 100GTV, D 95CTV, V 100GTV, V 100CTV and V 95PTV in the NACT+CCRT group were significantly higher than those in the CCRT group (all P<0.05). The complete remision (CR) rates in the NACT+CCRT and CCRT groups were 86.3% and 67.6%, with statistical significance between two groups ( P<0.01) . Regarding organs at risk, NACT+CCRT group significantly reduced the dose to the bladder, rectum, small intestine and urethra compared with CCRT group (all P<0.05). Conclusions:NACT can reduce the volume of tumors in patients with large cervical masses, increase the radiation dose to tumors, reduce the dose to organs at risk, and make the three-dimensional brachytherapy easier. Therefore, NACT combined with CCRT may be a new choice for patients with locally advanced cervical cancer with large masses.
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Objective:To compare the effect of uPWS R15 software based on deep learning with MIM-Maestro 6.9 software based on atlas library to automatically delineate the organs at risk of prostate cancer in order to provide a reference for clinical application.Methods:The CT data of 90 prostate cancer patients admitted to the Department of Oncology Radiotherapy of the Affiliated Hospital of North Sichuan Medical College from 2018 to 2022 were retrospectively selected. Based on the uPWS R15 software developed by Shanghai United Imaging Medical Technology Company and the MIM-Maestro 6.9 software developed by Beijing Mingwei Vision Medical Software Company, the effects of uPWS and MIM software on automatic delineation of organs at risk were evaluated according to five parameters, including delineation time (T), Dice similarity coefficient (DSC), Jaccard similarity coefficient (JSC), Hausdorff distance (HD) and the mean distance to agreement (MDA).Results:The sketching time of uPWS software was less than that of MIM software. There were no significant differences in the sketching effect of femoral head and skin between the two software (all P>0.05). The delineation of right kidney ( tMDA=-3.43, zDSC=-4.03, zJSC=-4.16, P<0.05), left kidney ( tMDA=-3.87, zDSC=-4.18, zJSC=-4.41, P<0.05), small intestine ( tMDA=-8.57, zDSC=-9.99, tJSC=14.21, P<0.05) and rectum ( zMDA=-4.00, tDSC=-9.98, tJSC= 9.72, P< 0.05) except HD, was statistically different. The bladder ( z=-7.88, -9.00, -8.17, -8.74, P<0.05) and spinalcord ( z=-3.87, -4.43, 4.03, 3.05, P<0.05) were also delineated with significant differences. The DSC automatically delineated by uPWS software was >0.7, while the DSC automatically delineated by MIM software was >0.7 for all other organs at risk except small intestine and rectum. In addition, the HD, MDA and JSC values of the organs at risk (bilateral femoral head, bilateral kidneys, spinal cord, bladder, skin, rectum and small intestine) automatically delineated by uPWS software were generally better than those with MIM software. Conclusions:The uPWS software outlines better than the MIM software, but the MIM software can also be used clinically with modifications to the small bowel and rectum, saving a great deal of time in preparation for radiation therapy.
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Objective To study the irradiation dose of organs at risk (OAR) in involved field radiation and extended field radiation in patients with thoracic esophageal cancer who received intensity modulated radiotherapy (IMRT). Methods A total of 40 patients with thoracic esophageal cancer were treated with IMRT. The involved field, extended field, and OAR were outlined to generate IMRT plans. The conformity index (CI) and homogeneity index (HI) of planning target volume (PTV) and the irradiation parameters of OAR were evaluated for the two plans. Paired t-test was used for comparison of irradiation parameters. Results The PTV of both plans received the prescribed dose. There were no significant differences in CI and HI of PTV between the two groups (P = 0.317, 0.130). There were significant differences in average lung dose, lung V5, lung V20, lung V30, spinal cord Dmean, heart Dmean, heart Dmax, heart V30, heart V40, and heart V60 between the two groups (P < 0.01). Conclusion Compared with the extended field, the involved field can reduce the irradiation dose of ORA in patients with thoracic esophageal cancer, thus reducing the risk of radiation.
