Testing for lynch syndrome in people with endometrial cancer using immunohistochemistry and microsatellite instability-based testing strategies – a systematic review of test accuracy

Background Lynch syndrome is an inherited genetic condition that is associated with an increased risk of cancer, including endometrial and colorectal cancer. We assessed the test accuracy of immunohistochemistry and microsatellite instability-based testing (with or without MLH1 promoter methylation testing) for Lynch syndrome in women with endometrial cancer. Methods We conducted a systematic review of literature published up to August 2019. We searched bibliographic databases, contacted experts and checked reference lists of relevant studies. Two reviewers conducted each stage of the review. Results Thirteen studies were identified that included approximately 3500 participants. None of the studies was at low risk of bias in all domains. Data could not be pooled due to the small number of heterogeneous studies. Sensitivity ranged from 60.7–100% for immunohistochemistry, 41.7–100% for microsatellite instability-based testing, and 90.5–100% for studies combining immunohistochemistry, microsatellite instability-based testing, and MLH1 promoter methylation testing. Specificity ranged from 60.9–83.3% (excluding 1 study with highly selective inclusion criteria) for immunohistochemistry, 69.2–89.9% for microsatellite instability-based testing, and 72.4–92.3% (excluding 1 study with highly selective inclusion criteria) for testing strategies that included immunohistochemistry, microsatellite instability-based testing, and MLH1 promoter methylation. We found no statistically significant differences in test accuracy estimates (sensitivity, specificity) in head-to-head studies of immunohistochemistry versus microsatellite instability-based testing. Reported test failures were rare. Conclusions Sensitivity of the index tests were generally high, though most studies had much lower specificity. We found no evidence that test accuracy differed between IHC and MSI based strategies. The evidence base is currently small and at high risk of bias

A Case Study on the Impact of Web-based Technology in a Simulation Analysis Course

A case study is presented on the use of web-based technol ogy to transition from a lecture-based delivery system to an online/multimedia technology delivery system at the University of Oklahoma's School of Industrial Engineer ing. Coupling web and multimedia technology with a pyramid approach to a simulation course sequence, the goal is to provide both undergraduate and graduate stu dents with strong simulation skills in both modeling and analysis. Web-based technology is used to provide course access to non-traditional students, to re-enforce prerequi site knowledge, and to support learning statistical con cepts. The approach has been successful at (i) generating two types of graduates, the simulation modeler and the simulation analyst/consultant, (ii) increasing the reten tion of non-traditional students (industrial engineering students with full-time jobs and other engineering majors without strong statistical backgrounds), and (iii) gradu ating two non-traditional students in the School's master's degree program as based on their research in simulation analysis. However, online technologies are not without their disadvantages. While the burden has been eased on student learning and their out-of-class activities, the faculty is now tasked with an increased load of sup porting online courses and utilizing web-based technolo gies both within and outside the classroom.Yeshttps://us.sagepub.com/en-us/nam/manuscript-submission-guideline

A 3-D PYRAMID/PRISM APPROACH TO VIEW KNOWLEDGE REQUIREMENTS FOR THE BATCH MEANS METHOD WHEN TAUGHT IN A LANGUAGE-FOCUSED, UNDERGRADUATE SIMULATION COURSE

We develop a 3-D knowledge pyramid/prism model to structure the relationships of (i) lower-level learning, (ii) ‘optional ’ knowledge bases, (iii) concurrent knowledge, and (ii) new knowledge; so one may view the learning needs of a higher-level learning objective. Our paradigm stems from Bloom’s taxonomy of learning, but has the advantage of supporting ‘just-in-time ’ and ‘learn-by-doing’ delivery, teaching and learning styles. We illustrate the paradigm through the BMMKP (the 3-D knowledge pyramid/prism model of the highest-level, batch-means-method learning objective for our language-focused, undergraduate course). The BMMKP reveals how highly dependent and fully integrated this learning is to calculus, probability, statistics, and queuing theory—regardless of the simulation modeling language chosen to teach in the course. The BMMKP is then used to develop a set of lower-level learning objectives for the undergraduate course. The 3-D pyramid/prism approach should lend itself well as a communication tool for visualizing other simulation learning objectives.

Mice lacking Astn2 have ASD-like behaviors and altered cerebellar circuit properties

Astrotactin 2 (ASTN2) is a transmembrane neuronal protein highly expressed in the cerebellum that functions in receptor trafficking and modulates cerebellar Purkinje cell (PC) synaptic activity. Individuals wit

A Real-Time Contouring Feedback Tool for Consensus-Based Contour Training

PURPOSE: Variability in contouring structures of interest for radiotherapy continues to be challenging. Although training can reduce such variability, having radiation oncologists provide feedback can be impractical. We developed a contour training tool to provide real-time feedback to trainees, thereby reducing variability in contouring. METHODS: We developed a novel metric termed localized signed square distance (LSSD) to provide feedback to the trainee on how their contour compares with a reference contour, which is generated real-time by combining trainee contour and multiple expert radiation oncologist contours. Nine trainees performed contour training by using six randomly assigned training cases that included one test case of the heart and left ventricle (LV). The test case was repeated 30 days later to assess retention. The distribution of LSSD maps of the initial contour for the training cases was combined and compared with the distribution of LSSD maps of the final contours for all training cases. The difference in standard deviations from the initial to final LSSD maps, ΔLSSD, was computed both on a per-case basis and for the entire group. RESULTS: For every training case, statistically significant ΔLSSD were observed for both the heart and LV. When all initial and final LSSD maps were aggregated for the training cases, before training, the mean LSSD ([range], standard deviation) was -0.8 mm ([-37.9, 34.9], 4.2) and 0.3 mm ([-25.1, 32.7], 4.8) for heart and LV, respectively. These were reduced to -0.1 mm ([-16.2, 7.3], 0.8) and 0.1 mm ([-6.6, 8.3], 0.7) for the final LSSD maps during the contour training sessions. For the retention case, the initial and final LSSD maps of the retention case were aggregated and were -1.5 mm ([-22.9, 19.9], 3.4) and -0.2 mm ([-4.5, 1.5], 0.7) for the heart and 1.8 mm ([-16.7, 34.5], 5.1) and 0.2 mm ([-3.9, 1.6],0.7) for the LV. CONCLUSIONS: A tool that uses real-time contouring feedback was developed and successfully used for contour training of nine trainees. In all cases, the utility was able to guide the trainee and ultimately reduce the variability of the trainee\u27s contouring

