Dosimetric comparison of advanced radiation techniques for scalp-sparing in low-grade gliomas.

BACKGROUND: Alopecia causes significant distress for patients and negatively impacts quality of life for low-grade glioma (LGG) patients. We aimed to compare and evaluate variations in dose distribution for scalp-sparing in LGG patients with proton therapy and photon therapy, namely intensity-modulated proton therapy (IMPT), intensity-modulated radiotherapy (IMRT), volumetric modulated arc therapy (VMAT), and helical tomotherapy (HT). METHODS: This retrospective study utilized a dataset comprising imaging data from 22 patients with LGG who underwent postoperative radiotherapy. Treatment plans were generated for each patient with scalp-optimized (SO) approaches and scalp-non-optimized (SNO) approaches using proton techniques and photons techniques; all plans adhered to the same dose constraint of delivering a total radiation dose of 54.04 Gy to the target volume. All treatment plans were subsequently analyzed. RESULTS: All the plans generated in this study met the dose constraints for the target volume and OARs. The SO plans resulted in reduced maximum scalp dose (Dmax), mean scalp dose (Dmean), and volume of the scalp receiving 30 Gy (V30) and 40 Gy (V40) compared with SNO plans in all radiation techniques. Among all radiation techniques, the IMPT plans exhibited superior performance compared to other plans for dose homogeneity as for SO plans. Also, IMPT showed lower values for Dmean and Dmax than all photon radiation techniques. CONCLUSION: Our study provides evidence that the SO approach is a feasible technique for reducing scalp radiation dose. However, it is imperative to conduct prospective trials to assess the benefits associated with this approach.

Assembling High-Quality Lymph Node Clinical Target Volumes for Cervical Cancer Radiotherapy Using a Deep Learning-Based Approach.

AIM: The study aimed to explore an approach for accurately assembling high-quality lymph node clinical target volumes (CTV) on CT images in cervical cancer radiotherapy with the encoder-decoder 3D network. METHODS: 216 cases of CT images treated at our center between 2017 and 2020 were included as a sample, which were divided into two cohorts, including 152 cases and 64 controls, respectively. Para-aortic lymph node, common iliac, external iliac, internal iliac, obturator, presacral, and groin nodal regions were delineated as sub-CTV manually in the cohort including 152 cases. Then, the 152 cases were randomly divided into training (96 cases), validation (36 cases), and test (20 cases) groups for the training process. Each structure was individually trained and optimized through a deep learning model. An additional 64 cases with 6 different clinical conditions were taken as examples to verify the feasibility of CTV generation based on our model. Dice similarity coefficient (DSC) and Hausdorff distance (HD) metrics were both used for quantitative evaluation. RESULTS: Comparing auto-segmentation results to ground truth, the mean DSC value/HD was 0.838/7.7mm, 0.853/4.7mm, 0.855/4.7mm, 0.844/4.7mm, 0.784/5.2mm, 0.826/4.8mm and 0.874/4.8mm for CTV_PAN, CTV_common iliac, CTV_internal iliac, CTV_external iliac, CTV_obturator, CTV_presacral, and CTV_groin, respectively. The similarity comparison results of six different clinical situations were 0.877/4.4mm, 0.879/4.6mm, 0.881/4.2mm, 0.882/4.3mm, 0.872/6.0mm, and 0.875/4.9mm for DSC value/HD, respectively. CONCLUSION: We have developed a deep learning-based approach to segmenting lymph node sub-regions automatically and assembling high-quality CTVs according to clinical needs in cervical cancer radiotherapy. This work can increase the efficiency of the process of cervical cancer detection and treatment.

Quality Assurance based on Deep Learning for Pelvic OARs Delineation in Radiotherapy.

