Metabolic interactions between organs in overweight and obesity using total-body positron emission tomography.

BACKGROUND AND OBJECTIVES: Overweight and obesity is a complex condition resulting from unbalanced energy homeostasis among various organs. However, systemic abnormalities in overweight and obese people are seldom explored in vivo by metabolic imaging techniques. The aim of this study was to determine metabolic abnormities throughout the body in overweight and obese adults using total-body positron emission tomography (PET) glucose uptake imaging. METHODS: Thirty normal weight subjects [body mass index (BMI) < 25 kg/m2, 55.47 ± 13.94 years, 16 men and 14 women], and 26 overweight and obese subjects [BMI ≥ 25 kg/m2, 52.38 ± 9.52 years, 21 men and 5 women] received whole-body 18F-fluorodeoxyglucose PET imaging using the uEXPLORER. Whole-body standardized uptake value normalized by lean body mass (SUL) images were calculated. Metabolic networks were constructed based on the whole-body SUL images using covariance network approach. Both group-level and individual-level network differences between normal weight and overweight/obese subjects were evaluated. Correlation analysis was conducted between network properties and BMI for the overweight/obese subjects. RESULTS: Compared with normal weight subjects, overweight/obese subjects exhibited altered network connectivity strength in four network nodes, namely the pancreas (p = 0.033), spleen (p = 0.021), visceral adipose tissue (VAT) (p = 1.12 × 10-5) and bone (p = 0.021). Network deviations of overweight/obese subjects from the normal weight were positively correlated with BMI (r = 0.718, p = 3.64 × 10-5). In addition, overweight/obese subjects experienced altered connections between organs, and some of the altered connections, including pancreas-right lung and VAT-bilateral lung connections were significantly correlated with BMI. CONCLUSION: Overweight/obese individuals exhibit metabolic alterations in organ level, and altered metabolic interactions at the systemic level. The proposed approach using total-body PET imaging can reveal individual metabolic variability and metabolic deviations between organs, which would open up a new path for understanding metabolic alterations in overweight and obesity.

Effect of abdominal adipose tissue glucose uptake on brain aging.

INTRODUCTION: Abdominal adipose tissue (AT) mass has adverse effects on the brain. This study aimed to investigate the effect of glucose uptake by abdominal AT on brain aging. METHODS: Three-hundred twenty-five participants underwent total-body positron emission tomography scan. Brain age was estimated in an independent test set (n = 98) using a support vector regression model that was built using a training set (n = 227). Effects of abdominal subcutaneous and visceral AT (SAT/VAT) glucose uptake on brain age delta were evaluated using linear regression. RESULTS: Higher VAT glucose uptake was linked to negative brain age delta across all subgroups. Higher SAT glucose uptake was associated with negative brain age delta in lean individuals. In contrast, increased SAT glucose uptake demonstrated positive trends with brain age delta in female and overweight/obese participants. DISCUSSION: Increased glucose uptake of the abdominal VAT has positive influences on the brain, while SAT may not have such influences, except for lean individuals. HIGHLIGHTS: Higher glucose uptake of the visceral adipose tissue was linked to decelerated brain aging. Higher glucose uptake of the subcutaneous adipose tissue (SAT) was associated with negative brain age delta in lean individuals. Faster brain aging was associated with increased glucose uptake of the SAT in female and overweight and obese individuals.

Predicting the error magnitude in patient-specific QA during radiotherapy based on ResNet.

BACKGROUND: The error magnitude is closely related to patient-specific dosimetry and plays an important role in evaluating the delivery of the radiotherapy plan in QA. No previous study has investigated the feasibility of deep learning to predict error magnitude. OBJECTIVE: The purpose of this study was to predict the error magnitude of different delivery error types in radiotherapy based on ResNet. METHODS: A total of 34 chest cancer plans (172 fields) of intensity-modulated radiation therapy (IMRT) from Eclipse were selected, of which 30 plans (151 fields) were used for model training and validation, and 4 plans including 21 fields were used for external testing. The collimator misalignment (COLL), monitor unit variation (MU), random multi-leaf collimator shift (MLCR), and systematic MLC shift (MLCS) were introduced. These dose distributions of portal dose predictions for the original plans were defined as the reference dose distribution (RDD), while those for the error-introduced plans were defined as the error-introduced dose distribution (EDD). Different inputs were used in the ResNet for predicting the error magnitude. RESULTS: In the test set, the accuracy of error type prediction based on the dose difference, gamma distribution, and RDD + EDD was 98.36%, 98.91%, and 100%, respectively; the root mean squared error (RMSE) was 1.45-1.54, 0.58-0.90, 0.32-0.36, and 0.15-0.24; the mean absolute error (MAE) was 1.06-1.18, 0.32-0.78, 0.25-0.27, and 0.11-0.18, respectively, for COLL, MU, MLCR and MLCS. CONCLUSIONS: In this study, error magnitude prediction models with dose difference, gamma distribution, and RDD + EDD are established based on ResNet. The accurate prediction of the error magnitude under different error types can provide reference for error analysis in patient-specific QA.