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Objective:To measure serum 25-hydroxyvitamin D (25(OH)D) levels in older adult patients with essential hypertension and correlate serum 25(OH)D levels with target organ damage.Methods:The general data and laboratory test results of 163 older adult patients with essential hypertension who received treatment in Zhejiang Veteran Hospital between January 2019 and April 2021 were collected for this study. The included patients were divided into different groups according to the number of injured target organs, serum 25(OH)D level, and single target organ damage site. Two variables were correlated using the Pearson correlation analysis. The factors that affect target organ damage were analyzed using multivariate logistic regression analysis.Results:There were significant differences in age, course of the disease, serum 25(OH)D level, systolic blood pressure, and diastolic blood pressure among patients with different numbers of damaged target organs ( F = 16.95, 14.39, 14.95, 33.18, 20.88, all P < 0.001). There were significant differences in serum triacylglycerol level and 25(OH)D level among the patients who had different serum 25(OH)D levels ( F = 2.31, 178.48, both P < 0.05). There was a negative correlation between serum 25(OH)D levels and serum triacylglycerol levels. Serum 25(OH)D level was significantly lower in patients with damage to a single target organ heart, kidney, or carotid artery than in patients without target organ damage ( t = 9.24, 6.15, 6.09, all P < 0.05). There was no significant difference in serum 25(OH)D level between patients with damage to a single target organ ( P > 0.05). Older age, long course of disease, high systolic and diastolic blood pressure, and low serum 25(OH)D level were the independent risk factors for target organ damage (all P < 0.05). Conclusion:There is a correlation between serum 25(OH)D levels and target organ damage in older adult patients with essential hypertension. However, the evidence of vitamin D deficiency and target organ damage in essential hypertension is still insufficient, and further investigation is needed. This study is highly innovative and scientific.
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Objective:To investigate the optimal bladder filling volume in the 3D brachytherapy of postoperative cervical cancer.Methods:Totally 111 early cervical cancer patients with positive incisal margins or insufficient safety boundaries were included. The normal saline 50, 60, 70, 80, 90, and 100 ml were filled into their bladders, and accordingly six groups were determined, and 66, 69, 66, 69, 72, 56 person-times in each group, respectively. The CT-based simulation positioning was performed. According to the ICRU 89 report, high-risk clinical target volume and organs at risk such as bladder and rectum were delineated. The Oncentra planning system was used to prepare the treatment program. The high-risk clinical target volume (HR-CTV), D90, and the D2 cm 3 and D1 cm 3 of organs at risk under different volumes were recorded. Results:Compared to the 60 ml group, the volume and dosage of HR-CTV in the groups of 50, 70, 80, 90, and 100 ml had no significant difference ( P>0.05). The D2 cm 3 and D1 cm 3 of the bladder and rectum of patients in these groups significantly decreased, and the difference was statistically significant ( tbladder = 3.21, 5.83, 2.89, 12.95, 7.96, Pbladder = 0.031, 0.010, 0.041, 0.000, 0.001; trectum = 2.94, 4.66, 2.53, 5.89, 4.13, Prectum = 0.037, 0.024, 0.049, 0.005, 0.028). The pairwise comparison among these groups except for the 60 ml group showed that the volume and dosage of HR-CTV and the D2 cm 3 and D1 cm 3 of bladder and rectum had no significant difference ( P > 0.05). Moreover, the D2 cm 3 and D1 cm 3 of sigmoid colon and small intestine of these groups had no significant difference ( P > 0.05). Conclusions:In the 3D brachytherapy of postoperative early cervical cancer, a bladder filling volume of 60 ml can ensure the volume and dose of HR-CTV and can protect the bladder and rectum compared with other filling volumes.
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Objective:To evaluate the application of a multi-task learning-based light-weight convolution neural network (MTLW-CNN) for the automatic segmentation of organs at risk (OARs) in thorax.Methods:MTLW-CNN consisted of several layers for sharing features and 3 branches for segmenting 3 OARs. 497 cases with thoracic tumors were collected. Among them, the computed tomography (CT) images encompassing lung, heart and spinal cord were included in this study. The corresponding contours delineated by experienced radiation oncologists were ground truth. All cases were randomly categorized into the training and validation set ( n=300) and test set ( n=197). By applying MTLW-CNN on the test set, the Dice similarity coefficients (DSCs) of 3 OARs, training and testing time and space complexity (S) were calculated and compared with those of Unet and DeepLabv3+ . To evaluate the effect of multi-task learning on the generalization performance of the model, 3 single-task light-weight CNNs (STLW-CNNs) were built. Their structures were totally the same as the corresponding branches in MTLW-CNN. After using the same data and algorithm to train STLW-CNN, the DSCs were statistically compared with MTLW-CNN on the testing set. Results:For MTLW-CNN, the averages (μ) of lung, heart and spinal cord DSCs were 0.954, 0.921 and 0.904, respectively. The differences of μ between MTLW-CNN and other two models (Unet and DeepLabv3+ ) were less than 0.020. The training and testing time of MTLW-CNN were 1/3 to 1/30 of that of Unet and DeepLabv3+ . S of MTLW-CNN was 1/42 of that of Unet and 1/1 220 of that of DeepLabv3+ . The differences of μ and standard deviation (σ) of lung and heart between MTLW-CNN and STLW-CNN were approximately 0.005 and 0.002. The difference of μ of spinal cord was 0.001, but σof STLW-CNN was 0.014 higher than that of MTLW-CNN.Conclusions:MTLW-CNN spends less time and space on high-precision automatic segmentation of thoracic OARs. It can improve the application efficiency and generalization performance of the models.