BACKGROUND: Correct delineation of organs at risk (OARs) is an important step for radiotherapy and it is also a time-consuming process that depends on many factors. OBJECTIVE: An automatic quality assurance (QA) method based on deep learning (DL) was proposed to improve efficiency for detecting contouring errors of OARs. MATERIALS AND METHODS: A total of 180 planning CT scan sets at the pelvic site and the corresponding OARs contours from clinics were enrolled in this study. Among them, 140 cases were randomly chosen as the training datasets, 20 cases were used as the validation datasets, and the remaining 20 cases were used as the test datasets. DL-based models were trained through data curation for data cleaning based on the Dice similarity coefficient and the 95th percentile Hausdorff distance between the original contours and the predicted contours. All contouring errors could be classified into two types; minor modification required and major modification required. The pass criteria were established using Bias- Corrected and Accelerated bootstrap on 20 manually reviewed validation datasets. The performance of the QA method was evaluated with the metrics of sensitivity, specificity, the area under the receiving operator characteristic curve (AUC), and detection rate sensitivity on the 20 test datasets. RESULTS: For all OARs, segmentation results after data curation were superior to those without. The sensitivity of the QA method was greater than 0.890 and the specificity was higher than 0.975. The AUCs were 0.948, 0.966, 0.965, and 0.932 for the bladder, right femoral head, left femoral head, and rectum, respectively. Almost all major errors could be detected by the automatic QA method, and the lowest detection rate sensitivity of minor errors was 0.863 for the rectum. CONCLUSIONS: QA of OARs is an important step for the correct implementation of radiotherapy. The DL-based QA method proposed in this study showed a high potential to automatically detect contouring errors with high precision. The method can be integrated into the existing radiotherapy procedures to improve the efficiency of delineating the OARs.

CDC6 is a prognostic biomarker and correlated with immune infiltrates in glioma.

BACKGROUND: Cell division cycle 6 (CDC6) has been proven to be associated with the initiation and progression of human multiple tumors. However, it's role in glioma, which is ranked as one of the common primary malignant tumor in the central nervous system and is associated with high morbidity and mortality, is unclear. METHODS: In this study, we explored CDC6 gene expression level in pan-cancer. Furthermore, we focused on the relationships between CDC6 expression, its prognostic value, potential biological functions, and immune infiltrates in glioma patients. We also performed vitro experiments to assess the effect of CDC6 expression on proliferative, apoptotic, migrant and invasive abilities of glioma cells. RESULTS: As a result, CDC6 expression was upregulated in multiple types of cancer, including glioma. Moreover, high expression of CDC6 was significantly associated with age, IDH status, 1p/19q codeletion status, WHO grade and histological type in glioma (all p < 0.05). Meanwhile, high CDC6 expression was associated with poor overall survival (OS) in glioma patients, especially in different clinical subgroups. Furthermore, a univariate Cox analysis showed that high CDC6 expression was correlated with poor OS in glioma patients. Functional enrichment analysis indicated that CDC6 was mainly involved in pathways related to DNA transcription and cytokine activity, and Gene Set Enrichment Analysis (GSEA) revealed that MAPK pathway, P53 pathway and NF-κB pathway in cancer were differentially enriched in glioma patients with high CDC6 expression. Single-sample gene set enrichment analysis (ssGSEA) showed CDC6 expression in glioma was positively correlated with Th2 cells, Macrophages and Eosinophils, and negative correlations with plasmacytoid dendritic cells, CD8 T cells and NK CD56bright cells, suggesting its role in regulating tumor immunity. Finally, CCK8 assay, flow cytometry and transwell assays showed that silencing CDC6 could significantly inhibit proliferation, migration, invasion, and promoted apoptosis of U87 cells and U251 cells (p < 0.05). CONCLUSION: In conclusion, high CDC6 expression may serve as a promising biomarker for prognosis and correlated with immune infiltrates, presenting to be a potential immune therapy target in glioma.

[The Effects of Different Adaptive Statistical Iterative Reconstruction-V and Convolution Kernel Parameters on Auto-Segmentation Stability in CT Images].

