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Background The objective of this study was to investigate the treatment efficacy of stereotactic body radiotherapy (SBRT) and evaluate the influence of radiation dose on local control and survival in patients with abdominal lymph node metastases (LNM) from hepatocellular carcinoma (HCC). Patients and methods Between 2010 and 2020, data of 148 patients with HCC with abdominal LNM, including 114 who underwent SBRT and 34 who received conventional fractionation radiation therapy (CFRT), were collected. A total radiation dose of 28-60 Gy was delivered in 3-30 fractions, with a median biologic effective dose (BED) of 60 Gy (range, 39-105 Gy). Freedom from local progression (FFLP) and overall survival (OS) rates were analyzed. Results With a median follow-up of 13.6 months (range, 0.4-96.0 months), the 2-year FFLP and OS rates of the entire cohort were 70.6% and 49.7%, respectively. Median OS of the SBRT group was longer than the CFRT group (29.7 vs. 9.9 months, P =.007). A dose-response relationship was observed between local control and BED in either the entire cohort or the SBRT subgroup. Patients who received SBRT with a BED ≥60 Gy had significantly higher 2-year FFLP and OS rates than those who received a BED <60 Gy (80.1% vs. 63.4%, P =. 004; 68.3% vs. 33.0%, P <. 001). On multivariate analysis, BED was an independent prognostic factor for both FFLP and OS. Conclusions SBRT achieved satisfactory local control and survival with feasible toxicities in patients with HCC with abdominal LNM. Moreover, the findings of this large series suggest a dose-response relationship between local control and BED. [ABSTRACT FROM AUTHOR]

Purpose: The Monte Carlo (MC) algorithm has been widely accepted as the most accurate algorithm for dosimetric calculations under various conditions in radiotherapy. However, the calculation time remains an important obstacle hindering the routine use of MC in clinical settings. In this study, full MC three‐dimensional dose distributions were obtained with the inputs of the total energy release per unit mass (TERMA) distributions and the electron density (ED) distributions using a convolutional neural network (CNN). A new Dose‐mixup data augmentation routine and training strategy are proposed and applied in the training process. Attempts were made to reduce the calculation time while ensuring that the calculation accuracy is comparable to that of the MC. Methods: Datasets were generated via the MC with random rectangular field sizes, random iso‐centers, and random gantry angles for head and neck computed tomography (CT) images with Mohan 6‐MV spectrum photon beams. 1500 samples were generated for the training set, and 150 samples were generated for the validation set. The T‐MC Net model was obtained with the Dose‐mixup data augmentation routine. The new CTs were used to test the performance of the model in the rectangular fields and the intensity‐modulated radiation therapy (IMRT) fields. The mean ± 95% confidence interval of gamma pass rates were calculated. Results: For 150 rectangular field test samples, the 1%/2 mm, 2%/2 mm, and 3%/2 mm criteria gamma pass rates were 90.11% ± 0.65%, 97.65% ± 0.31%, and 99.16% ± 0.19%, respectively. For the 100 IMRT field test samples, the 1%/2 mm, 2%/2 mm, and 3%/2 mm criteria gamma pass rates were 96.48% ± 0.28%, 99.14% ± 0.10%, and 99.63% ± 0.06%, respectively. For the 7‐fields IMRT plan, the 1%/2 mm, 2%/2 mm, and 3%/2 mm criteria gamma pass rates were 97.06%, 99.10%, and 99.52%, respectively. For the 9‐fields IMRT plan, the 1%/2 mm, 2%/2 mm, and 3%/2 mm criteria gamma pass rates were 98.16%, 99.61%, and 99.89%, respectively. Conclusions: The feasibility of calculating dose distribution using a CNN with the TERMA three‐dimensional distribution and ED distribution was established. The dosimetric results were comparable to those of the full MC. The accuracy and speed of the proposed approach make it a potential solution for full MC in radiotherapy. This method may be used as an acceleration engine for the dose algorithm and shows great potential for cases where fast dose calculations are needed. [ABSTRACT FROM AUTHOR]