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Objective:To explore a new technology that can protect the lungs and heart better by utilizing the dose distribution attributes of the half-field and the characteristics of the VMAT (volumetric modulated arc therapy) technology.Methods:A three-dimensional water tank was used to measure the dose of symmetrical field and half field edge and analyze them comparatively. A total of 50 patients with left breast cancer receiving the postoperative radiotherapy were selected. Among them, 25 patients were performed conserving surgery and 25 patients were performed radical mastectomy. After the operation, all the patients received the prescription dose of 50 Gy in 25 fractions. Based on the Eclipse system, the symmetrical field continuous arc VMAT technology and the semi-field segmented arc VMAT technology were used to design the plan. Besides, the dose suitability data and the treatment efficiency of target areas and organs at risk were compared and analyzed.Results:The radiation size of half-field did not increase with the increased depth in the water mode. The symmetric field gradually enlarged due to the angle of tensor factor, increased to about 2 cm at the depth of 30 cm, and the delivery dose in the half-field was lower than that in the symmetric field. The closer the field edge is, the more obvious it is. Compared with the symmetric field continuous arc plan, the half-field segmental arc VMAT plan significantly improved the delivery dose of the lungs and heart ( t=-4.11, -4.42, P=0.00), in which the mean values of V5, V30, and Dmean for the whole structure of the heart were reduced by 52.5%, 65.5%, and 47%, respectively. The left anterior descending coronary artery, which was closely related to the target area, had a decrease of more than 20%. The mean values of V5, V10, V20, and Dmean of the affected lung were reduced by 21.6%, 24.8%, 25.0%, and 23.2%, respectively. The mean values of the doses of other endangered healthy organs, and the execution time of half-field segment arc plan were also better than the continuous arc plan. Conclusions:For breast cancer radiotherapy, the combination of half-field and VMAT can give full play to the advantages of half-field and VMAT, and significantly reduce the irradiated dose of the heart, affected lung, and healthy side of the breast.
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@#This is a retrospective study, the organ doses of the bladder and the rectum were compared between routine PLATO V14.2.3 (Nucletron B.V., Veenendaal, The Netherlands) and newer version software Oncentra MasterPlan (OMP) V4.3 (Nucletron B.V., Veenendaal, The Netherlands) treatment planning systems (TPS). The treatment data of 32 intracavitary brachytherapy patients at Hospital Universiti Sains Malaysia from January 2010 to June 2015 were used. These data sets were used for catheter reconstruction for both PLATO and OMP TPS followed by independent verification using Excel. There was no significant difference in mean doses to organs at risk (OARs) that calculated by both TPS (p>0.05). The mean percentage of doses calculated by PLATO TPS for bladder and rectum were 66.58 ± 27.42% and 46.27 ± 14.47%, respectively. While, the mean percentage of doses for bladder and rectum calculated by OMP TPS were 65.68 ± 24.24% and 46.46 ± 16.66%, respectively. The mean percentage difference in doses comparison between independent verification calculation and PLATO TPS was 1.96 ± 6.00% and then became 6.37 ± 5.17% when it was compared with OMP TPS. Overall, the dose calculation differences for both versions of TPS were within the range recommended by Nuclear Regulatory Commission (NRC). The dose calculations of the two treatment planning systems showed good agreement and both could be used in planning intracavitary brachytherapy for cervical cancer. Whereas, Excel based independent verification is suitable to be implemented as routine dose verification programme prior to treatment delivery.