Objective The study aims to investigate the effects of different adaptive statistical iterative reconstruction-V（ ASiR-V） and convolution kernel parameters on stability of CT auto-segmentation which is based on deep learning. Method Twenty patients who have received pelvic radiotherapy were selected and different reconstruction parameters were used to establish CT images dataset. Then structures including three soft tissue organs (bladder, bowelbag, small intestine) and five bone organs (left and right femoral head, left and right femur, pelvic) were segmented automatically by deep learning neural network. Performance was evaluated by dice similarity coefficient（ DSC） and Hausdorff distance, using filter back projection(FBP) as the reference. Results Auto-segmentation of deep learning is greatly affected by ASIR-V, but less affected by convolution kernel, especially in soft tissues. Conclusion The stability of auto-segmentation is affected by parameter selection of reconstruction algorithm. In practical application, it is necessary to find a balance between image quality and segmentation quality, or improve segmentation network to enhance the stability of auto-segmentation.

Quantitative Comparisons of Deep-learning-based and Atlas-based Auto- segmentation of the Intermediate Risk Clinical Target Volume for Nasopharyngeal Carcinoma.

BACKGROUND: Manual segment target volumes were time-consuming and inter-observer variability couldn't be avoided. With the development of computer science, auto-segmentation had the potential to solve this problem. OBJECTIVE: To evaluate the accuracy and stability of Atlas-based and deep-learning-based auto-segmentation of the intermediate risk clinical target volume, composed of CTV2 and CTVnd, for nasopharyngeal carcinoma quantitatively. METHODS AND MATERIALS: A cascade-deep-residual neural network was constructed to automatically segment CTV2 and CTVnd by deep learning method. Meanwhile, a commercially available software was used to automatically segment the same regions by Atlas-based method. The datasets included contrast computed tomography scans from 102 patients. For each patient, the two regions were manually delineated by one experienced physician. The similarity between the two auto-segmentation methods was quantitatively evaluated by Dice similarity coefficient, the 95th Hausdorff distance, volume overlap error and relative volume difference, respectively. Statistical analyses were performed using the ranked Wilcoxon test. RESULTS: The average Dice similarity coefficient (±standard deviation) given by the deep-learning- based and Atlas-based auto-segmentation were 0.84 (±0.03) and 0.74 (±0.04) for CTV2, 0.79 (±0.02) and 0.68 (±0.03) for CTVnd, respectively. For the 95th Hausdorff distance, the corresponding values were 6.30±3.55 mm and 9.34±3.39 mm for CTV2, 7.09±2.27 mm and 14.33±3.98 mm for CTVnd. Besides, volume overlap error and relative volume difference could also predict the same situations. Statistical analyses showed significant difference between the two auto-segmentation methods (p<0.01). CONCLUSION: Compared with the Atlas-based segmentation approach, the deep-learning-based segmentation method performed better both in accuracy and stability for meaningful anatomical areas other than organs at risk.

[Feasibility of Evaluating Result of Auto-segmentation of Target Volumes in Radiotherapy with Medical Consideration Index].

OBJECTIVE: To explore the feasibility of using the bidirectional local distance based medical similarity index (MSI) to evaluate automatic segmentation on medical images. METHODS: Taking the intermediate risk clinical target volume for nasopharyngeal carcinoma manually segmented by an experience radiation oncologist as region of interest, using Atlas-based and deep-learning-based methods to obtain automatic segmentation respectively, and calculated multiple MSI and Dice similarity coefficient (DSC) between manual segmentation and automatic segmentation. Then the difference between MSI and DSC was comparatively analyzed. RESULTS: DSC values for Atlas-based and deep-learning-based automatic segmentation were 0.73 and 0.84 respectively. MSI values for them varied between 0.29~0.78 and 0.44~0.91 under different inside-outside-level. CONCLUSIONS: It is feasible to use MSI to evaluate the results of automatic segmentation. By setting the penalty coefficient, it can reflect phenomena such as under-delineation and over-delineation, and improve the sensitivity of medical image contour similarity evaluation.