Background: The objective of this study was to investigate the relationship between clinical characteristics, as well as dosimetric parameters, and the risk of treatment‐related lymphopenia in esophageal squamous cell carcinoma (ESCC) treated with definitive chemoradiotherapy (CRT). Materials and Methods: Clinical characteristics and dosimetric parameters were collected from 436 patients with ESCC who received definitive CRT from 2010 through 2017. Absolute lymphocyte counts (ALCs) were obtained before, during, and 1 month after CRT. Grade 4 (G4) lymphopenia was defined as ALC <0.2 × 109/L during CRT. Logistic regression analysis was used to evaluate the effect of each factor on predicting G4 lymphopenia. The relationship between lymphopenia and overall survival (OS) was examined, and a nomogram was developed to predict OS. Results: G4 lymphopenia was observed in 103 patients (23.6%) during CRT. Multivariate analysis indicated that planning target volume (PTV), lung V10, heart V10, performance status, and pretreatment lymphopenia were significant risk factors for G4 lymphopenia. Patients with G4 lymphopenia had significantly worse survival than those without. Based on multivariate analysis, clinical TNM stage, radiotherapy modality, pretreatment ALC, and G4 lymphopenia were predictive of OS and were incorporated into the nomogram, yielding a concordance index of 0.71. Conclusions: G4 lymphopenia during definitive CRT was associated with larger PTVs, higher lung V10 and heart V10, and worse survival. Implications for Practice: The purpose of this study was to investigate the relationship between clinical characteristics, as well as dosimetric parameters, and the risk of treatment‐related lymphopenia in 436 patients with esophageal squamous cell carcinoma who received definitive chemoradiotherapy. Grade 4 (G4) lymphopenia was observed in 23.6% of patients during radiotherapy. G4 lymphopenia was associated with larger planning target volumes, higher lung V10 and heart V10, and worse survival. Then, a nomogram was built based on multivariate analysis, yielding excellent performance to predict overall survival. Prospective studies are needed to investigate potential approaches for mitigating severe lymphopenia, which may ultimately convert into survival benefits. This study explored the relationship between clinical characteristics, as well as dose‐volume histogram parameters, and the risk of treatment‐related lymphopenia in patients with esophageal squamous cell carcinoma treated with definitive chemoradiotherapy. This article evaluates whether lymphopenia is associated with patient outcomes and describes a nomogram built to predict survival. [ABSTRACT FROM AUTHOR]

Background: In this study, a deep convolutional neural network (CNN)-based automatic segmentation technique was applied to multiple organs at risk (OARs) depicted in computed tomography (CT) images of lung cancer patients, and the results were compared with those generated through atlas-based automatic segmentation. Materials and methods: An encoder-decoder U-Net neural network was produced. The trained deep CNN performed the automatic segmentation of CT images for 36 cases of lung cancer. The Dice similarity coefficient (DSC), the mean surface distance (MSD) and the 95% Hausdorff distance (95% HD) were calculated, with manual segmentation results used as the standard, and were compared with the results obtained through atlas-based segmentation. Results: For the heart, lungs and liver, both the deep CNN-based and atlas-based techniques performed satisfactorily (average values: 0.87 < DSC < 0.95, 1.8 mm < MSD < 3.8 mm, 7.9 mm < 95% HD <11 mm). For the spinal cord and the oesophagus, the two methods had statistically significant differences. For the atlas-based technique, the average values were 0.54 < DSC < 0.71, 2.6 mm < MSD < 3.1 mm and 9.4 mm < 95% HD <12 mm. For the deep CNN-based technique, the average values were 0.71 < DSC < 0.79, 1.2 mm < MSD <2.2 mm and 4.0 mm < 95% HD < 7.9 mm. Conclusion: Our results showed that automatic segmentation based on a deep convolutional neural network enabled us to complete automatic segmentation tasks rapidly. Deep convolutional neural networks can be satisfactorily adapted to segment OARs during radiation treatment planning for lung cancer patients. [ABSTRACT FROM AUTHOR]

The aim of the work was to evaluate the absorbed dose ratio factor fmd of an Al2O3 dosemeter to water in photon radiotherapy beams using cavity theory. Burlin theory was used for calculating of this ratio. The effective mass attenuation coefficient β was obtained by comparing Monte Carlo simulations in monoenergetic photon beams. The evaluations of the absorbed dose ratio factor fmd were studied for Al2O3 dosemeters of different sizes, which were placed at various depths of the water phantom in different radiation field sizes of Mohan's 6, 10 and 15-MV X-rays. Beyond the build-up region, the variation of fmd increases by 0.25 % as the depth increases from 4 to 10 cm. The maximum variation due to different dosemeter sizes is 8.3 %. The difference in the fmd due to different radiation field sizes is 1.5 %. The effect of the dosemeter size cannot be neglected. The difference in the fmd due to the radiation field sizes of different beams would increase as the dosemeter size increases. [ABSTRACT FROM AUTHOR]

Presents an abstract of the article 'The Microstructure of Permanent Magnetic Material Nd[sub15.8](Fe[sub1-x]Al[subx])[sub78.2]B[sub6],' by Rongli Gui and JinHan Zhu.