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Objective:To investigate the effects of deep learning-based AiContour ??versus atlas-based Raystation ?? automatic contouring methods on the contouring of organs-at-risk on CT images of patients with rectal cancer who undergo radiotherapy, providing evidence for clinical application. Methods:Fifty patients with rectal cancer who received treatment during January to June 2020 in Zhejiang Provincial People's Hospital (Affiliated Hospital of Hangzhou Medical College) were included in this study. The CT images from 20 patients with rectal cancer that had been contoured by experienced radiotherapist were selected as target images and automatically contoured using the data template library of AiContour ?? and Raystation ?? automatic contouring methods. Hausdorff distance, mean distance to agreement, dice similarity coefficient, Jaccard coefficient were used to quantitatively evaluate the accuracy of the volume of contour of organs-at-risk automatically sketched by the two methods. Results:There was no significant difference in Hausdorff distance in left femoral head [(6.81 ± 2.66) vs. (7.24 ± 2.10)], right femoral head [(7.38 ± 3.91) vs. (8.14 ± 3.71)], pelvis [(24.00 ± 9.01) vs. (24.66 ± 9.67)] between AiContour ?? and Raystation ?? automatic contouring methods ( tleft femoral head = -0.831, tright femoral head = -0.821, tpelvis = -0.357, all P > 0.05). Significant differences were observed in mean distance to agreement, dice similarity coefficient and Jaccard coefficient of organs-at-risk (all P < 0.05). The mean values of dice similarity coefficient automatically sketched by AiContour ?? method were > 0.7. The DSC of left kidney, right kidney, rectum and bladder automatically sketched by Raystation ?? method were < 0.7, and the dice similarity coefficient values of other organs-at-risk automatically sketched by Raystation ?? method were > 0.7. In addition, Hausdorff distance, mean distance to agreement and Jaccard coefficient values of organs-at-risk automatically sketched by AiContour ?? method were superior to those automatically sketched by Raystation ??. Conclusion:After slight modification, the organs-at-risk automatically sketched by AiContour ?? and Raystation ?? methods can meet clinical requirement. The contouring effects provided byAiContour ?? method were superior to those provided by Raystation ?? method.
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Purpose: The purpose of this study was to evaluate the organs at risk (OARs) doses for lung tumors in gated radiotherapy (RT) compared to conventional RT using the four-dimensional extended cardiac-torso (4D-XCAT) digital phantom in a simulation study. Materials and Methods: 4D-XCAT digital phantom was used to create 32 digital phantom datasets of different tumor diameters of 3 and 4 cm, and motion ranges (MRs) of 2, 2.5, 3, and 3.5 cm and each tumor was placed in four different lung locations (right lower lobe, right upper lobe, left lower lobe, and left upper lobe). XCAT raw binary images were converted to the digital imaging and communication in medicine format using an in-house MATLAB-based program and were imported to treatment planning system (TPS). For each dataset, gated and conventional treatment plans were prepared using Planning Computerized RadioTherapy-three dimensional (PCRT-3D) TPS with superposition computational algorithm. Dose differences between gated and conventional plans were evaluated and compared (as a function of 3D motion and tumor volume and its location) with respect to the dose-volume histograms of different organs-at-risk. Results: There are statistically significant differences in dosimetric parameters among gated and conventional RT, especially for the tumors near the diaphragm (P < 0.05). The maximum reduction in the mean dose of the lung, heart, and liver were 6.11 Gy, 1.51 Gy, and 10.49 Gy, respectively, using gated RT. Conclusions: Dosimetric comparison between gated and conventional RT showed that gated RT provides relevant dosimetric improvements to lung normal tissue and the other OARs, especially for the tumors near the diaphragm. In addition, dosimetric differences between gated and conventional RT did generally increase with increasing tumor motion and decreasing tumor volume
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The segmentation of organs at risk is an important part of radiotherapy. The current method of manual segmentation depends on the knowledge and experience of physicians, which is very time-consuming and difficult to ensure the accuracy, consistency and repeatability. Therefore, a deep convolutional neural network (DCNN) is proposed for the automatic and accurate segmentation of head and neck organs at risk. The data of 496 patients with nasopharyngeal carcinoma were reviewed. Among them, 376 cases were randomly selected for training set, 60 cases for validation set and 60 cases for test set. Using the three-dimensional (3D) U-NET DCNN, combined with two loss functions of Dice Loss and Generalized Dice Loss, the automatic segmentation neural network model for the head and neck organs at risk was trained. The evaluation parameters are Dice similarity coefficient and Jaccard distance. The average Dice Similarity coefficient of the 19 organs at risk was 0.91, and the Jaccard distance was 0.15. The results demonstrate that 3D U-NET DCNN combined with Dice Loss function can be better applied to automatic segmentation of head and neck organs at risk.
