Cardiorespiratory motion characteristics and their dosimetric impact on cardiac stereotactic body radiotherapy.

BACKGROUND: Cardiac stereotactic body radiotherapy (CSBRT) is an emerging and promising noninvasive technique for treating refractory arrhythmias utilizing highly precise, single or limited-fraction high-dose irradiations. This method promises to revolutionize the treatment of cardiac conditions by delivering targeted therapy with minimal exposure to surrounding healthy tissues. However, the dynamic nature of cardiorespiratory motion poses significant challenges to the precise delivery of dose in CSBRT, introducing potential variabilities that can impact treatment efficacy. The complexities of the influence of cardiorespiratory motion on dose distribution are compounded by interplay and blurring effects, introducing additional layers of dose uncertainty. These effects, critical to the understanding and improvement of the accuracy of CSBRT, remain unexplored, presenting a gap in current clinical literature. PURPOSE: To investigate the cardiorespiratory motion characteristics in arrhythmia patients and the dosimetric impact of interplay and blurring effects induced by cardiorespiratory motion on CSBRT plan quality. METHODS: The position and volume variations in the substrate target and cardiac substructures were evaluated in 12 arrhythmia patients using displacement maximum (DMX) and volume metrics. Moreover, a four-dimensional (4D) dose reconstruction approach was employed to examine the dose uncertainty of the cardiorespiratory motion. RESULTS: Cardiac pulsation induced lower DMX than respiratory motion but increased the coefficient of variation and relative range in cardiac substructure volumes. The mean DMX of the substrate target was 0.52 cm (range: 0.26-0.80 cm) for cardiac pulsation and 0.82 cm (range: 0.32-2.05 cm) for respiratory motion. The mean DMX of the cardiac structure ranged from 0.15 to 1.56 cm during cardiac pulsation and from 0.35 to 1.89 cm during respiratory motion. Cardiac pulsation resulted in an average deviation of -0.73% (range: -4.01%-4.47%) in V25 between the 3D and 4D doses. The mean deviations in the homogeneity index (HI) and gradient index (GI) were 1.70% (range: -3.10%-4.36%) and 0.03 (range: -0.14-0.11), respectively. For cardiac substructures, the deviations in D50 due to cardiac pulsation ranged from -1.88% to 1.44%, whereas the deviations in Dmax ranged from -2.96% to 0.88% of the prescription dose. By contrast, the respiratory motion led to a mean deviation of -1.50% (range: -10.73%-4.23%) in V25. The mean deviations in HI and GI due to respiratory motion were 4.43% (range: -3.89%-13.98%) and 0.18 (range: -0.01-0.47) (p < 0.05), respectively. Furthermore, the deviations in D50 and Dmax in cardiac substructures for the respiratory motion ranged from -0.28% to 4.24% and -4.12% to 1.16%, respectively. CONCLUSIONS: Cardiorespiratory motion characteristics vary among patients, with the respiratory motion being more significant. The intricate cardiorespiratory motion characteristics and CSBRT plan complexity can induce substantial dose uncertainty. Therefore, assessing individual motion characteristics and 4D dose reconstruction techniques is critical for implementing CSBRT without compromising efficacy and safety.

Causal analysis of radiotherapy safety incidents based on a hybrid model of HFACS and Bayesian network.

Objective: This research investigates the role of human factors of all hierarchical levels in radiotherapy safety incidents and examines their interconnections. Methods: Utilizing the human factor analysis and classification system (HFACS) and Bayesian network (BN) methodologies, we created a BN-HFACS model to comprehensively analyze human factors, integrating the hierarchical structure. We examined 81 radiotherapy incidents from the radiation oncology incident learning system (RO-ILS), conducting a qualitative analysis using HFACS. Subsequently, parametric learning was applied to the derived data, and the prior probabilities of human factors were calculated at each BN-HFACS model level. Finally, a sensitivity analysis was conducted to identify the human factors with the greatest influence on unsafe acts. Results: The majority of safety incidents reported on RO-ILS were traced back to the treatment planning phase, with skill errors and habitual violations being the primary unsafe acts causing these incidents. The sensitivity analysis highlighted that the condition of the operators, personnel factors, and environmental factors significantly influenced the occurrence of incidents. Additionally, it underscored the importance of organizational climate and organizational process in triggering unsafe acts. Conclusion: Our findings suggest a strong association between upper-level human factors and unsafe acts among radiotherapy incidents in RO-ILS. To enhance radiation therapy safety and reduce incidents, interventions targeting these key factors are recommended.