Dosimetry and treatment efficiency of SBRT using TaiChiB radiotherapy system for two-lung lesions with one overlapping organs at risk.

Purpose: This study aims to assess the dosimetry and treatment efficiency of TaiChiB-based Stereotactic Body Radiotherapy (SBRT) plans applying to treat two-lung lesions with one overlapping organs at risk. Methods: For four retrospective patients diagnosed with two-lung lesions each patient, four treatment plans were designed including Plan Edge, TaiChiB linac-based, RGS-based, and a linac-RGS hybrid (Plan TCLinac, Plan TCRGS, and Plan TCHybrid). Dosimetric metrics and beam-on time were employed to evaluate and compare the TaiChiB-based plans against Plan Edge. Results: For Conformity Index (CI), Plan TCRGS outperformed all other plans with an average CI of 1.06, as opposed to Plan Edge's 1.33. Similarly, for R50 %, Plan TCRGS was superior with an average R50 % of 3.79, better than Plan Edge's 4.28. In terms of D2 cm, Plan TCRGS also led with an average of 48.48%, compared to Plan Edge's 56.25%. For organ at risk (OAR) sparing, Plan TCRGS often displayed the lowest dosimetric values, notably for the spinal cord (Dmax 5.92 Gy) and lungs (D1500cc 1.00 Gy, D1000cc 2.61 Gy, V10 Gy 15.14%). However, its high Dmax values for the heart and great vessels sometimes exceeded safety thresholds. Plan TCHybrid presented a balanced approach, showing doses comparable to or better than Plan Edge without crossing safety limits. In terms of beam-on time, Plan TCLinac emerged as the most efficient treatment option in three out of four cases, followed closely by Plan Edge in one case. Plan TCRGS, despite its dosimetric advantages, was the least efficient, recording notably longer beam-on times, with a peak at 33.28 minutes in Case 2. Conclusion: For patients with two-lung lesions treated by SBRT whose one lesion overlaps with OARs, the Plan TCHybrid delivered by TaiChiB digital radiotherapy system can be recommended as a clinical option.

Improving CBCT image quality to the CT level using RegGAN in esophageal cancer adaptive radiotherapy.

OBJECTIVE: This study aimed to improve the image quality and CT Hounsfield unit accuracy of daily cone-beam computed tomography (CBCT) using registration generative adversarial networks (RegGAN) and apply synthetic CT (sCT) images to dose calculations in radiotherapy. METHODS: The CBCT/planning CT images of 150 esophageal cancer patients undergoing radiotherapy were used for training (120 patients) and testing (30 patients). An unsupervised deep-learning method, the 2.5D RegGAN model with an adaptively trained registration network, was proposed, through which sCT images were generated. The quality of deep-learning-generated sCT images was quantitatively compared to the reference deformed CT (dCT) image using mean absolute error (MAE), root mean square error (RMSE) of Hounsfield units (HU), and peak signal-to-noise ratio (PSNR). The dose calculation accuracy was further evaluated for esophageal cancer radiotherapy plans, and the same plans were calculated on dCT, CBCT, and sCT images. RESULTS: The quality of sCT images produced by RegGAN was significantly improved compared to the original CBCT images. ReGAN achieved image quality in the testing patients with MAE sCT vs. CBCT: 43.7 ± 4.8 vs. 80.1 ± 9.1; RMSE sCT vs. CBCT: 67.2 ± 12.4 vs. 124.2 ± 21.8; and PSNR sCT vs. CBCT: 27.9 ± 5.6 vs. 21.3 ± 4.2. The sCT images generated by the RegGAN model showed superior accuracy on dose calculation, with higher gamma passing rates (93.3 ± 4.4, 90.4 ± 5.2, and 84.3 ± 6.6) compared to original CBCT images (89.6 ± 5.7, 85.7 ± 6.9, and 72.5 ± 12.5) under the criteria of 3 mm/3%, 2 mm/2%, and 1 mm/1%, respectively. CONCLUSION: The proposed deep-learning RegGAN model seems promising for generation of high-quality sCT images from stand-alone thoracic CBCT images in an efficient way and thus has the potential to support CBCT-based esophageal cancer adaptive radiotherapy.