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Objective@#To compare the accuracy of two automatic segmentation softwares (Smart Segmentation and MIM Atlas) in organs at risk (OARs) contouring for nasopharyngeal carcinoma (NPC).@*Methods@#Totally 55 NPC patients were retrospectively reviewed with manually contoured OARs on CT images, in which 30 cases were randomly selected to create a data base in the Smart Segmentation and MIM Atlas. The remaining 25 cases were automatically contoured with Smart Segmentation and MIM as test cases. The automatic contouring accuracies of two softwares were evaluated with Dice coefficient(DSC), Hausdorff distance(HD), and absolute volume difference(△V) using manual contours as a golden standard.@*Results@#The overall DSC, HD and △V of all organs contoured by MIM Atlas and Smart Segmentation were (0.79±0.13) vs. (0.62±0.24) (t=14.06, P<0.05), (5.50±3.84)mm vs.(8.38±4.88)mm (t=-11.40, P<0.05), and (1.52±2.46) cm3 vs. (2.38±3.57) cm3 (t=-4.70, P<0.05), respectively. The average DSC of 11 organs (brain stem, optic chiasm, bilateral lens, bilateral optic nerve, bilateral eyeballs, bilateral parotid gland, spinal cord) delineated by MIM Atlas was statistically greater than that of Smart Segmentation (t=5.27, 4.41, 6.34, 5.70, 10.62, 7.45, 3.96, 4.26, 6.25, 5.42, 7.23, P<0.05). The average HD of 10 organs (brain stem, optic chiasm, bilateral lens, bilateral optic nerve, bilateral eyeballs, left parotid gland, spinal cord) delineated by MIM Atlas was statistically less than that of Smart Segmentation (t=-4.51, -4.49, -3.92, -3.45, -5.36, -5.56, -3.89, -3.90, -3.60, -3.68, P<0.05). The average △V of 6 organs (brain stem, optic chiasm, left len, bilateral optic nerve, right eyeball) delineated by MIM Atlas was statistically less than that of Smart Segmentation (t=-2.83, -3.39, -2.56, -2.27, -2.43, -2.51, P<0.05).@*Conclusions@#Both softwares have reasonable contouring accuracy for larger organs. The accuracy decreased with the decrease of organ volumes and blurred boundary. Generally, MIM Atlas′s performs better than Smart Segmentation does.
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Objective@#To investigate the impact of bladder filling status on the movement of uterine and on the volume of organs at risk(OAR), so as to provide theoretical basis for individualized internal target volume (ITV) and planning target volume (PTV).@*Methods@#Simulation CT images for a total of 27 patients with locally advanced cervical cancer were acquired with empty bladder, 1.0 h after bladder filling, 1.5 h after bladder filling, respectively. The volumes of uterine corpus, cervix, OARs, CTV and PTV 1.0 h after bladder filling were delineated. The impact of bladder filling status on the displacements of cervix and corpus, volumetric changes of rectum, small intestine, and the volumes of small intestine, bladder and rectum within PTV were analyzed. The correlation between bladder filling status and uterine displacement and volume of OAR was also investigated, as well as the volume of cervix and corpus in the PTV with empty bladder and 1.5 h after bladder filling.@*Results@#Bladder filling status is different for individual cases. The maximum movement range of cervix and uterine body with bladder filling state was 0-3.04 cm and 0-4.31 cm respectively. The anterior displacements of corpus (F=7.818, P<0.05), the volumetric changes of blander, as well the volume of bladder and small intestine in the PTV (F=46.197, 44.609, 29.546, P<0.05) were significantly different between different bladder filling status. The bladder filling status was correlated with the displacements of the anterior of corpus, volumetric changes of small intestine, and the volumes of bladder and small intestine within the PTV (r=-0.232, -0.298, 0.915, -0.336, P<0.05). The volumes of cervix and corpus out of the PTV were significantly different between the empty bladder and 1.5 h after bladder filling (t=-1.326, -1.559, P<0.05).@*Conclusions@#Bladder filling status was different for individual patients. The displacements of the anterior of the corpus were significantly affected by the bladder filling status. Increasing the anterior margin of PTV was recommended. The consistency of bladder filling status was critical for the precise cervical cancer IMRT.