Composite hydrogels with antioxidant and robust adhesive properties for the prevention of radiation-induced dermatitis.

Radiotherapy is a pivotal means of cancer treatment, but it often leads to radiation dermatitis, a skin injury caused by radiation-induced excess reactive oxygen species (ROS). Scavenging free radicals in the course of radiation therapy will be an effective means to prevent radiation dermatitis. This study demonstrates a novel double network hydrogel doped with MoS2 nanosheets for the prevention of radiation-induced dermatitis. The resultant SPM hydrogel constructed from polyacrylamide (PAM) and sodium alginate (SA) nanofiber presented favorable mechanical and adhesion properties. It could conform well to the human body's irregular contours without secondary dressing fixation, making it suitable for skin protection applications. The in vitro and in vivo experiments showed that the antioxidant properties conferred by MoS2 nanosheets enable SPM to effectively mitigate excessive ROS and reduce oxidative stress, thereby preventing radiation dermatitis caused by oxidative damage. Biosafety assessments indicated good biocompatibility of the composite hydrogel, suggesting SPM's practicality and potential as an external dressing for skin radiation protection.

Neural Network Model Based on Branch Architecture for the Quality Assurance of Volumetric Modulated Arc Therapy.

Radiation therapy relies on quality assurance (QA) to verify dose delivery accuracy. However, current QA methods suffer from operation lag as well as inaccurate performance. Hence, to address these shortcomings, this paper proposes a QA neural network model based on branch architecture, which is based on the analysis of the category features of the QA complexity metrics. The designed branch network focuses on category features, which effectively improves the feature extraction capability for complexity metrics. The branch features extracted by the model are fused to predict the GPR for more accurate QA. The performance of the proposed method was validated on the collected dataset. The experiments show that the prediction performance of the model outperforms other QA methods; the average prediction errors for the test set are 2.12% (2%/2 mm), 1.69% (3%/2 mm), and 1.30% (3%/3 mm). Moreover, the results indicate that two-thirds of the validation samples' model predictions perform better than the clinical evaluation results, suggesting that the proposed model can assist physicists in the clinic.

An unusual aromatase/cyclase programs the formation of the phenyldimethylanthrone framework in anthrabenzoxocinones and fasamycin.

Aromatic polyketides are renowned for their wide-ranging pharmaceutical activities. Their structural diversity is mainly produced via modification of limited types of basic frameworks. In this study, we characterized the biosynthesis of a unique basic aromatic framework, phenyldimethylanthrone (PDA) found in (+)/(-)-anthrabenzoxocinones (ABXs) and fasamycin (FAS). Its biosynthesis employs a methyltransferase (Abx(+)M/Abx(-)M/FasT) and an unusual TcmI-like aromatase/cyclase (ARO/CYC, Abx(+)D/Abx(-)D/FasL) as well as a nonessential helper ARO/CYC (Abx(+)C/Abx(-)C/FasD) to catalyze the aromatization/cyclization of polyketide chain, leading to the formation of all four aromatic rings of the PDA framework, including the C9 to C14 ring and a rare angular benzene ring. Biochemical and structural analysis of Abx(+)D reveals a unique loop region, giving rise to its distinct acyl carrier protein-dependent specificity compared to other conventional TcmI-type ARO/CYCs, all of which impose on free molecules. Mutagenic analysis discloses critical residues of Abx(+)D for its catalytic activity and indicates that the size and shape of its interior pocket determine the orientation of aromatization/cyclization. This study unveils the tetracyclic and non-TcmN type C9 to C14 ARO/CYC, significantly expanding our cognition of ARO/CYCs and the biosynthesis of aromatic polyketide framework.

A gln-tRNA-based CRISPR/Cas9 knockout system enables the functional characterization of genes in the genetically recalcitrant brassica anthracnose fungus Colletotrichum higginsianum.

