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.

Altered cerebral white matter network topology and cognition in children with obstructive sleep apnea.

OBJECTIVES: The study aimed to explore the underlying mechanisms of OSA-related cognitive impairment by investigating the altered topology of brain white matter networks in children with OSA. METHODS: Graph theory was used to examine white matter networks' network topological properties in 46 OSA and 31 non-OSA children. All participants underwent MRI, polysomnography, and cognitive testing. The effects of the obstructive apnea-hypopnea index (OAHI) on topological properties of white matter networks and network properties on cognition were studied using hierarchical linear regression. Mediation analyses were used to explore whether white matter network properties mediated the effects of OAHI on cognition. RESULTS: Children with OSA had significantly higher assortativity than non-OSA children. Furthermore, OAHI was associated with the nodal properties of several brain regions, primarily in the frontal and temporal lobes. The relationship between OAHI and verbal comprehension index was mediated through clustering coefficients in the right temporal pole of the superior temporal gyrus. CONCLUSIONS: OSA affects the development of white matter networks in children's brains. Besides, the mediating role of white matter network properties between the OAHI and the verbal comprehension index provided neuroimaging evidence of impaired cognitive function in children with OSA.

Gut microbiota moderates multimodal brain structure-function integration and behavioral cognition in growth hormone deficient children.

Background Previous brain studies of growth hormone deficiency (GHD) often used single-mode neuroimaging, missing the complexity captured by multimodal data. Growth hormone affects gut microbiota and metabolism in GHD. However, from a gut-brain axis perspective, the relationship between abnormal GHD brain development and microbiota alterations remains unclear. The ultimate goal is to uncover the manifestations underlying gut-brain axis (GBA) abnormalities in GHD and idiopathic short stature (ISS). Methods Participants included 23 GHD and 25 ISS children. The fusion independent component analysis was applied to integrat multimodal brain datas (high resolution structural, diffusion tensor, and resting state functional MRI) covering regional homogeneity (ReHo), amplitude of low frequency fluctuations (ALFF), and White matter fractional anisotropy (FA). Gut microbiome diversity and metabolites were analyzed using 16S sequencing and proton nuclear magnetic resonance (1H-NMR). Associations between multimodal neuroimaging and cognition were assessed using moderation analysis. Results Six components (ReHo, ALFF, and FA) differed significantly between GHD and ISS patients, with three functional components linked to processing speed. GHD individuals showed higher levels of acetate in microbiota metabolism. Higher alpha diversity in GHD strengthened connections between ReHo-IC1, ReHo-IC5, ALFF-IC1, and processing speed, while increasing Agathobacter levels in ISS weakened the link between ALFF-IC1 and speech comprehension. Conclusions Our findings uncover differing brain structure and functional fusion in GHD, alongside microbiota metabolism of short-chain fatty acids. Additionally, microbiome influences connections between neuroimaging and cognition, offering insight into diverse gut-brain axis patterns in GHD and ISS, enhancing our understanding of the disease's pathophysiology and interventions.

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.

Alterations of brain gray matter volume in children with obstructive sleep apnea.

Objective: Obstructive sleep apnea (OSA) seriously affects the children's cognitive functions, but the neuroimaging mechanism of cognitive impairment is still unclear. The purpose of our study was to explore the difference in brain local gray matter volume (GMV) between children with OSA and non-OSA, and the correlation between the difference regions of brain gray matter volume and cognitive, the severity of OSA. Method: Eighty-three children aged 8-13 years were recruited in our study, 52 children were diagnosed as OSA by polysomnography, and 31 as the non-OSA. All the subjects were underwent high-resolution 3-dimensional T1-weighted magnetic resonance images. The voxel-based morphometry (VBM) was be used to analyse the local GMV. The Das-Naglieri cognitive assessment system (DN: CAS) was used to assess the subjects' cognitive. The difference of local GMV between the two groups was analyzed by two-sample T-test. The PSG variables and the scores of DN: CAS between the OSA group and non-OSA group were compared by independent samples t-tests. Pearson correlation was used to calculate the association between the difference areas of gray matter volumes in brain and DN: CAS scores, obstructive apnea/hypopnea index (OAHI, an index of the severity of OSA). Results: The gray matter volume of the right Middle Frontal Gyrus (MFG_R) in OSA children were larger than the non-OSA children, and the OSA children had lower scores of the Word Series in DN: CAS. There was negative correlation between the scores of Expressive Attention in DN: CAS and the gray matter volume of the right middle frontal gyrus, and it was no significantly correlation between OAHI and the gray matter volume of the right middle frontal gyrus. Conclusion: Our results suggest that the development of gray matter volume in frontal cortex, which associated with attention, were sensitive to the effects of OSA, provides neuroimaging evidence for cognitive impairment in children with OSA.