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Objective@#To investigate the feasibility and dosimetric characteristics of using dual-arc volumetric modulated arc therapy and multiple partial-arc VMAT for T3 lung cancer.@*Methods@#From June 2016 to May 2018, thirteen lung cancer patients with large planning target volume were replanned with dual full arcs VMAT(F-VMAT) and six partial-arc s VMAT(P-VMAT)on RayStation v4.5 RayArc function.PTV volume median was 550.9cm3(ranged 402.2-834.8cm3) and to a prescribed dose of 60 Gy in 30 fractions.Equivalent target coverage was required for all plans, and clinical goals were evaluated using various dose-volume metrics.These included PTV dose conformity, mean lung/heart dose, lung V5, V10, V20, V30, heart V30 and V40, and Dmax of spinal canal.The total monitor units (MUs) were also examined.@*Results@#All VMAT plans satisfied the treatment criteria.F-VMAT achieved better homogeneity index(HI) and MUs than P-VMRT(t=-3.904, P=0.002), and the conformal number(CN) of tumor volumes was likely clinically indistinguishable.However, F-VMAT significantly reduced lung V5, V10 and mean lung dose[V5: (51.31±5.36)% vs.(43.44±5.28)%, t=6.908, P=0.00; V10: (38.34±3.26)% vs.(34.05±3.74)%, t=4.632, P=0.001; Dmean: (1 449±117.19)cGy vs.(1 375.38±148.98)cGy, t=4.93, P=0.00], and heart dosimetric parameters were also observed in favor of P-VMRT[V30: (20.6±10.4)% vs.(16.4±8.9)%, t=3.822, P=0.02; V40: (14.6±7.5)% vs.(11.88±7.1)%, t=3.096, P=0.009; Dmean: (1 442.9±651.2)cGy vs.(1 263.5±605.6)cGy, t=3.986, P=0.02], and there were no statistically significant differences in lung V20, V30 and spinal cord Dmax between the two groups(all P>0.05).@*Conclusion@#VMAT is an effective treatment for stage T3 lung cancer patients.The primary advantage of P-VMAT was the reduction in low dose area and decreased risk of symptomatic radioactive lung injury.It may be a priority for pulmonary malignancy patients with the large planning target volume.
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PURPOSE: The aim is to study the dependence of deformable based auto-segmentation of head and neck organs-at-risks (OAR) on anatomy matching for a single atlas based system and generate an acceptable set of contours. METHODS: A sample of ten patients in neutral neck position and three atlas sets consisting of ten patients each in different head and neck positions were utilized to generate three scenarios representing poor, average and perfect anatomy matching respectively and auto-segmentation was carried out for each scenario. Brainstem, larynx, mandible, cervical oesophagus, oral cavity, pharyngeal muscles, parotids, spinal cord, and trachea were the structures selected for the study. Automatic and oncologist reference contours were compared using the dice similarity index (DSI), Hausdroff distance and variation in the centre of mass (COM). RESULTS: The mean DSI scores for brainstem was good irrespective of the anatomy matching scenarios. The scores for mandible, oral cavity, larynx, parotids, spinal cord, and trachea were unacceptable with poor matching but improved with enhanced bony matching whereas cervical oesophagus and pharyngeal muscles had less than acceptable scores for even perfect matching scenario. HD value and variation in COM decreased with better matching for all the structures. CONCLUSION: Improved anatomy matching resulted in better segmentation. At least a similar setup can help generate an acceptable set of automatic contours in systems employing single atlas method. Automatic contours from average matching scenario were acceptable for most structures. Importance should be given to head and neck position during atlas generation for a single atlas based system.