Introduction: Generalized anxiety disorder (GAD) frequently emerges during childhood or adolescence, yet, few studies have examined functional connectivity differences in youth GAD. Functional MRI studies of adult GAD have implicated multiple brain regions; however, frequent examination of individual brain seed regions and/or networks has limited a holistic view of GAD-associated differences. The current study therefore used resting-state fMRI data from the Adolescent Brain Cognitive Development study to investigate connectivity in youth with GAD across multiple cortical networks and subcortical regions implicated in adult GAD, considering diagnosis changes across two assessment periods., Methods: Within- and between-network connectivity in 164 youth with GAD and 3158 healthy controls for 6 cortical networks and 6 subcortical regions was assessed using linear mixed effect models. Changes in GAD-associated connectivity between baseline and 2-year follow-up were then compared for subjects with: continuous GAD, GAD at baseline and not follow-up (GAD-remitters), GAD at follow-up and not baseline (GAD-converters), and controls., Results: Youth with GAD showed greater within-ventral attention network (VAN) connectivity, and hyperconnectivity between the amygdala and cingulo-opercular network, and between striatal regions and the cingulo-opercular, default mode, and salience networks (FDR p<0.05). Within-VAN connectivity decreased for GAD-remitters between baseline and follow-up. Sensitivity analyses revealed that these hyperconnectivity patterns were not observed in major depressive disorder (n=19), separation anxiety (n=33), or social anxiety disorder (n=111) without GAD., Discussion: Results indicate that GAD in childhood and adolescence is associated with altered subcortical to cortical network connectivity, and that within-VAN hyperconnectivity, in particular, is associated with clinically-significant GAD-specific symptoms., (Copyright © 2025. Published by Elsevier Inc.)

Background: Quality control (QC) in radiotherapy planning is crucial for ensuring treatment efficacy and patient safety. Traditionally, QC relies on standard indicators and subjective assessments, which may lead to inconsistencies., Purpose: This study aims to develop a novel peer review method for personalized QC in radiotherapy planning, which is based on patient anatomical information, and utilizes deep learning dose prediction and a statistical model., Methods: A UNet model was trained on 139 nasopharyngeal carcinoma patients to predict 3D dose distribution, with plans divided into 95 for training, 20 for validation, and 24 for testing. For the clinical evaluation (24 items in total) of organs at risk (OAR), the QC interval (qualified, acceptable, or unqualified) for these items was set according to the model accuracy. Peer review was performed on another 29 clinical treatment plans, the items identified by the model as requiring optimization and improvement were optimized, and the effectiveness of the peer review method was tested., Results: The predicted mean voxel-based dose difference was 0.29 ± 0.13 Gy. For most evaluation items, the model prediction results were comparable to the planned results. Peer review results suggested that 66% of the plans were acceptable or unqualified. After optimization, 100% of the acceptable plans and 47% of the unqualified plans became qualified, and 20% of the unqualified plans became acceptable., Conclusions: A deep learning dose prediction model based on patient information can be used to develop personalized QC in radiotherapy planning and can help improve the quality of radiotherapy plans., (© 2025 The Author(s). Medical Physics published by Wiley Periodicals LLC on behalf of American Association of Physicists in Medicine.)