Dosimetric evaluation of synthetic CT image generated using a neural network for MR-only brain radiotherapy.

PURPOSE AND BACKGROUND: The magnetic resonance (MR)-only radiotherapy workflow is urged by the increasing use of MR image for the identification and delineation of tumors, while a fast generation of synthetic computer tomography (sCT) image from MR image for dose calculation remains one of the key challenges to the workflow. This study aimed to develop a neural network to generate the sCT in brain site and evaluate the dosimetry accuracy. MATERIALS AND METHODS: A generative adversarial network (GAN) was developed to translate T1-weighted MRI to sCT. First, the "U-net" shaped encoder-decoder network with some image translation-specific modifications was trained to generate sCT, then the discriminator network was adversarially trained to distinguish between synthetic and real CT images. We enrolled 37 brain cancer patients acquiring both CT and MRI for treatment position simulation. Twenty-seven pairs of 2D T1-weighted MR images and rigidly registered CT image were used to train the GAN model, and the remaining 10 pairs were used to evaluate the model performance through the metric of mean absolute error. Furthermore, the clinical Volume Modulated Arc Therapy plan was calculated on both sCT and real CT, followed by gamma analysis and comparison of dose-volume histogram. RESULTS: On average, only 15 s were needed to generate one sCT from one T1-weighted MRI. The mean absolute error between synthetic and real CT was 60.52 ± 13.32 Housefield Unit over 5-fold cross validation. For dose distribution on sCT and CT, the average pass rates of gamma analysis using the 3%/3 mm and 2%/2 mm criteria were 99.76% and 97.25% over testing patients, respectively. For parameters of dose-volume histogram for both target and organs at risk, no significant differences were found between both plans. CONCLUSION: The GAN model can generate synthetic CT from one single MRI sequence within seconds, and a state-of-art accuracy of CT number and dosimetry was achieved.

Evaluation on Auto-segmentation of the Clinical Target Volume (CTV) for Graves' Ophthalmopathy (GO) with a Fully Convolutional Network (FCN) on CT Images.

CDATA[Purpose: The aim of this study is to evaluate the accuracy and dosimetric effects for auto- segmentation of the CTV for GO in CT images based on FCN. METHODS: An FCN-8s network architecture for auto-segmentation was built based on Caffe. CT images of 121 patients with GO who have received radiotherapy at the West China Hospital of Sichuan University were randomly selected for training and testing. Two methods were used to segment the CTV of GO: treating the two-part CTV as a whole anatomical region or considering the two parts of CTV as two independent regions. Dice Similarity Coefficient (DSC) and Hausdorff Distance (HD) were used as evaluation criteria. The auto-segmented contours were imported into the original treatment plan to analyse the dosimetric characteristics. RESULTS: The similarity comparison between manual contours and auto-segmental contours showed an average DSC value of up to 0.83. The max HD values for segmenting two parts of CTV separately was a little bit smaller than treating CTV with one label (8.23±2.80 vs. 9.03±2.78). The dosimetric comparison between manual contours and auto-segmental contours showed there was a significant difference (p<0.05) with the lack of dose for auto-segmental CTV. CONCLUSION: Based on deep learning architecture, the automatic segmentation model for small target areas can carry out auto contouring tasks well. Treating separate parts of one target as different anatomic regions can help to improve the auto-contouring quality. The dosimetric evaluation can provide us with different perspectives for further exploration of automatic sketching tools.

[Application and Development Trend of Medical Image Automatic Segmentation Technology in Radiation Therapy].

The development of medical image segmentation technology has been briefly reviewed. The applications of auto-segmentation of organs at risk and target volumes based on Atlas and deep learning in the field of radiotherapy have been introduced in detail, respectively. Then the development direction and product model for general automatic sketching tools or systems based on solid clinical data are discussed.

[Using stacked neural network to improve the auto-segmentation accuracy of Graves' ophthalmopathy target volumes for radiotherapy].