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.

Real-Time Image-Guided Navigation in the Management of Alveolar Cleft Repair.

Objectives: To present the use of dynamic navigation system in the repair of alveolar cleft. Patients and Participants: A total of three non-syndromic patients with unilateral alveolar cleft were involved in this study. Real-time computer-aided navigation were used to achieve restoration and reconstruction with standardized surgical technique. Methods: With the individual virtual 3-dimensional (3-D) modeling based on computed tomography (CT) data, preoperative planning and surgical simulation were carried out with the navigation system. During preoperative virtual planning, the defect volume or the quantity of graft is directly assessed at the surgical region. With the use of this system, the gingival periosteum flap incision can be tracked in real-time, and the bone graft can be navigated under the guidance of the 3-D views until it matches the preoperatively planned position. Results: Three patients with alveolar cleft were successfully performed under navigation guidance. Through the model alignment procedure, accurate matches between the actual intraoperative position and the CT images were achieved within the systematic error of 0.3 mm. The grafted bone was implanted according to the preoperative plan with the aid of instrument- and probe-based navigation. All the patients were healed well without serious complications. Conclusions: These findings suggest that image-guided surgical navigation, including preoperative planning, surgical simulation, postoperative assessment, and computer-assisted navigation was feasible and yielded good clinical outcomes. Clinical relevance: This dynamic navigation could be proved to be a valuable option for this complicated surgical procedure in the management of alveolar cleft repair.

Clinical efficacy of Hogan posterior pharyngeal flap in repairing velopharyngeal insufficiency secondary to cleft palate in older patients.

OBJECTIVES: The clinical effects and surgical procedures of Hogan posterior pharyngeal flap in the treatment of the older patients with velopharyngeal insufficiency (VPI) after cleft palate repair were investigated. METHODS: A total of 33 patients (aged 10-35 years; average of 20.4 years) with VPI secondary to cleft palate were included. They underwent Hogan posterior pharyngeal flap to improve velopharyngeal closure function. The clinical efficacy of the ope-ration was evaluated with Chinese speech clarity measurement and nasopharyngeal fiberscope (NPF), and the velopharyngeal closure was graded. The average follow-up time was 13.3 months. RESULTS: The wounds of all patients were healed by first intention, and speech assessment showed that the consonant articulation increased and the rate of hypernasality and nasal emission decreased significantly (P<0.05). NPF examination showed that the postoperative velopharyngeal closure function significantly improved, 30 cases (91%) were gradeⅠ, and 3 cases (9%) were grade Ⅱ. CONCLUSIONS: Hogan posterior pharyngeal flap for VPI secondary to cleft palate can significantly improve velopharyngeal closure.

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.

Altered Spontaneous Brain Activity in Left-Behind Children: A Resting-State Functional MRI Study.

Background: Parental migration has been associated with a higher risk of cognitive and behavioral abnormalities in left-behind children (LBC). This study aimed to explore the spontaneous brain activity in LBC and reveal the mechanisms underlying behavioral and cognitive abnormalities. Methods: Involved LBC (n = 36) and non-LBC (n = 22) underwent resting-state functional MRI (fMRI) examination and cognitive and behavioral assessment. The fMRI-based amplitude of low-frequency fluctuation (ALFF) and fractional ALFF (fALFF) were assessed to analyze the spontaneous brain activity pattern. The relationships among abnormal spontaneous brain activity, behavioral and cognitive deficits and altered family environment were assessed by partial correlation analysis. Results: Compared with non-LBC, LBC exhibited increased amplitude of low-frequency fluctuations in the right lingual gyrus (LING), while a decreased ALFF was observed in the bilateral insula and right orbital part of the middle frontal gyrus (ORBmid) (two-tailed voxel-level p < 0.01 and cluster-level p <0.05, Gaussian Random Field (GRF) correction). The fALFF in LBC were significantly increased in the left cerebellum 9 (Cbe9) and right cerebellum Crus2 (CbeCru2), while it decreased in the right hippocampus and left superior temporal gyrus (STG) (two-tailed voxel-level p < 0.01 and cluster-level p < 0.05, GRF correction). The ALFF and fALFF values in abnormal brain regions were found to be correlated with the learning ability, except for the right insula, while the fALFF values of the left STG were positively correlated with the full-scale IQ scores (p < 0.05). Moreover, the ALFF and fALFF values in all abnormal brain regions correlated with the education level of caregivers (p < 0.05). Conclusions: Our study provided empirical evidence that the lack of direct parental care during early childhood could affect brain function development involving cognition, behavior, and emotion. Our findings emphasized that intellectual and emotional cares are essential for LBC.