Colletotrichum higginsianum causes anthracnose disease in brassicas. The availability of the C. higginsianum genome has paved the way for the genome-wide exploration of genes associated with virulence/pathogenicity. However, delimiting the biological functions of these genes remains an arduous task due to the recalcitrance of C. higginsianum to genetic manipulations. Here, we report a CRISPR/Cas9-based system that can knock out the genes in C. higginsianum with a staggering 100% homologous recombination frequency (HRF). The system comprises two vectors: pCas9-Ch_tRp-sgRNA, in which a C. higginsianum glutaminyl-tRNA drives the expression of sgRNA, and pCE-Zero-HPT carrying a donor DNA cassette containing the marker gene HPT flanked by homology arms. Upon co-transformation of the C. higginsianum protoplasts, pCas9-Ch_tRp-sgRNA causes a DNA double-strand break in the targeted gene, followed by homology-directed replacement of the gene with HPT by pCE-Zero-HPT, thereby generating loss-of-function mutants. Using the system, we generated the knockout mutants of two effector candidates (ChBas3 and OBR06881) with a 100% HRF. Interestingly, the ΔChBas3 and ΔOBR06881 mutants did not seem to affect the C. higginsianum infection of Arabidopsis thaliana. Altogether, the CRISPR/Cas9 system developed in the study enables the targeted deletion of genes, including effectors, in C. higginsianum, thus determining their biological functions.

Clinical evaluation on automatic segmentation results of convolutional neural networks in rectal cancer radiotherapy.

Purpose: Image segmentation can be time-consuming and lacks consistency between different oncologists, which is essential in conformal radiotherapy techniques. We aimed to evaluate automatic delineation results generated by convolutional neural networks (CNNs) from geometry and dosimetry perspectives and explore the reliability of these segmentation tools in rectal cancer. Methods: Forty-seven rectal cancer cases treated from February 2018 to April 2019 were randomly collected retrospectively in our cancer center. The oncologists delineated regions of interest (ROIs) on planning CT images as the ground truth, including clinical target volume (CTV), bladder, small intestine, and femoral heads. The corresponding automatic segmentation results were generated by DeepLabv3+ and ResUNet, and we also used Atlas-Based Autosegmentation (ABAS) software for comparison. The geometry evaluation was carried out using the volumetric Dice similarity coefficient (DSC) and surface DSC, and critical dose parameters were assessed based on replanning optimized by clinically approved or automatically generated CTVs and organs at risk (OARs), i.e., the Planref and Plantest. Pearson test was used to explore the correlation between geometric metrics and dose parameters. Results: In geometric evaluation, DeepLabv3+ performed better in DCS metrics for the CTV (volumetric DSC, mean = 0.96, P< 0.01; surface DSC, mean = 0.78, P< 0.01) and small intestine (volumetric DSC, mean = 0.91, P< 0.01; surface DSC, mean = 0.62, P< 0.01), ResUNet had advantages in volumetric DSC of the bladder (mean = 0.97, P< 0.05). For critical dose parameters analysis between Planref and Plantest, there was a significant difference for target volumes (P< 0.01), and no significant difference was found for the ResUNet-generated small intestine (P > 0.05). For the correlation test, a negative correlation was found between DSC metrics (volumetric, surface DSC) and dosimetric parameters (δD95, δD95, HI, CI) for target volumes (P< 0.05), and no significant correlation was found for most tests of OARs (P > 0.05). Conclusions: CNNs show remarkable repeatability and time-saving in automatic segmentation, and their accuracy also has a certain potential in clinical practice. Meanwhile, clinical aspects, such as dose distribution, may need to be considered when comparing the performance of auto-segmentation methods.

Neural signals-based respiratory motion tracking: a proof-of-concept study.

Objective.Respiratory motion tracking techniques can provide optimal treatment accuracy for thoracoabdominal radiotherapy and robotic surgery. However, conventional imaging-based respiratory motion tracking techniques are time-lagged owing to the system latency of medical linear accelerators and surgical robots. This study aims to investigate the precursor time of respiratory-related neural signals and analyze the potential of neural signals-based respiratory motion tracking.Approach.The neural signals and respiratory motion from eighteen healthy volunteers were acquired simultaneously using a 256-channel scalp electroencephalography (EEG) system. The neural signals were preprocessed using the MNE python package to extract respiratory-related EEG neural signals. Cross-correlation analysis was performed to assess the precursor time and cross-correlation coefficient between respiratory-related EEG neural signals and respiratory motion.Main results.Respiratory-related neural signals that precede the emergence of respiratory motion are detectable via non-invasive EEG. On average, the precursor time of respiratory-related EEG neural signals was 0.68 s. The representative cross-correlation coefficients between EEG neural signals and respiratory motion of the eighteen healthy subjects varied from 0.22 to 0.87.Significance.Our findings suggest that neural signals have the potential to compensate for the system latency of medical linear accelerators and surgical robots. This indicates that neural signals-based respiratory motion tracking is a potential promising solution to respiratory motion and could be useful in thoracoabdominal radiotherapy and robotic surgery.