Subject(s)
Humans , Brain Stem , Head and Neck Neoplasms , Head , Larynx , Mandible , Methods , Mouth , Neck , Organs at Risk , Pharyngeal Muscles , Radiotherapy , Spinal Cord , TracheaABSTRACT
Objective To compare the accuracy of two automatic segmentation softwares ( Smart Segmentation and MIM Atlas) in organs at risk ( OARs) contouring for nasopharyngeal carcinoma ( NPC) . Methods Totally 55 NPC patients were retrospectively reviewed with manually contoured OARs on CT images, in which 30 cases were randomly selected to create a data base in the Smart Segmentation and MIM Atlas. The remaining 25 cases were automatically contoured with Smart Segmentation and MIM as test cases. The automatic contouring accuracies of two softwares were evaluated with Dice coefficient( DSC) , Hausdorff distance( HD) , and absolute volume difference(△V) using manual contours as a golden standard. Results The overall DSC, HD and △V of all organs contoured by MIM Atlas and Smart Segmentation were (0.79±0.13) vs. (0.62±0.24) (t= 14.06, P<0.05),(5.50±3.84)mm vs.(8.38±4.88)mm ( t=-11. 40, P<0. 05 ) , and ( 1. 52 ± 2. 46 ) cm3 vs. ( 2. 38 ± 3. 57 ) cm3 ( t = -4. 70, P<0. 05 ) , respectively. The average DSC of 11 organs ( brain stem, optic chiasm, bilateral lens, bilateral optic nerve, bilateral eyeballs, bilateral parotid gland, spinal cord) delineated by MIM Atlas was statistically greater than that of Smart Segmentation ( t=5. 27, 4. 41, 6. 34, 5. 70, 10. 62, 7. 45, 3. 96, 4. 26, 6. 25, 5. 42, 7. 23, P<0. 05) . The average HD of 10 organs ( brain stem, optic chiasm, bilateral lens, bilateral optic nerve, bilateral eyeballs, left parotid gland, spinal cord) delineated by MIM Atlas was statistically less than that of Smart Segmentation ( t=-4. 51, -4. 49, -3. 92, -3. 45, -5. 36, -5. 56, -3. 89, -3. 90,-3. 60, -3. 68, P<0. 05). The average △V of 6 organs (brain stem, optic chiasm, left len, bilateral optic nerve, right eyeball) delineated by MIM Atlas was statistically less than that of Smart Segmentation ( t=-2. 83, -3. 39, -2. 56, -2. 27, -2. 43, -2. 51, P<0. 05 ) . Conclusions Both softwares have reasonable contouring accuracy for larger organs. The accuracy decreased with the decrease of organ volumes and blurred boundary. Generally, MIM Atlas's performs better than Smart Segmentation does.
ABSTRACT
Objective To investigate the impact of bladder filling status on the movement of uterine and on the volume of organs at risk(OAR), so as to provide theoretical basis for individualized internal target volume ( ITV) and planning target volume ( PTV) . Methods Simulation CT images for a total of 27 patients with locally advanced cervical cancer were acquired with empty bladder, 1. 0 h after bladder filling, 1. 5 h after bladder filling, respectively. The volumes of uterine corpus, cervix, OARs, CTV and PTV 1. 0 h after bladder filling were delineated. The impact of bladder filling status on the displacements of cervix and corpus, volumetric changes of rectum, small intestine, and the volumes of small intestine, bladder and rectum within PTV were analyzed. The correlation between bladder filling status and uterine displacement and volume of OAR was also investigated, as well as the volume of cervix and corpus in the PTV with empty bladder and 1. 5 h after bladder filling. Results Bladder filling status is different for individual cases. The maximum movement range of cervix and uterine body with bladder filling state was 0-3.04 cm and 0-4.31 cm respectively. The anterior displacements of corpus (F=7. 818, P<0. 05) , the volumetric changes of blander, as well the volume of bladder and small intestine in the PTV ( F=46. 197, 44. 609, 29. 546, P<0. 05 ) were significantly different between different bladder filling status. The bladder filling status was correlated with the displacements of the anterior of corpus, volumetric changes of small intestine, and the volumes of bladder and small intestine within the PTV ( r=-0. 232,-0. 298, 0. 915, -0. 336, P<0. 05) . The volumes of cervix and corpus out of the PTV were significantly different between the empty bladder and 1. 5 h after bladder filling ( t=-1. 326, -1. 559, P<0. 05 ) . Conclusions Bladder filling status was different for individual patients. The displacements of the anterior of the corpus were significantly affected by the bladder filling status. Increasing the anterior margin of PTV was recommended. The consistency of bladder filling status was critical for the precise cervical cancer IMRT.