Compared with the previous automatic segmentation neural network for the target area which considered the target area as an independent area, a stacked neural network which uses the position and shape information of the organs around the target area to regulate the shape and position of the target area through the superposition of multiple networks and fusion of spatial position information to improve the segmentation accuracy on medical images was proposed in this paper. Taking the Graves' ophthalmopathy disease as an example, the left and right radiotherapy target areas were segmented by the stacked neural network based on the fully convolutional neural network. The volume Dice similarity coefficient (DSC) and bidirectional Hausdorff distance (HD) were calculated based on the target area manually drawn by the doctor. Compared with the full convolutional neural network, the stacked neural network segmentation results can increase the volume DSC on the left and right sides by 1.7% and 3.4% respectively, while the two-way HD on the left and right sides decrease by 0.6. The results show that the stacked neural network improves the degree of coincidence between the automatic segmentation result and the doctor's delineation of the target area, while reducing the segmentation error of small areas. The stacked neural network can effectively improve the accuracy of the automatic delineation of the radiotherapy target area of Graves' ophthalmopathy.

[Modified palatoplasty for children of Pierre Robin sequence with cleft palate：clinical analysis of 12 consecutive cases].

PURPOSE: To explore a safe, effective and functional surgical treatment for children of Pierre Robin sequence (PRS) with cleft palate. METHODS: Twelve children of PRS with cleft palate underwent mandibular distraction osteogenesis before cleft palate surgery in order to correct severe hypoxia. A modified palatoplasty was then performed, the palatal flaps on both sides were not elevated in the anterior portions to prevent soft palate backward moving, and the levator veli palatini was repositioned simultaneously. RESULTS: All children achieved velopharyngeal closure without dyspnea after follow-up of 10-12 months. CONCLUSIONS: Measures should be taken to avoid backward movement of the soft palate, which may result in dyspnea of children with PRS in palatoplasty.

Altered Brain Structure and Functional Connectivity Associated with Pubertal Hormones in Girls with Precocious Puberty.

Pubertal hormones play an important role in brain and psychosocial development. However, the role of abnormal HPG axis states in altering brain function and structure remains unclear. The present study is aimed at determining whether there were significant differences in gray matter volume (GMV) and resting state (RS) functional connectivity (FC) patterns in girls with idiopathic central precocious puberty (CPP) and peripheral precocious puberty (PPP). We further explored the correlation between these differences and serum pubertal hormone levels. To assess this, we recruited 29 idiopathic CPP girls and 38 age-matched PPP girls. A gonadotropin-releasing hormone (GnRH) stimulation test was performed, and pubertal hormone levels (including luteinizing hormone (LH), follicle-stimulating hormone (FSH), estradiol (E2), prolactin, and cortisol) were assessed. All subjects underwent multimodal magnetic resonance imaging of brain structure and function. Voxel-based morphometry (VBM) analysis was paired with seed-to-voxel whole-brain RS-FC analysis to calculate the GMV and RS-FC in idiopathic CPP and PPP girls. Correlation analyses were used to assess the effects of pubertal hormones on brain regions with structural and functional differences between the groups. We found that girls with CPP exhibited decreased GMV in the left insula and left fusiform gyrus, while connectivity between the left and right insula and the right middle frontal gyrus (MFG), as well as the left fusiform gyrus and right amygdala, was reduced in girls with CPP. Furthermore, the GMV of the left insula and peak FSH levels were negatively correlated while higher basal and peak E2 levels were associated with increased bilateral insula RS-FC. These findings suggest that premature activation of the HPG axis and pubertal hormone fluctuations alter brain structure and function involved in the cognitive and emotional process in early childhood. These findings provide vital insights into the early pathophysiology of idiopathic CPP.

Open Rhinoplasty Using Concealing Incisions for Mild Bifid Nose With Unilateral Mini-Microform Cleft Lip.