Application of Semiconductor Metal Oxide in Chemiresistive Methane Gas Sensor: Recent Developments and Future Perspectives.

The application of semiconductor metal oxides in chemiresistive methane gas sensors has seen significant progress in recent years, driven by their promising sensitivity, miniaturization potential, and cost-effectiveness. This paper presents a comprehensive review of recent developments and future perspectives in this field. The main findings highlight the advancements in material science, sensor fabrication techniques, and integration methods that have led to enhanced methane-sensing capabilities. Notably, the incorporation of noble metal dopants, nanostructuring, and hybrid materials has significantly improved sensitivity and selectivity. Furthermore, innovative sensor fabrication techniques, such as thin-film deposition and screen printing, have enabled cost-effective and scalable production. The challenges and limitations facing metal oxide-based methane sensors were identified, including issues with sensitivity, selectivity, operating temperature, long-term stability, and response times. To address these challenges, advanced material science techniques were explored, leading to novel metal oxide materials with unique properties. Design improvements, such as integrated heating elements for precise temperature control, were investigated to enhance sensor stability. Additionally, data processing algorithms and machine learning methods were employed to improve selectivity and mitigate baseline drift. The recent developments in semiconductor metal oxide-based chemiresistive methane gas sensors show promising potential for practical applications. The improvements in sensitivity, selectivity, and stability achieved through material innovations and design modifications pave the way for real-world deployment. The integration of machine learning and data processing techniques further enhances the reliability and accuracy of methane detection. However, challenges remain, and future research should focus on overcoming the limitations to fully unlock the capabilities of these sensors. Green manufacturing practices should also be explored to align with increasing environmental consciousness. Overall, the advances in this field open up new opportunities for efficient methane monitoring, leak prevention, and environmental protection.

Engineering the Substrate Specificity of a P450 Dimerase Enables the Collective Biosynthesis of Heterodimeric Tryptophan-Containing Diketopiperazines.

Heterodimeric tryptophan-containing diketopiperazines (HTDKPs) are an important class of bioactive secondary metabolites. Biosynthesis offers a practical opportunity to access their bioactive structural diversity, however, it is restricted by the limited substrate scopes of the HTDKPs-forming P450 dimerases. Herein, by genome mining and investigation of the sequence-product relationships, we unveiled three important residues (F387, F388 and E73) in these P450s that are pivotal for selecting different diketopiperazine (DKP) substrates in the upper binding pocket. Engineering these residues in NasF5053 significantly expanded its substrate specificity and enabled the collective biosynthesis, including 12 self-dimerized and at least 81 cross-dimerized HTDKPs. Structural and molecular dynamics analysis of F387G and E73S revealed that they control the substrate specificity via reducing steric hindrance and regulating substrate tunnels, respectively.

Identification of disease propagation paths in two-layer networks.

To determine the path of disease in different types of networks, a new method based on compressive sensing is proposed for identifying the disease propagation paths in two-layer networks. If a limited amount of data from network nodes is collected, according to the principle of compressive sensing, it is feasible to accurately identify the path of disease propagation in a multilayer network. Experimental results show that the method can be applied to various networks, such as scale-free networks, small-world networks, and random networks. The impact of network density on identification accuracy is explored. The method could be used to aid in the prevention of disease spread.

Guaranteed performance of individual control chart used in gamma passing rate-based patient-specific quality assurance.