Image-based features in machine learning to identify delivery errors and predict error magnitude for patient-specific IMRT quality assurance.

OBJECTIVE: To identify delivery error type and predict associated error magnitude by image-based features using machine learning (ML). METHODS: In this study, a total of 40 thoracic plans (including 208 beams) were selected, and four error types with different magnitudes were introduced into the original plans, including 1) collimator misalignment (COLL), 2) monitor unit (MU) variation, 3) systematic multileaf collimator misalignment (MLCS), and 4) random MLC misalignment (MLCR). These dose distributions of portal dose predictions for the original plans were defined as the reference dose distributions (RDD), while those for the error-introduced plans were defined as the error-introduced dose distributions (EDD). Both distributions were calculated for all beams with portal dose image prediction (PDIP). Besides, 14 image-based features were extracted from RDD and EDD of portal dose predictions to obtain the feature vectors. In addition, a random forest was adopted for the multiclass classification task, and regression prediction for error magnitude. RESULTS: The top five features extracted with the highest weight included 1) the relative displacement in the x direction, 2) the ratio of the absolute minimum residual error to the maximal RDD value, 3) the product of the maximum and minimum residuals, 4) the ratio of the absolute maximum residual error to the maximal RDD value, and 5) the ratio of the absolute mean residual value to the maximal RDD value. The relative displacement in the x direction had the highest weight. The overall accuracy of the five-class classification model was 99.85% for the validation set and 99.30% for the testing set. This model could be applied to the classification of the error-free plan, COLL, MU, MLCS, and MLCR with an accuracy of 100%, 98.4%, 99.9%, 98.0%, and 98.3%, respectively. MLCR had the worst performance in error magnitude prediction (70.1-96.6%), while others had better performance in error magnitude prediction (higher than 93%). In the error magnitude prediction, the mean absolute error (MAE) between predicted error magnitude and actual error ranged from 0.03 to 0.33, with the root mean squared error (RMSE) varying from 0.17 to 0.56 for the validation set. The MAE and RMSE ranged from 0.03 to 0.50 and 0.44 to 0.59 for the test set, respectively. CONCLUSION: It could be demonstrated in this study that the image-based features extracted from RDD and EDD can be employed to identify different types of delivery errors and accurately predict error magnitude with the assistance of ML techniques. They can be used to associate traditional gamma analysis with clinically based analysis for error classification and magnitude prediction in patient-specific IMRT quality assurance.

The feasibility of quantitative assessment of dynamic 18F-fluorodeoxyglucose PET in Takayasu's arteritis: a pilot study.

PURPOSE: PET has been demonstrated to be sensitive for detecting active inflammation in Takayasu's arteritis (TAK) patients, but semi-quantitative-based assessment may be susceptible to various biological and technical factors. Absolute quantification via dynamic PET (dPET) may provide a more reliable and quantitative assessment of TAK-active arteries. The purpose of this study was to investigate the feasibility and efficacy of dPET in quantifying TAK-active arteries compared to static PET. MATERIALS AND METHODS: This prospective study enrolled 10 TAK-active patients (fulfilled the NIH criteria) and 5 control participants from March to October 2022. One-hour dPET scan (all TAK and control participants) and delayed static PET scan at 2-h (all TAK patients) were acquired. For 1-h static PET, summed images from 50 to 60 min of the dPET were extracted. PET parameters derived from 1- and 2-h static PET including SUV (SUV1H and SUV2H), target-to-background ratio (TBR) (TBR1H and TBR2H), net influx rate (Ki), and TBRKi extracted from dPET were obtained. The detectability of TAK-active arteries was compared among different scanning methods using the generalized estimating equation (GEE) with a logistic regression with repeated measures, and the GEE with gamma distribution and log link function was used to evaluate the different study groups or scanning methods. RESULTS: Based on the disease states, 5 cases of TAK were classified as untreated and relapsed, respectively. The SUVmax on 2-h PET was higher than that on 1-h PET in the untreated patients (P < 0.05). However, no significant differences were observed in the median SUVmax between 1-h PET and 2-h PET in the relapsed patients (P > 0.05). The TBRKi was significantly higher than both TBR1H and TBR2H (all P < 0.001). Moreover, the detectability of TAK-active arteries by dPET-derived Ki was significantly higher than 1-h and 2-h PET (all P < 0.001). Significant differences were observed in Kimax, SUVmax-1H, TBR1H, and TBRKi among untreated, relapsed, and control groups (all P < 0.05). CONCLUSIONS: Absolute quantitative assessment by dPET provides an improved sensitivity and detectability in both visualization and quantification of TAK-active arteries. This elucidates the clinical significance of dPET in the early detection of active inflammation and monitoring recurrence.