Introduction: Precision radiotherapy relies on accurate segmentation of tumor targets and organs at risk (OARs). Clinicians manually review automatically delineated structures on a case-by-case basis, a time-consuming process dependent on reviewer experience and alertness. This study proposes a general process for automated threshold generation for structural evaluation indicators and patient-specific quality assurance (QA) for automated segmentation of nasopharyngeal carcinoma (NPC)., Methods: The patient-specific QA process for automated segmentation involves determining the confidence limit and error structure highlight stage. Three expert physicians segmented 17 OARs using computed tomography images of NPC and compared them using the Dice similarity coefficient, the maximum Hausdorff distance, and the mean distance to agreement. For each OAR, the 95% confidence interval was calculated as the confidence limit for each indicator. If two or more evaluation indicators (N2) or one or more evaluation indicators (N1) exceeded the confidence limits, the structure segmentation result was considered abnormal. The quantitative performances of these two methods were compared with those obtained by artificially introducing small/medium and serious errors., Results: The sensitivity, specificity, balanced accuracy, and F-score values for N2 were 0.944 ± 0.052, 0.827 ± 0.149, 0.886 ± 0.076, and 0.936 ± 0.045, respectively, whereas those for N1 were 0.955 ± 0.045, 0.788 ± 0.189, 0.878 ± 0.096, and 0.948 ± 0.035, respectively. N2 and N1 had small/medium error detection rates of 97.67 ± 0.04% and 98.67 ± 0.04%, respectively, with a serious error detection rate of 100%., Conclusion: The proposed automated patient-specific QA process effectively detected segmentation abnormalities, particularly serious errors. These are crucial for enhancing review efficiency and automated segmentation, and for improving physician confidence in automated segmentation., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Schizophrenia spectrum disorders (SSDs) are characterized by substantial clinical and genetic heterogeneity. Multiple recurrent copy number variants (CNVs) increase risk for SSDs; however, how known risk CNVs and broader genome-wide CNVs influence clinical variability is unclear. The current study examined associations between borderline intellectual functioning or childhood-onset psychosis, known risk CNVs, and burden of deletions affecting genes in 18 previously validated neurodevelopmental gene-sets in 618 SSD individuals. CNV associations were assessed for replication in 235 SSD relatives and 583 controls, and 9,930 youth from the Adolescent Brain Cognitive Development (ABCD) Study. Known SSD- and neurodevelopmental disorder (NDD)-risk CNVs were associated with borderline intellectual functioning in SSD cases (odds ratios (OR) = 7.09 and 4.57, respectively); NDD-risk deletions were nominally associated with childhood-onset psychosis (OR = 4.34). Furthermore, deletion of genes involved in regulating gene expression during fetal brain development was associated with borderline intellectual functioning across SSD cases and non-cases (OR = 2.58), with partial replication in the ABCD cohort. Exploratory analyses of cortical morphology showed associations between fetal gene regulatory gene deletions and altered gray matter volume and cortical thickness across cohorts. Results highlight contributions of known risk CNVs to phenotypic variability in SSD and the utility of a neurodevelopmental framework for identifying mechanisms that influence phenotypic variability in SSDs, as well as the broader population, with implications for personalized medicine approaches to care.

Objective: To compare the accuracy of different methods for image registration in image-guided adaptive brachytherapy (IGABT) for cervical cancer., Methods: The last treatment planning CT images (CT1) and the first treatment planning CT images (CT2) were acquired from 15 patients with cervical cancer and registered with different match image qualities (retained/removed catheter source in images) and different match regions [target only (S Group)/ interested organ structure (M Group)/body (L Group)] in Velocity3.2 software. The dice similarity coefficient (DSC) between the clinical target volumes (CTV) of the CT1 and CT2 images (CTVCT1 and CTVCT2, respectively) and between the organs-at-risk (OAR) of the two imaging datasets (OARCT1 and OARCT2, respectively) were used to evaluate the image registration accuracy., Results: The auto-segmentation volume of the catheter source using Velocity software based on the CT threshold was the closest to the actual volume within the CT value range of 1700-1800 HU. In the retained group, the DSC for the OARs of was better than or equal to that of the removed group, and the DSC value of the rectum was significantly improved ( P &lt; 0.05). For comparison of different match regions, the high-risk target volume (HRCTV) and the low-risk target volume (IRCTV) had the best precision for registration of the target area, which was significantly greater than that of M group and L group ( P &lt; 0.05). The M group had better registration accuracy of the target area and the best accuracy for the OARs. The DSC values of the bladder and rectum were significantly better than those of the other two groups ( P &lt; 0.05)., Conclusions: The CT value range of 1700-1800 HU is optimal for automatic image segmentation using Velocity software. Automatic segmentation and shielding the volume of the catheter source can improve the image quality. We recommend the use of interested organ structures regions for image registration in image-guided adaptive brachytherapy for cervical cancer.