PURPOSE: To assess the effect of sampling variability on the performance of individual charts (I-charts) for PSQA and provide a robust and reliable method for unknown PSQA processes. MATERIALS AND METHODS: A total of 1327 pretreatment PSQAs were analyzed. Different datasets with samples in the range of 20-1000 were used to estimate the lower control limit (LCL). Based on the iterative "Identify-Eliminate-Recalculate" and direct calculation without any outlier filtering procedures, five I-charts methods, namely the Shewhart, quantile, scaled weighted variance (SWV), weighted standard deviation (WSD), and skewness correction (SC) method, were used to compute the LCL. The average run length (ARL0) and false alarm rate (FAR0) were calculated to evaluate the performance of LCL. RESULTS: The ground truth of the values of LCL, FAR0, and ARL0 obtained via in-control PSQAs were 92.31%, 0.135%, and 740.7, respectively. Further, for in-control PSQAs, the width of the 95% confidence interval of LCL values for all methods tended to decrease with the increase in sample size. In all sample ranges of in-control PSQAs, only the median LCL and ARL0 values obtained via WSD and SWV methods were close to the ground truth. For the actual unknown PSQAs, based on the "Identify-Eliminate-Recalculate" procedure, only the median LCL values obtained by the WSD method were closest to the ground truth. CONCLUSIONS: Sampling variability seriously affected the I-chart performance in PSQA processes, particularly for small samples. For unknown PSQAs, the WSD method based on the implementation of the iterative "Identify-Eliminate-Recalculate" procedure exhibited sufficient robustness and reliability.

A Functionalized Heterogeneous Catalyst from Atomically Precise Pd1 Au8 Clusters Facilitates Carbon-Carbon Bond Construction.

It has become possible to establish a connection between homogeneous and heterogeneous catalysis with atomically precise metal clusters. Due to their defined coordination geometry, in this work, atomically precise Pd1 Au8 (PPh3 )8 2+ clusters are exploited to identify the crucial factor that can impact the catalytic efficiency for the Suzuki-Miyaura cross-coupling process and further gain valuable insight into the exclusive cooperative effect of the central Pd atom and the peripheral Au atoms of the Pd1 Au8 (PPh3 )8 2+ cluster on controlling the cross-coupling reaction. Specifically, a heterogeneous catalyst, namely Pd1 Au8 @Resin, is designed by exchanging positively charged Pd1 Au8 (PPh3 )8 2+ clusters into the porous resin, thereby not only facilitating catalyst recyclability when performed in a batch reactor, but also realizing time-on-stream performance for the Suzuki-Miyaura cross-coupling reaction carried out in a fixed-bed reactor. The integrated advantages of homogeneous complexes and heterogeneous catalysts are expected to advance the usability of atomically precise metal clusters as heterogeneous catalysts for important bond constructions in homogeneous systems.

Machine learning for predicting accuracy of lung and liver tumor motion tracking using radiomic features.

Background: Internal tumor motion is commonly predicted using external respiratory signals. However, the internal/external correlation is complex and patient-specific. The purpose of this study was to develop various models based on the radiomic features of computed tomography (CT) images to predict the accuracy of tumor motion tracking using external surrogates and to find accurate and reliable tracking algorithms. Methods: Images obtained from a total of 108 and 71 patients pathologically diagnosed with lung and liver cancers, respectively, were examined. Real-time position monitoring motion was fitted to tumor motion, and samples with fitting errors greater than 2 mm were considered positive. Radiomic features were extracted from internal target volumes of average intensity projections, and cross-validation least absolute shrinkage and selection operator (LassoCV) was used to conduct feature selection. Based on the radiomic features, a total of 26 separate models (13 for the lung and 13 for the liver) were trained and tested. Area under the receiver operating characteristic curve (AUC), sensitivity, and specificity were used to assess performance. Relative standard deviation was used to assess stability. Results: Thirty-three and 22 radiomic features were selected for the lung and liver, respectively. For the lung, the AUC varied from 0.848 (decision tree) to 0.941 [support vector classifier (SVC), logistic regression]; sensitivity varied from 0.723 (extreme gradient boosting) to 0.848 [linear support vector classifier (linearSVC)]; specificity varied from 0.834 (gaussian naive bayes) to 0.936 [multilayer perceptron (MLP), wide and deep (W&D)]; and MLP and W&D had better performance and stability than the median. For the liver, the AUC varied from 0.677 [light gradient boosting machine (Light)] to 0.892 (logistic regression); sensitivity varied from 0.717 (W&D) to 0.862 (MLP); specificity varied from 0.566 (Light) to 0.829 (linearSVC); and logistic regression, MLP, and SVC had better performance and stability than the median. Conclusions: Respiratory-sensitive radiomic features extracted from CT images of lung and liver tumors were proved to contain sufficient information to establish an external/internal motion relationship. We developed a rapid and accurate method based on radiomics to classify the accuracy of monitoring a patient's external surface for lung and liver tumor tracking. Several machine learning algorithms-in particular, MLP-demonstrated excellent classification performance and stability.

Autophagy mediates a direct synergistic interaction during co-transmission of two distinct arboviruses by insect vectors.