A planning strategy may reduce the risk of heart diseases and radiation pneumonia: Avoiding the specific heart substructures.

BACKGROUND: Dose to heart substructures is a better predictor for major adverse cardiac events (MACE) than mean heart dose (MHD). We propose an avoidance planning strategy for important cardiac substructures. MATERIAL AND METHODS: Two plans, clinical and cardiac substructure-avoidance plan, were generated for twenty patients. Five dose-sensitive substructures, including left ventricle, pulmonary artery, left anterior descending branch, left circumflex branch and the coronary artery were chosen. The avoidance plan aims to meet the target criteria and organ-at-risk (OARs) constraints while minimizing the dose parameters of the above five substructures. The dosimetric assessments included the mean dose and the maximum dose of cardiac substructures and several volume parameters. In addition, we also evaluated the relative risk of coronary artery disease (CAD), chronic heart failure (CHF), and radiation pneumonia (RP). RESULTS: Pearson correlation coefficient and R2 value of linear regression fitting demonstrated that MHD had poor prediction ability for the mean dose of the cardiac substructures. Compared to clinical plans, an avoidance plan is able to statistically significantly decrease the dose to key substructures. Meanwhile, the dose to OARs and the coverage of the target are comparable in the two plans. In addition, it can be observed that the avoidance plan statistically decreases the relative risks of CAD, CHF, and RP. CONCLUSIONS: The substructure-avoidance planning strategy that incorporates the cardiac substructures into optimization process, can protect the important heart substructures, such as left ventricle, left anterior descending branch and pulmonary artery, achieving the substantive sparing of dose-sensitive cardiac structures, and have the potential to decrease the relative risks of CAD, CHF, and RP.

A critical review of on-site inorganic arsenic screening methods.

Approximately 94 to 220 million people worldwide are at risk of drinking well water containing arsenic > 10 µg/L, the WHO guideline value. To identify non-compliant domestic wells, assess health risks and reduce exposure, accurate and rapid on-site inorganic arsenic screening methods are desirable because all domestic wells worldwide need to be tested. Here, the principles, advantages and limitations of commonly used colorimetry, electrochemistry, and biosensing methods are critically reviewed, with the performance compared with laboratory-based benchmark methods. Most commercial kits are based on the classic Gutzeit reaction. Despite being semi-quantitative, the more recent and more expensive products display improved and acceptable accuracy and shorter testing time (∼10 min). Carried out by trained professionals, electrochemical methods are also feasible for on-site analysis, although miniaturization is desirable yet challenging. Biosensing using whole bacterial cells or bio-engineered materials such as aptamers is promising, if incorporated with function specific nanomaterials and biomaterials. Since arsenic is frequently found as arsenite in reducing groundwater and subject to oxidation during sampling, transportation and storage, on-site separation and sample preservation are feasible but the specific methods should be chosen based on sample matrix and tested before use. To eliminate arsenic exposure among hundreds of millions of mostly rural residents worldwide, we call for concerted efforts in research community and regulatory authority to develop accurate, rapid, and affordable tests for on-site screening and monitoring of arsenic in drinking water. Access to affordable testing will benefit people who are socioeconomically disadvantaged.

A novel lung-avoidance planning strategy based on 4DCT ventilation imaging and CT density characteristics for stage III non-small-cell lung cancer patients.

