Differences Between Schools of Choice and Traditional Comprehensive Schools in Their Grade 9 Academic Performance

This study was conducted to determine the differences between schools of choice and traditional comprehensive high schools in terms of their Grade 9 State of Texas Assessments of Academic Readiness (STAAR) End-of-Course (EOC) exams in Biology, English 1, and Algebra 1 during the 2017-2018 school year. Inferential statistical analyses revealed the presence of a statistically significant difference between the two types of schools on their EOC exam passing rates in all three content areas for students who were not at-risk and for students who were not in poverty. In contrast, statistically significant differences were not revealed between the two types of schools in terms of the EOC exam passing rates of their CATE and Non-CATE students. Knowing that students who have choice appear to perform better academically, policymakers are encouraged to study the feasibility of channeling more funding to help school districts expand their school choice programs to (a) motivate more students to find the school that fit their needs and their future college and career aspirations, and (b) to solicit more support from parents and community businesses to invest in their communities to improve schools through taxes. Given that the data for this research were gathered for only 16 school districts in South Texas, researchers are encouraged to conduct a study that will involve all school districts in the whole state and possibly the whole nation to reach more conclusive evidence on the differences between schools of choice and neighborhood schools.

End-to-end test for fractionated online adaptive MR-guided radiotherapy using a deformable anthropomorphic pelvis phantom

Objective: In MR-guided radiotherapy (MRgRT) for prostate cancer treatments inter-fractional anatomy changes such as bladder and rectum fillings may be corrected by an online adaption of the treatment plan. To clinically implement such complex treatment procedures, however, specific end-to-end tests are required that are able to validate the overall accuracy of all treatment steps from pre-treatment imaging to dose delivery. Approach: In this study, an end-to-end test of a fractionated and online adapted MRgRT prostate irradiation was performed using the so-called ADAM-PETer phantom. The phantom was adapted to perform 3D polymer gel (PG) dosimetry in the prostate and rectum. Furthermore, thermoluminescence detectors (TLDs) were placed at the center and on the surface of the prostate for additional dose measurements as well as for an external dose renormalization of the PG. For the end-to-end test, a total of five online adapted irradiations were applied in sequence with different bladder and rectum fillings, respectively. Main results: A good agreement of measured and planned dose was found represented by high γ-index passing rates (3 %⁄ 3 mm criterion) of the PG evaluation of 98.9 % in the prostate and 93.7 % in the rectum. TLDs used for PG renormalization at the center of the prostate showed a deviation of -2.3 %. Significance: The presented end-to-end test, which allows for 3D dose verification in the prostate and rectum, demonstrates the feasibility and accuracy of fractionated and online-adapted prostate irradiations in presence of inter-fractional anatomy changes. Such tests are of high clinical importance for the commissioning of new image-guided treatment procedures such as online adaptive MRgRT.

Assessment of Delivery Quality Assurance for Stereotactic Radiosurgery With Cyberknife

PurposeThe purpose of this study is to establish and assess a practical delivery quality assurance method for stereotactic radiosurgery with Cyberknife by analyzing the geometric and dosimetric accuracies obtained using a PTW31016 PinPoint ionization chamber and EBT3 films. Moreover, this study also explores the relationship between the parameters of plan complexity, target volume, and deliverability parameters and provides a valuable reference for improving plan optimization and validation.MethodsOne hundred fifty cases of delivery quality assurance plans were performed on Cyberknife to assess point dose and planar dose distribution, respectively, using a PTW31016 PinPoint ionization chamber and Gafchromic EBT3 films. The measured chamber doses were compared with the planned mean doses in the sensitive volume of the chamber, and the measured planar doses were compared with the calculated dose distribution using gamma index analysis. The gamma passing rates were evaluated using the criteria of 3%/1 mm and 2%/2 mm. The statistical significance of the correlations between the complexity metrics, target volume, and the gamma passing rate were analyzed using Spearman’s rank correlation coefficient.ResultsFor point dose comparison, the averaged dose differences (± standard deviations) were 1.6 ± 0.73% for all the cases. For planar dose distribution, the mean gamma passing rate for 3%/1 mm, and 2%/2 mm evaluation criteria were 94.26% ± 1.89%, and 93.86% ± 2.16%, respectively. The gamma passing rates were higher than 90% for all the delivery quality assurance plans with the criteria of 3%/1 mm and 2%/2 mm. The difference in point dose was lowly correlated with volume of PTV, number of beams, and treatment time for 150 DQA plans, and highly correlated with volume of PTV for 18 DQA plans of small target. DQA gamma passing rate (2%/2 mm) was a moderate significant correlation for the number of nodes, number of beams and treatment time, and a low correlation with MU.ConclusionPTW31016 PinPoint ionization chamber and EBT3 film can be used for routine Cyberknife delivery quality assurance. The point dose difference should be within 3%. The gamma passing rate should be higher than 90% for the criteria of 3%/1 mm and 2%/2 mm. In addition, the plan complexity and PTV volume were found to have some influence on the plan deliverability.