Multiple viral infections in insect vectors with synergistic effects are common in nature, but the underlying mechanism remains elusive. Here, we find that rice gall dwarf reovirus (RGDV) facilitates the transmission of rice stripe mosaic rhabdovirus (RSMV) by co-infected leafhopper vectors. RSMV nucleoprotein (N) alone activates complete anti-viral autophagy, while RGDV nonstructural protein Pns11 alone induces pro-viral incomplete autophagy. In co-infected vectors, RSMV exploits Pns11-induced autophagosomes to assemble enveloped virions via N-Pns11-ATG5 interaction. Furthermore, RSMV could effectively propagate in Sf9 cells. Expression of Pns11 in Sf9 cells or leafhopper vectors causes the recruitment of N from the ER to Pns11-induced autophagosomes and inhibits N-induced complete autophagic flux, finally facilitating RSMV propagation. In summary, these results demonstrate a previously unappreciated role of autophagy in the regulation of the direct synergistic interaction during co-transmission of two distinct arboviruses by insect vectors and reveal the functional importance of virus-induced autophagosomes in rhabdovirus assembly.

Predictive value of magnetic resonance imaging radiomics-based machine learning for disease progression in patients with high-grade glioma.

Background: Accurately predicting the prognosis of patients with high-grade glioma (HGG) is potentially important for treatment. However, the predictive value of images of various magnetic resonance imaging (MRI) sequences for prognosis at different time points is unknown. We established predictive machine learning models of HGG disease progression and recurrence using MRI radiomics and explored the factors influencing prediction accuracy. Methods: Radiomics features were extracted from T1-weighted (T1WI), contrast-enhanced T1-weighted (CE-T1WI), T2-weighted (T2WI), and fluid-attenuated inversion recovery (FLAIR) images (postoperative radiotherapy planning MRI images) obtained from 162 patients with HGG. The Mann-Whitney U test and least absolute shrinkage and selection operator (LASSO) algorithm were used for feature selection. Machine learning models were used to build prediction models to estimate disease progression or recurrence. The influence of different MRI sequences, regions of interest (ROIs), and prediction time points was also explored. The receiver operating characteristic (ROC) curve was used to evaluate the discriminative performance of each model, and the DeLong test was employed to compare the ROC curves. Results: Radiomics features from T2WI and FLAIR demonstrated greater predictive value for disease progression compared with T1WI or CE-TIWI. The best predictive models, with areas under the ROC curves (AUCs) of 0.70, 0.68, 0.78, 0.78, and 0.78 for predicting disease progression at the 6th, 9th, 12th, 15th, and 18th month after radiotherapy, respectively, were obtained by combining clinical features with gross tumor volume (GTV) and clinical target volume (CTV) features extracted from T2WI and FLAIR. Conclusions: Structural MRI obtained before radiotherapy can be used to predict the disease progression or posttreatment recurrence of HGG. When using MRI radiomics to predict long-term outcomes as opposed to short-term outcomes, better predictive results may be obtained.

Reactivity and Recyclability of Ligand-Protected Metal Cluster Catalysts for CO2 Transformation.

It remains a significant challenge to construct an integrated catalyst that combines advantages of homogeneous and heterogeneous catalysis with clarified mechanism and high performance. Here we show atomically precise CuAg cluster catalysts for CO2 capture and utilization, where two functional units are combined into the clusters: metal and ligand. Due to atomic resolution on total and local structures of such catalysts to be achieved, which disentangles heterogeneous imprecise systems and permits tracing the reaction processes via experiments coupled with theory, site-specific catalysis induced by metal-ligand synergy can be accurately elucidated. The CuAg cluster catalysts exhibit excellent reactivity and recyclability to forge the C-N bonding from CO2 formylation with secondary amines that can make the cluster catalysts more unique compared with typically homogeneous complexes.

A Script-Based Automatic Intensity Modulated Radiation Therapy Planning Method With Robust Optimization for Craniospinal Irradiation.