BACKGROUND: Functional planning based merely on 4DCT ventilation imaging has limitations. In this study, we proposed a radiotherapy planning strategy based on 4DCT ventilation imaging and CT density characteristics. MATERIALS AND METHODS: For 20 stage III non-small-cell lung cancer (NSCLC) patients, clinical plans and lung-avoidance plans were generated. Through deformable image registration (DIR) and quantitative image analysis, a 4DCT ventilation map was calculated. High-, medium-, and low-ventilation regions of the lung were defined based on the ventilation value. In addition, the total lung was also divided into high-, medium-, and low-density areas according to the HU threshold. The lung-avoidance plan aimed to reduce the dose to functional and high-density lungs while meeting standard target and critical structure constraints. Standard and dose-function metrics were compared between the clinical and lung-avoidance plans. RESULTS: Lung avoidance plans led to significant reductions in high-function and high-density lung doses, without significantly increasing other organ at risk (OAR) doses, but at the expense of a significantly degraded homogeneity index (HI) and conformity index (CI; p < 0.05) of the planning target volume (PTV) and a slight increase in monitor units (MU) as well as in the number of segments (p > 0.05). Compared with the clinical plan, the mean lung dose (MLD) in the high-function and high-density areas was reduced by 0.59 Gy and 0.57 Gy, respectively. CONCLUSION: A lung-avoidance plan based on 4DCT ventilation imaging and CT density characteristics is feasible and implementable, with potential clinical benefits. Clinical trials will be crucial to show the clinical relevance of this lung-avoidance planning strategy.

A 2D-3D hybrid convolutional neural network for lung lobe auto-segmentation on standard slice thickness computed tomography of patients receiving radiotherapy.

BACKGROUND: Accurate segmentation of lung lobe on routine computed tomography (CT) images of locally advanced stage lung cancer patients undergoing radiotherapy can help radiation oncologists to implement lobar-level treatment planning, dose assessment and efficacy prediction. We aim to establish a novel 2D-3D hybrid convolutional neural network (CNN) to provide reliable lung lobe auto-segmentation results in the clinical setting. METHODS: We retrospectively collected and evaluated thorax CT scans of 105 locally advanced non-small-cell lung cancer (NSCLC) patients treated at our institution from June 2019 to August 2020. The CT images were acquired with 5 mm slice thickness. Two CNNs were used for lung lobe segmentation, a 3D CNN for extracting 3D contextual information and a 2D CNN for extracting texture information. Contouring quality was evaluated using six quantitative metrics and visual evaluation was performed to assess the clinical acceptability. RESULTS: For the 35 cases in the test group, Dice Similarity Coefficient (DSC) of all lung lobes contours exceeded 0.75, which met the pass criteria of the segmentation result. Our model achieved high performances with DSC as high as 0.9579, 0.9479, 0.9507, 0.9484, and 0.9003 for left upper lobe (LUL), left lower lobe (LLL), right upper lobe (RUL), right lower lobe (RLL), and right middle lobe (RML), respectively. The proposed model resulted in accuracy, sensitivity, and specificity of 99.57, 98.23, 99.65 for LUL; 99.6, 96.14, 99.76 for LLL; 99.67, 96.13, 99.81 for RUL; 99.72, 92.38, 99.83 for RML; 99.58, 96.03, 99.78 for RLL, respectively. Clinician's visual assessment showed that 164/175 lobe contours met the requirements for clinical use, only 11 contours need manual correction. CONCLUSIONS: Our 2D-3D hybrid CNN model achieved accurate automatic segmentation of lung lobes on conventional slice-thickness CT of locally advanced lung cancer patients, and has good clinical practicability.

A novel specific grading standard study of auto-segmentation of organs at risk in thorax: subjective-objective-combined grading standard.