Introducing matrix sparsity with kernel truncation into dose calculations for fluence optimization.

Deep learning algorithms for radiation therapy treatment planning automation require large patient datasets and complex architectures that often take hundreds of hours to train. Some of these algorithms require constant dose updating (such as with reinforcement learning) and may take days. When these algorithms rely on commerical treatment planning systems to perform dose calculations, the data pipeline becomes the bottleneck of the entire algorithm’s efficiency. Further, uniformly accurate distributions are not always needed for the training and approximations can be introduced to speed up the process without affecting the outcome. These approximations not only speed up the calculation process, but allow for custom algorithms to be written specifically for the purposes of use in AI/ML applications where the dose and fluence must be calculated a multitude of times for a multitude of different situations. Here we present and investigate the effect of introducing matrix sparsity through kernel truncation on the dose calculation for the purposes of fluence optimzation within these AI/ML algorithms. The basis for this algorithm relies on voxel discrimination in which numerous voxels are pruned from the computationally expensive part of the calculation. This results in a significant reduction in computation time and storage. Comparing our dose calculation against calculations in both a water phantom and patient anatomy in Eclipse without heterogenity corrections produced gamma index passing rates around 99% for individual and composite beams with uniform fluence and around 98% for beams with a modulated fluence. The resulting sparsity introduces a reduction in computational time and space proportional to the square of the sparsity tolerance with a potential decrease in cost greater than 10 times that of a dense calculation allowing not only for faster caluclations but for calculations that a dense algorithm could not perform on the same system.

Factors influencing performance on the Board Certified Psychiatric Pharmacist Examination: Passing rates and domain-level scores

The Board Certified Psychiatric Pharmacist (BCPP) specialty certification was launched by the Board of Pharmacy Specialties in 1994. Candidates for the BCPP can qualify for the examination through 3 possible pathways: practice experience (4 years) in the specialty, completion of a PGY-1 residency plus an additional 2 years of practice experience, or completion of a PGY-2 specialty residency in psychiatric pharmacy. Recent fluctuations in the passing rate raised questions as to explanatory factors. This article represents the first published comprehensive study of candidate performance on the BCPP Examination. It describes a retrospective, observational study presenting (a) statistical trends of examination passing rates for biannual cohorts over the past 5 years, as well as (b) score distributions on the 3 performance domains of the certification. Pass-rate trend analyses suggest that variation in the proportion of eligibility pathway cohorts in the respective testing samples explains some of the fluctuation in passing rates. An analysis of variance of domain-level scores, using groups defined by eligibility pathway, yielded significant differences for nearly all group comparisons. Evaluation of the effect sizes suggest that the most disparate performance was observed on the core clinical domain, Patient-Centered Care. The results of this study are consistent with previously published research and will inform the upcoming role delineation study for the Psychiatric Pharmacy Certification.

Generating synthetic CT from low-dose cone-beam CT by using generative adversarial networks for adaptive radiotherapy

Objective To develop high-quality synthetic CT (sCT) generation method from low-dose cone-beam CT (CBCT) images by using attention-guided generative adversarial networks (AGGAN) and apply these images to dose calculations in radiotherapy. Methods The CBCT/planning CT images of 170 patients undergoing thoracic radiotherapy were used for training and testing. The CBCT images were scanned under a fast protocol with 50% less clinical projection frames compared with standard chest M20 protocol. Training with aligned paired images was performed using conditional adversarial networks (so-called pix2pix), and training with unpaired images was carried out with cycle-consistent adversarial networks (cycleGAN) and AGGAN, through which sCT images were generated. The image quality and Hounsfield unit (HU) value of the sCT images generated by the three neural networks were compared. The treatment plan was designed on CT and copied to sCT images to calculated dose distribution. Results The image quality of sCT images by all the three methods are significantly improved compared with original CBCT images. The AGGAN achieves the best image quality in the testing patients with the smallest mean absolute error (MAE, 43.5 ± 6.69), largest structural similarity (SSIM, 93.7 ± 3.88) and peak signal-to-noise ratio (PSNR, 29.5 ± 2.36). The sCT images generated by all the three methods showed superior dose calculation accuracy with higher gamma passing rates compared with original CBCT image. The AGGAN offered the highest gamma passing rates (91.4 ± 3.26) under the strictest criteria of 1 mm/1% compared with other methods. In the phantom study, the sCT images generated by AGGAN demonstrated the best image quality and the highest dose calculation accuracy. Conclusions High-quality sCT images were generated from low-dose thoracic CBCT images by using the proposed AGGAN through unpaired CBCT and CT images. The dose distribution could be calculated accurately based on sCT images in radiotherapy.