This report describes a script-based automatic planning method with robust optimization for craniospinal irradiation (CSI) to reduce sensitivity to field matching errors and increase planning efficiency. The data of 10 CSI patients with planning target volume (PTV) lengths between 49.8 and 85.0 cm were retrospectively studied. Robust intensity modulated radiation therapy plans with ±5-mm longitudinal position uncertainty were generated by the automatic planning script. A simple dose prediction model and a self-adjusting method were implied in the automatic plans. The plans' robustness against setup errors was evaluated by deliberately shifting the middle beamset ±5 mm in the superior-inferior direction. Manual and nonrobust plans were also created to evaluate the automatic robust plans' quality, efficiency, and robustness. There were no significant differences between the manual and automatic plans in terms of homogeneity index; conformity index; D1%, D2%, and D98% of PTV; and average doses of organs at risk. However, the D99% of the PTV in the automatic plans was slightly inferior to that in the manual plans. Compared with the manual plans, the automatic plans greatly increased efficiency, with a reduction in planning time of approximately 48%. When ±5-mm superior-inferior errors were introduced, the average deviations of the maximum dose D1% and minimum dose D99% to the spinal cord were 4.9% (±1.1%) and -3.4% (±1.3%), respectively. However, the corresponding values of the nonrobust plans were 20.0% (±5.4%) and -21.2 (±6.3%), respectively. The script-based automatic CSI planning method, combining robust optimization and a dose prediction model, efficiently created a good-quality plan that was robust to setup errors.

Effect of inoculation density of bone marrow mesenchymal stem cells cultured on calcium phosphate cement scaffold on osteogenic differentiation.

BACKGROUND: Calcium phosphate cements (CPCs) are biocompatible materials that have been evaluated as scaffolds in bone tissue engineering. At present, the stem cell density of inoculation on CPC scaffold varies. OBJECTIVE: The aim of this study is to analyze the effect of seeding densities on cell growth and osteogenic differentiation of bone marrow mesenchymal stem cells (BMMSCs) on a calcium phosphate cements (CPCs) scaffold. METHODS: BMMSCs derived from minipigs were seeded onto a CPC scaffold at three densities [1 million/mL (1M), 5 million/mL (5M) and 25 million/mL 25M)], and cultured for osteogenic induction for 1, 4 and 8 days. RESULTS: Well adhered and extended BMMSCs on the CPC scaffold showed significantly different proliferation rates within each seeding density group at different time points (P < 0.05). The number of live cells per unit area in 1M, 5M and 25M increased by 3.5, 3.9 and 2.5 folds respectively. The expression of ALP peaked at 4 days post inoculation with the fold-change being 2.6 and 2.8 times higher in 5M and 25M respectively as compared to 1M. The expression levels of OC, Coll-1 and Runx-2 peaked at 8 days post inoculation. CONCLUSIONS: An optimal seeding density may be more conducive for cell proliferation, differentiation, and extracellular matrix synthesis on scaffolds. We suggest the optimal seeding density should be 5 million/mL.

Machine Learning Radiomics Model for External and Internal Respiratory Motion Correlation Prediction in Lung Tumor.

Objectives: The complexity and specificity of lung tumor motion render it necessary to determine the external and internal correlation individually before applying indirect tumor tracking. However, the correlation cannot be determined from patient respiratory and tumor clinical characteristics before treatment. The purpose of this study is to present a machine learning model for an external/internal correlation prediction that is based on computed tomography (CT) radiomic features. Methods: 4-dimensional computed tomography (4DCT) images of 67 patients were collected retrospectively, and the external/internal correlation of lung tumor was calculated based on Spearman's rank correlation coefficient. Radiomic features were extracted from average intensity projection and the light gradient boosting machine (LightGBM)-based cross-validation (the recursive elimination method) was used for feature selection. The LightGBM framework forecasting models with classification thresholds 0.7, 0.8, and 0.9 are established using stratified 5-fold cross-validation. Model performance was assessed using receiver operating characteristics, sensitivity, and specificity. Results: There were 16, 18, and 13 features selected for models 0.7, 0.8, and 0.9, respectively. Texture features are of great importance in external/internal correlation prediction compared to other features in all models. The sensitivities of the predictions in models 0.7, 0.8, and 0.9 were 0.800 ± 0.126, 0.829 ± 0.140, and 0.864 ± 0.086, respectively. The specificities were 0.771 ± 0.114, 0.936 ± 0.0581, and 0.839 ± 0.101, whereas the area under the curve (AUC) was 0.837, 0.946, and 0.877, respectively. Conclusions: Our findings indicate that radiomics is an effective tool for respiratory motion correlation prediction, which can extract tumor motion characteristics. We proposed a machine learning framework for correlation prediction in the motion management strategy for lung tumor patients.