BACKGROUND: To develop a novel subjective-objective-combined (SOC) grading standard for auto-segmentation for each organ at risk (OAR) in the thorax. METHODS: A radiation oncologist manually delineated 13 thoracic OARs from computed tomography (CT) images of 40 patients. OAR auto-segmentation accuracy was graded by five geometric objective indexes, including the Dice similarity coefficient (DSC), the difference of the Euclidean distance between centers of mass (ΔCMD), the difference of volume (ΔV), maximum Hausdorff distance (MHD), and average Hausdorff distance (AHD). The grading results were compared with those of the corresponding geometric indexes obtained by geometric objective methods in the other two centers. OAR auto-segmentation accuracy was also graded by our subjective evaluation standard. These grading results were compared with those of DSC. Based on the subjective evaluation standard and the five geometric indexes, the correspondence between the subjective evaluation level and the geometric index range was established for each OAR. RESULTS: For ΔCMD, ΔV, and MHD, the grading results of the geometric objective evaluation methods at our center and the other two centers were inconsistent. For DSC and AHD, the grading results of three centers were consistent. Seven OARs' grading results in the subjective evaluation standard were inconsistent with those of DSC. Six OARs' grading results in the subjective evaluation standard were consistent with those of DSC. Finally, we proposed a new evaluation method that combined the subjective evaluation level of those OARs with the range of corresponding DSC to determine the grading standard. If the DSC ranges between the adjacent levels did not overlap, the DSC range was used as the grading standard. Otherwise, the mean value of DSC was used as the grading standard. CONCLUSIONS: A novel OAR-specific SOC grading standard in thorax was developed. The SOC grading standard provides a possible alternative for evaluation of the auto-segmentation accuracy for thoracic OARs.

A novel CRT-IMRT-combined (Co-CRIM) planning technique for peripheral lung stereotactic body radiotherapy in pinnacle treatment planning system.

OBJECTIVES: This study attempts to explore a novel peripheral lung stereotactic body radiotherapy (SBRT) planning technique that can balance the pros and cons of three-dimensional conformal radiotherapy (CRT) and intensity-modulated radiation therapy (IMRT) / volumetric modulated arc therapy (VMAT). METHODS: Treatment plans were retrospectively designed based on CRT, IMRT, VMAT, and the proposed CRT-IMRT-combined (Co-CRIM) techniques using Pinnacle treatment planning system (TPS) for 20 peripheral lung cancer patients. Co-CRIM used an inverse optimization algorithm available in Pinnacle TPS. To develop a Co-CRIM plan, the number of segments in each field was limited to one, the minimum segment area was set to the internal target volume (ITV), and the minimum monitor units (MU) of the segment was the quotient of fractional dose divided by twice the number of total fields. The performance of Co-CRIM was then compared with other techniques. RESULTS: For conformity index (CI), Co-CRIM performed comparably to IMRT/VMAT but better than CRT. For gradient index (GI), Co-CRIM was similar to IMRT/VMAT or CRT. For heterogeneity index (HI), Co-CRIM was comparable to IMRT/VMAT, higher than CRT. The dosimetric results of spinal cord and lung with Co-CRIM were better than CRT, comparable to IMRT, but inferior to VMAT. The MU resulted from Co-CRIM was lower than IMRT/VMAT but higher than CRT. For plan verification γ passing rate, Co-CRIM was higher than IMRT/VMAT, comparable to CRT. For planning time, Co-CRIM was shorter than CRT or VMAT but similar to IMRT. CONCLUSIONS: The proposed Co-CRIM technique on Pinnacle TPS is an effective planning technique for peripheral lung SBRT.

Dynamic Indicators That Impact the Outcomes of Thoracic Endovascular Aortic Repair in Complicated Type B Aortic Dissection.

PURPOSE: To investigate dynamic variables obtained from retrospective computed tomography angiography for ability to predict thoracic endovascular aortic repair (TEVAR) outcomes in patients with complicated type B aortic dissection (cTBAD). MATERIALS AND METHODS: Seventy-nine patients with cTBAD who received TEVAR from March 2009 to June 2018 were retrospectively enrolled. Relative true lumen area (r-TLA) was computed at the level of tracheal bifurcation every 5% of all R-R intervals. Parameters that reflect the state of intimal motion were evaluated, including difference between maximum and minimum r-TLA (D-TLA) and true lumen collapse. The endpoints comprised early (≤ 30 days) and late (> 30 days) outcomes after intervention. RESULTS: Overall early mortality rate was 13.9% (11/79), and early adverse events rate was 24.1% (19/79). Patients who received TEVAR within 2 days of symptom onset demonstrated the worst outcomes. A longer time of r-TLA < 25% in 1 cardiac cycle (P = .049) and larger D-TLA (P < .001) were correlated to an increased early death. In addition, D-TLA was an independent predictor of early mortality. Area under the curve of D-TLA was 0.849 (95% confidence interval 0.730-0.967) for predicting early mortality and 0.742 (95% CI 0.611-0.873) for predicting early adverse events. Survival and event-free survival rates during follow-up were decreased in the D-TLA > 21.5% group compared with the D-TLA ≤ 21.5% group (all P < .001). CONCLUSIONS: Larger D-TLA is correlated with worse postoperative outcomes and might be a crucial parameter for future risk stratification in patients with cTBAD.