[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.

A novel red emission fluorescent probe for monitoring carbon monoxide in living cells and zebrafish.

Carbon monoxide (CO), a gaseous signal molecule, plays a crucial role in biological systems. With the aim of unraveling its biological functions, a novel fluorescent probe for sensing CO was rationally designed and synthesized based on a coumarin derivative fluorophore merging tetrahydroquinoxaline unit and five-membered pyrrolidine. This fluorescent probe demonstrated a large Stokes shift (Δλ = 132 nm), high quantum yield, red emission, high sensitivity and selectivity for CO with remarkable fluorescence turn-on. And the detection limit for CORM-3 is as low as 31.2 nM with the linear range of 0-30 µM. More importantly, this novel probe has been successfully applied to the fluorescence imaging of CO in HepG2 cells and zebrafish, providing a useful approach for the further understanding of the physiological and pathological roles of CO in living systems.

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.

Influence of the hypothalamus-pituitary-gonadal axis reactivation and pubertal hormones on gray matter volume in early pubertal girls.

OBJECTIVE: Puberty is a sensitive period of brain development accompany with pubertal hormones fluctuation. However, the underlying mechanisms of the impact of hypothalamus-pituitary-gonadal (HPG) axis reactivation and associated elevated pubertal hormones on brain structure are still unclear. Here, we investigated the brain structure differences between girls with and without HPG axis reactivation and the influence of pubertal hormones on these brain regions. METHODS: 126 girls aged 8-9.5 years underwent a gonadotropin-releasing hormone (GnRH) stimulation test to identify the HPG axis status and categorized into HPG+ group (n = 80) and HPG- group (n = 46). T1-weighted gradient echo three dimensional MRI was performed using a 3.0-Tesla scanner to assess the difference in GMV between the two groups. Correlation analyses were conducted to explore the relations between the brain regions showing significant GMV differences and serum hormone concentrations. RESULT: The HPG+ group showed significantly higher GMV in the bilateral lingual gyrus and lower GMV within the right orbital inferior frontal gyrus compare to the HPG - group. Furthermore, GMV in the right orbital inferior frontal gyrus was positively associated with plasma concentrations of follicle stimulating hormone (FSH) in HPG+ group. CONCLUSION: The present study suggests that the reactivated HPG axis could affects regional structural brain changes in early pubertal girls. FSH production play an important role in bilateral lingual gyrus, which are involved in vision processing, semantic processing and emotional expression.

[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.

Gray Matter Differences Between Premature Pubertal Girls With and Without the Reactivation of the Hypothalamic-Pituitary-Gonadal Axis.

The onset of puberty and related hormones exerts significant effects on brain morphometric and psychosocial development. The biological mechanisms underlying how the reactivation of the hypothalamic-pituitary-gonadal (HPG) axis and puberty-related hormonal maturation sculpts human brain architecture remain elusive. To address this question, 105 premature pubertal girls (age 8-11 years) without menstruation underwent brain structural scanning on a 3T MR system, and the luteinizing hormone releasing hormone (LHRH) stimulation test was used to identify the reactivation of the HPG axis. Among the 105 girls, 63 were positive for HPG axis reactivation (HPG+), while the others showed negative (HPG-). Cortical thickness was calculated and compared between the two groups after adjusting for age. The brain regions showing inter-group differences were then extracted and correlated with the peak value of serum hormone after the LHRH stimulation test in entire sample. Compared to HPG- girls, HPG+ girls showed reduced cortical thickness mainly in the the right precuneus, right inferior temporal gyrus, and right superior frontal gyrus, while increased cortical thickness primarily in the left superior parietal lobe and right inferior parietal lobe. Linear-regression analysis revealed negative correlations between the cortical thickness of the right inferior parietal lobe with the peak value of FSH and the right precuneus with LH and E. These findings provide evidence to support the notion that the reactivation of HPG axis and changes of hormones during the early phase of hormonal maturation exert influences on the development of gray matter.

[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.