Bifid nose, an indicator of Tessier No.0, is a rare congenital malformation. Because of its rarity, few cases were reported and the optimal surgical procedure and the best time for surgery have not been widely acknowledged. In this brief report, a 9-year-old girl with mild bifid nose and unilateral mini-microform cleft lip, and its surgical management, is presented. We focused our attention on modifying the shape of the nose through open rhinoplasty without excising the surplus skin on the nasal dorsum and achieved good results.

Dosimetric effect of beam arrangement for intensity-modulated radiation therapy in the treatment of upper thoracic esophageal carcinoma.

To evaluate the lung sparing in intensity-modulated radiation therapy (IMRT) for patients with upper thoracic esophageal tumors extending inferiorly to the thorax by different beam arrangement. Overall, 15 patient cases with cancer of upper thoracic esophagus were selected for a retrospective treatment-planning study. Intensity-modulated radiation therapy plans using 4, 5, and 7 beams (4B, 5B, and 7B) were developed for each patient by direct machine parameter optimization (DMPO). All plans were evaluated with respect to dose volumes to irradiated targets and normal structures, with statistical comparisons made between 4B with 5B and 7B intensity-modulated radiation therapy plans. Differences among plans were evaluated using a two-tailed Friedman test at a statistical significance of p < 0.05. The maximum dose, average dose, and the conformity index (CI) of planning target volume 1 (PTV1) were similar for 3 plans for each case. No significant difference of coverage for planning target volume 1 and maximum dose for spinal cords were observed among 3 plans in present study (p > 0.05). The average V5, V13, V20, mean lung dose, and generalized equivalent uniform dose (gEUD) for the total lung were significantly lower in 4B-plans than those data in 5B-plans and 7B-plans (p < 0.01). Although the average V30 for the total lung were significantly higher in 4B-plans than those in 5B-plans and 7B-plans (p < 0.05). In addition, when comparing with the 4B-plans, the conformity/heterogeneity index of the 5B- and 7B-plans were significantly superior (p < 0.05). The 4B-intensity-modulated radiation therapy plan has advantage to address the specialized problem of lung sparing to low- and intermediate-dose exposure in the thorax when dealing with relative long tumors extended inferiorly to the thoracic esophagus for upper esophageal carcinoma with the cost for less conformity. Studies are needed to compare the superiority of volumetric modulated arc therapy with intensity-modulated radiation therapy technique.

[From microdosimetry to nanodosimetry--the link between radiobiology and radiation physics].

The link between micro- and macro-parameters for radiation interactions that take place in living biological systems is described in this paper. Meanwhile recent progress and development in microdosimetry and nanodosimetry are introduced, including the methods to measure and calculate these micro- or nano-parameters. The relationship between radiobiology and physical quantities in microdosimetry and nanodosimetry was presented. Both the current problems on their applications in radiation protection and radiotherapy and the future development direction are proposed.

[The specification of parameters driven by measurement data in the construction of virtual sources in Monte Carlo simulation].

Dose calculation algorithms based on the Monte Carlo (MC) method are widely regarded as the most accurate tool available in radiotherapy. The MC simulation in radiotherapy has been split into two parts, the radiation source simulation and patient simulation. In this research, a virtual source for simulating the linear accelerator head was constructed with measurement-driven models. The dependence between the calculation accuracy and the specification of various parameters was studied by comparison between the measurement data and calculation results. It has been shown that the dose profile obtained by MC simulation can be consistent with measurement data, suggesting that the compound effect of primary photons and secondary photons are considered with appropriate parameter specification. The requirement of modeling for MC simulation can be met in clinical conditions.

Ionothermal synthesis of layered zirconium phosphates and their tribological properties in mineral oil.