Impact of Physico-Chemical Heterogeneity on Arsenic Sorption and Reactive Transport under Water Extraction.

Heterogeneity in physical and chemical properties is a common characteristic in a subsurface environment. This study investigated the effect of physico-chemical heterogeneity on arsenic (As) sorption and reactive transport under water extraction in a layered system with preferential flow paths. A flume experiment was performed to derive the spatio-temporal data of As reactive transport. The results indicated that the heterogeneous system significantly accelerated downward (vertical direction) As migration as a coupled effect of physical and chemical heterogeneity that led to fast As transport with low As sorption along the preferential flow paths. The results also indicated that such a heterogeneity effect was driven by water extraction that enhanced the downward groundwater flow along the preferential flow paths. Numerical simulations were performed by matching the experimental results to provide insights into the dominant processes controlling the As migration in the heterogeneous systems. The simulation results highlighted the importance of the kinetic oxidation of mineral-bonded Fe(II) to Fe(III) in the clay matrix that dynamically increased As sorption affinity and retarded As reactive transport. A coupled model of reactive transport along the preferential flow paths, sorption-retarded diffusion from the preferential flow paths into the clay matrixes, and reactions that change sorption affinity in the matrix was required to describe the As reactive transport systems with physico-chemical heterogeneities. The results have strong implications for understanding and modeling As downward migration from shallow to deep aquifers under groundwater pumping conditions in field systems with inherent heterogeneity.

Effect of MU-weighted multi-leaf collimator position error on dose distribution of SBRT radiotherapy in peripheral non-small cell lung cancer.

PURPOSE: Position accuracy of the multi-leaf collimator (MLC) is essential in stereotactic body radiotherapy (SBRT). This study is aimed to investigate the dosimetric impacts of the MU-weighted MLC positioning uncertainties of SBRT for patients with early stage peripheral non-small cell lung cancer (NSCLC). METHODS: Three types of MLC position error were simulated: Type 1, random error; Type 2, system shift, in which both MLC banks shifted to the left or right direction; and Type 3, in which both MLC banks moved with same magnitudes in the opposite directions. Two baseline plans were generated: an automatic plan (AP) and a manually optimized plan (MP). Multi-leaf collimator position errors were introduced to generate simulated plans with the preset MLC leaf position errors, which were then reimported into the Pinnacle system to generate simulated plans, respectively. The dosimetric parameters (CI, nCI, GI, etc.) and gEUD values of PTV and OARs were calculated. Linear regression between MU-weighted/unweighted MLC position error and gEUD was performed to obtain dose sensitivity. RESULTS: The dose sensitivities of the PTVs were -4.93, -38.94, -41.70, -55.55, and 30.33 Gy/mm for random, left shift, right shift, system close, and system open MLC errors, respectively. There were significant differences between the MU-weighted and the unweighted dose sensitivity, which was -38.94 Gy/mm vs -3.42 Gy/mm (left shift), -41.70 Gy/mm vs -3.56 Gy/mm (right shift), -55.55 Gy/mm vs -4.84 Gy/mm (system close), and 30.33 vs 2.64 Gy/mm (system open). For the system open/close MLC errors, as the PTV volume became larger, the dose sensitivity decreased. APs provided smaller dose sensitivity for the system shift and system close MLC errors compared to the conventional MPs. CONCLUSIONS: There was significant difference in dose sensitivity between MU-weighted and unweighted MLC position error of SBRT radiotherapy in peripheral NSCLC. MU is suggested to be included in the dosimetric evaluation of the MLC misalignments, since it is much closer to clinical radiotherapy.

On the optimal number of dose-limiting shells in the SBRT auto-planning design for peripheral lung cancer.