A Retrospective Study of Commercial Software System EDose in QA Application based on the Electronic Portal Imaging Device

Purpose: To evaluate the functions about the pre-treatment dose verification and, the in vivo dose verification for the commercial software EDose system based on Electronic Portal Imaging Device (EPID) retrospectively and establish the action limit level. Methods: The results of pre-treatment dose verification were compared with 2D array Seven29 and 3Dmap for 50 randomly selected IMRT plans of different lesions. A retrospective analysis was conducted for 287 radiotherapy plans using the EDose in pre-treatment dose verification, including 53 IMRT and 247 RapidArc plans, to establish the action limit level with statistical significance evaluation. 28 head and neck patients with different lesions were selected randomly for studying 3D online dose verification preliminary.Results: For pre-treatment dose verification, 50 plans’ average γ passing rates of the 3%/3mm criterion were > 98% for EDose, Seven29, 3Dmap, and 3%/2mm, 2%/2mm criteria were > 95%, 90%. The average γmean of the three verification methods were similar for the 3%/3mm criterion (0.35, 0.38, 0.35). Based on the 287 patients’ clinical data, the average γ passing rate was 97.5%, and the recommend clinical action level was established at 92% with a 95% confidence limit. The in vivo results showed that the γ pass rate had a decreasing trend as the 33 treatment fractions progressed. The γ passing rates means±SD of the first fraction was (91.92±3.31)% while the 33th fraction was (85.73±8.75)%. In addition, the standard deviation between the TPS calculations and the EDose measurement results indicated a higher value of the thirty-third treatment for PTVs and organ at risk compared to the first treatment.Conclusions: This study demonstrated that the EDose system is an accurate, efficient method for quality assurance of patient’ radiotherapy plans with remarkable consistency of treatment planning system (TPS).

Correlation between the γ passing rates of IMRT plans and the volumes of air cavities and bony structures in head and neck cancer

Background Both patient-specific dose recalculation and γ passing rate analysis are important for the quality assurance (QA) of intensity modulated radiotherapy (IMRT) plans. The aim of this study was to analyse the correlation between the γ passing rates and the volumes of air cavities (Vair) and bony structures (Vbone) in target volume of head and neck cancer. Methods Twenty nasopharyngeal carcinoma and twenty nasal natural killer T-cell lymphoma patients were enrolled in this study. Nine-field sliding window IMRT plans were produced and the dose distributions were calculated by anisotropic analytical algorithm (AAA), Acuros XB algorithm (AXB) and SciMoCa based on the Monte Carlo (MC) technique. The dose distributions and γ passing rates of the targets, organs at risk, air cavities and bony structures were compared among the different algorithms. Results The γ values obtained with AAA and AXB were 95.6 ± 1.9% and 96.2 ± 1.7%, respectively, with 3%/2 mm criteria (p > 0.05). There were significant differences (p < 0.05) in the γ values between AAA and AXB in the air cavities (86.6 ± 9.4% vs. 98.0 ± 1.7%) and bony structures (82.7 ± 13.5% vs. 99.0 ± 1.7%). Using AAA, the γ values were proportional to the natural logarithm of Vair (R2 = 0.674) and inversely proportional to the natural logarithm of Vbone (R2 = 0.816). When the Vair in the targets was smaller than approximately 80 cc or the Vbone in the targets was larger than approximately 6 cc, the γ values of AAA were below 95%. Using AXB, no significant relationship was found between the γ values and Vair or Vbone. Conclusion In clinical head and neck IMRT QA, greater attention should be paid to the effect of Vair and Vbone in the targets on the γ passing rates when using different dose calculation algorithms.

Virtual Patient-Specific Quality Assurance of IMRT Using UNet++: Classification, Gamma Passing Rates Prediction, and Dose Difference Prediction

The dose verification in radiotherapy quality assurance (QA) is time-consuming and places a heavy workload on medical physicists. To provide a clinical tool to perform patient specific QA accurately, the UNet++ is investigated to classify failed or pass fields (the GPR lower than 85% is considered “failed” while the GPR higher than 85% is considered “pass”), predict gamma passing rates (GPR) for different gamma criteria, and predict dose difference from virtual patient-specific quality assurance in radiotherapy. UNet++ was trained and validated with 473 fields and tested with 95 fields. All plans used Portal Dosimetry for dose verification pre-treatment. Planar dose distribution of each field was used as the input for UNet++, with QA classification results, gamma passing rates of different gamma criteria, and dose difference were used as the output. In the test set, the accuracy of the classification model was 95.79%. The mean absolute error (MAE) were 0.82, 0.88, 2.11, 2.52, and the root mean squared error (RMSE) were 1.38, 1.57, 3.33, 3.72 for 3%/3mm, 3%/2 mm, 2%/3 mm, 2%/2 mm, respectively. The trend and position of the predicted dose difference were consistent with the measured dose difference. In conclusion, the Virtual QA based on UNet++ can be used to classify the field passed or not, predict gamma pass rate for different gamma criteria, and predict dose difference. The results show that UNet++ based Virtual QA is promising in quality assurance for radiotherapy.