In an attempt to synthesize novel zirconium phosphate materials, a series of syntheses have been performed in a deep eutectic solvent (DES), composed of tetrapropylammonium bromide (TPABr) and oxalic acid. As a result, this DES does not act as a template provider in reaction probably owing to the steric effects of the longer chains of the TPA cation, and only the α-Zr(HPO(4))(2)·H(2)O (α-ZrP) phase has been achieved. However, after organic amine was added to the initial reaction mixture in a normal way, the additives did act as a template to induce the zirconium phosphate framework. For example, with 1,4-dimethylpiperazine as an additive, a novel layered compound, [C(6)H(16)N(2)](0.5)Zr(H(0.5)PO(4))(2)·H(2)O (denoted as ZrPO(4)-DES8) was obtained. Its structure was determined from single-crystal X-ray diffraction (XRD) data and consists of zirconium phosphate layers with the protonated 1,4-dimethylpiperazine and water molecules in between. Interestingly, the two layered materials as additives in a liquid lubricant exhibit excellent friction behavior with higher load-carrying and antiwear capacities in comparison to typical lubricant additives such as MoS(2) and graphite, increase the P(B) value of the base oil by 27.2% and 8.5%, and decrease the wear scar diameter of the base oil by 43% and 36%, respectively. Scanning electron microscopy, XRD, and energy-dispersive X-ray spectrometry are used to investigate the lubricant behavior of those materials.

Dosimetric comparison using different multileaf collimeters in intensity-modulated radiotherapy for upper thoracic esophageal cancer.

PURPOSE: To study the impacts of multileaf collimators (MLC) width [standard MLC width of 10 mm (sMLC) and micro-MLC width of 4 mm (mMLC)] in the intensity-modulated radiotherapy (IMRT) planning for the upper thoracic esophageal cancer (UTEC). METHODS AND MATERIALS: 10 patients with UTEC were retrospectively planned with the sMLC and the mMLC. The monitor unites (MUs) and dose volume histogram-based parameters [conformity index (CI) and homogeneous index (HI)] were compared between the IMRT plans with sMLC and with mMLC. RESULTS: The IMRT plans with the mMLC were more efficient (average MUs: 703.1 +/- 68.3) than plans with the sMLC (average MUs: 833.4 +/- 73.8) (p < 0.05). Also, compared to plans with the sMLC, the plans with the mMLC showed advantages in dose coverage of the planning gross tumor volume (Pgtv) (CI 0.706 +/- 0.056/HI 1.093 +/- 0.021) and the planning target volume (PTV) (CI 0.707 +/- 0.029/HI 1.315 +/- 0.013) (p < 0.05). In addition, the significant dose sparing in the D5 (3260.3 +/- 374.0 vs 3404.5 +/- 374.4)/gEUD (1815.1 +/- 281.7 vs 1849.2 +/- 297.6) of the spinal cord, the V10 (33.2 +/- 6.5 vs 34.0 +/- 6.7), V20 (16.0 +/- 4.6 vs 16.6 +/- 4.7), MLD (866.2 +/- 174.1 vs 887.9 +/- 172.1) and gEUD (938.6 +/- 175.2 vs 956.8 +/- 171.0) of the lungs were observed in the plans with the mMLC, respectively (p < 0.05). CONCLUSIONS: Comparing to the sMLC, the mMLC not only demonstrated higher efficiencies and more optimal target coverage, but also considerably improved the dose sparing of OARs in the IMRT planning for UTEC.

[Optimization of the craniospinal radiation technique by synthesizing multiple advanced radiation techniques].

Craniospinal radiation is one of essential components in the treatment flow for a number of central nervous system malignancies. Meticulous attention to technique and dosimetry is required to produce optimum tumor control. In this paper, an optimized treatment regimen was proposed based on multiple techniques. The CT images for a 17-year-old male patient in need of craniospinal radiation were acquired for 3D conformal treatment planning. The split-beam technique, the extended penumbra fields matching technique, and the multiple leaf collimator segments and extended SSD technique were synthesized in the treatment regimen so as to work out an optimized treatment plan. The added few segments improved the dose homogeneity in spinal cord. The maximal point dose was decreased from 124% to 108% of the prescribed dose in it. Comparative study on the anthropomorphic phantom showed that the data collected by thermoluminescent detectors and the data obtained by calculation were basically coincident. These results suggest that the proposed technique be clinically acceptable.