PURPOSE: The number of dose-limiting shells in the optimization process is one of the key factors determining the quality of stereotactic body radiotherapy (SBRT) auto-planning in the Pinnacle treatment planning system (TPS). This study attempted to derive the optimal number of shells by evaluating the auto-plans designed with different number of shells for peripheral lung cancer patients treated with SBRT. METHODS: Identical treatment technique, optimization process, constraints, and dose calculation algorithm in the Pinnacle TPS were retrospectively applied to 50 peripheral lung cancer patients who underwent SBRT in our center. For each of the patients, auto-plans were optimized based on two shells, three shells, four shells, five shells, six shells, seven shells, eight shells, respectively. The optimal number of shells for the SBRT auto-planning was derived through the evaluations and comparisons of various dosimetric parameters of planning target volume (PTV) and organs at risk (OARs), monitor units (MU), and optimization time of the plans. RESULTS: The conformity index (CI) and the gradient index (GI) of PTV, the maximum dose outside the 2 cm of PTV (D2cm ), Dmax of spinal cord (SCmax ), the percentage of volume of total lung excluding ITV receiving 20 Gy (V20) and 10 Gy (V10), and the mean lung dose (MLD) were improved when the number of shell increased, but the improvement became not significant as the number of shell reached six. The monitor units (MUs) varied little among different plans where no statistical differences were found. However, as the number of shell increased, the auto-plan optimization time increased significantly. CONCLUSIONS: It appears that for peripheral lung SBRT plan using six shells can yield satisfactory plan quality with acceptable beam MUs and optimization time in the Pinnacle TPS.

Elucidating the Role of Sulfide on the Stability of Ferrihydrite Colloids under Anoxic Conditions.

While the reaction mechanisms between ferrihydrite and sulfide are well-documented, the role of redox reactions on the particle-particle stability of ferrihydrite colloids is largely overlooked. Such reactions are critical for a number of (bio)geochemical processes governing ferrihydrite-based colloid processing and their associated role in nutrient and contaminant subsurface dynamics. Taking a fundamental colloid chemistry approach, along with a complementary suite of characterization techniques, here, we explore the stability mechanisms of ferrihydrite colloids over a wide range of environmentally relevant sulfide concentrations at pH 6.0. Results show that sulfide lowered the stability of both ferrihydrite colloids in a concentration-dependent fashion. At lower sulfide concentrations (15.6-62.5 µM), ferrihydrite colloids are apparently stable, but their critical coagulation concentration (CCC) in NaCl linearly decreases with increasing sulfide concentration. This is attributed to the formation of negatively charged elemental sulfur (S(0)) nanoparticles on the surfaces of positively charged ferrihydrite, intensifying the electrostatic attractions between oppositely charged regions on adjacent ferrihydrite surfaces. Further increasing sulfide concentration generates more S(0) attaching to the ferrihydrite surface. This results in effective surface charge neutralization and then subsequent charge reversal, leading to extensive aggregation of ferrihydrite (core) colloids. Interestingly, for the ferrihydrite colloids with higher hydrodynamic diameter, aggregation rates linearly decreases with increasing sulfide concentration from 156.3 to 312.5 µM, which is likely due to the formation of substantial amounts of negatively charged S(0) and FeS. Findings highlight the significance of sulfidation products in controlling the stability of ferrihydrite colloids in sulfidic environments.

An efficient optic cup segmentation method decreasing the influences of blood vessels.

BACKGROUND: Optic cup is an important structure in ophthalmologic diagnosis such as glaucoma. Automatic optic cup segmentation is also a key issue in computer aided diagnosis based on digital fundus image. However, current methods didn't effectively solve the problem of edge blurring caused by blood vessels around the optic cup. METHODS: In this study, an improved Bertalmio-Sapiro-Caselles-Ballester (BSCB) model was proposed to eliminate the noising induced by blood vessel. First, morphological operations were performed to get the enhanced green channel image. Then blood vessels were extracted and filled by improved BSCB model. Finally, Local Chart-Vest model was used to segment the optic cup. A total of 94 samples which included 32 glaucoma fundus images and 62 normal fundus images were experimented. RESULTS: The evaluation parameters of F-score and the boundary distance achieved by the proposed method against the results from experts were 0.7955 ± 0.0724 and 11.42 ± 3.61, respectively. Average vertical optic cup-to-disc ratio values of the normal and glaucoma samples achieved by the proposed method were 0.4369 ± 0.1193 and 0.7156 ± 0.0698, which were also close to those by experts. In addition, 39 glaucoma images from the public dataset RIM-ONE were also used for methodology evaluation. CONCLUSIONS: The results showed that our proposed method could overcome the influence of blood vessels in some degree and was competitive to other current optic cup segmentation algorithms. This novel methodology will be expected to use in clinic in the field of glaucoma early detection.