Measuring the capability to raise revenue process and output dimensions and their application to the Zambia revenue authority

The worldwide diffusion of the good governance agenda and new public management has triggered a renewed focus on state capability and, more specifically, on the capability to raise revenue in developing countries. However, the analytical tools for a comprehensive understanding of the capability to raise revenue remain underdeveloped. This article aims at filling this gap and presents a model consisting of the three process dimensions ‘information collection and processing’, ‘merit orientation’ and ‘administrative accountability’. ‘Revenue performance’ constitutes the fourth capability dimension which assesses tax administration’s output. This model is applied to the case of the Zambia Revenue Authority. The dimensions prove to be valuable not only for assessing the how much but also the how of collecting taxes. They can be a useful tool for future comparative analyses of tax administrations’ capabilities in developing countries.Die weltweite Verbreitung der Good-Governance- und New-Public-Management-Konzepte hat zu einer zunehmenden Konzentration auf staatliche Leistungsfähigkeit und, im Besonderen, auf die Leistungsfähigkeit der Steuererhebung in Entwicklungsländern geführt. Allerdings bleiben die analytischen Werkzeuge für ein umfassendes Verständnis von Leistungsfähigkeit unterentwickelt. Dieser Artikel stellt hierfür ein Modell vor, das die drei Prozess-Dimensionen „Sammeln und Verarbeiten von Informationen“, „Leistungsorientierung der Mitarbeiter“ und „Verantwortlichkeit der Verwaltung“ beinhaltet. „Einnahmeperformanz“ ist die vierte Dimension und erfasst den Output der Steuerverwaltung. Das mehrdimensionale Modell wird für die Analyse der Leistungsfähigkeit der Steuerbehörde Zambias (Zambia Revenue Authority) genutzt. Es erweist sich nicht nur für die Untersuchung des Wieviel, sondern auch des Wie des Erhebens von Steuern als wertvoll. Die vier Dimensionen können in Zukunft zur umfassenden und vergleichenden Analyse der Leistungsfähigkeit verschiedener Steuerverwaltungen in Entwicklungsländern genutzt werden

Deep Learning-Based Dose Prediction To Improve the Plan Quality of Volumetric Modulated Arc Therapy for Gynecologic Cancers

Background: In recent years, deep‐learning models have been used to predict entire three‐dimensional dose distributions. However, the usability of dose predictions to improve plan quality should be further investigated. Purpose: To develop a deep‐learning model to predict high‐quality dose distributions for volumetric modulated arc therapy (VMAT) plans for patients with gynecologic cancer and to evaluate their usability in driving plan quality improvements. Methods: A total of 79 VMAT plans for the female pelvis were used to train (47 plans), validate (16 plans), and test (16 plans) 3D dense dilated U‐Net models to predict 3D dose distributions. The models received the normalized CT scan, dose prescription, and target and normal tissue contours as inputs. Three models were used to predict the dose distributions for plans in the test set. A radiation oncologist specializing in the treatment of gynecologic cancers scored the test set predictions using a 5‐point scale (5, acceptable as‐is; 4, prefer minor edits; 3, minor edits needed; 2, major edits needed; and 1, unacceptable). The clinical plans for which the dose predictions indicated that improvements could be made were reoptimized with constraints extracted from the predictions. Results: The predicted dose distributions in the test set were of comparable quality to the clinical plans. The mean voxel‐wise dose difference was −0.14 ± 0.46 Gy. The percentage dose differences in the predicted target metrics of D1% and D98% were −1.05% ± 0.59% and 0.21% ± 0.28%, respectively. The dose differences in the predicted organ at risk mean and maximum doses were −0.30 ± 1.66 Gy and −0.42 ± 2.07 Gy, respectively. A radiation oncologist deemed all of the predicted dose distributions clinically acceptable; 12 received a score of 5, and four received a score of 4. Replanning of flagged plans (five plans) showed that the original plans could be further optimized to give dose distributions close to the predicted dose distributions. Conclusions: Deep‐learning dose prediction can be used to predict high‐quality and clinically acceptable dose distributions for VMAT female pelvis plans, which can then be used to identify plans that can be improved with additional optimization

Deep Learning-Based Dose Prediction for Automated, Individualized Quality Assurance of Head and Neck Radiation Therapy Plans

PURPOSE: This study aimed to use deep learning-based dose prediction to assess head and neck (HN) plan quality and identify suboptimal plans. METHODS AND MATERIALS: A total of 245 volumetric modulated arc therapy HN plans were created using RapidPlan knowledge-based planning (KBP). A subset of 112 high-quality plans was selected under the supervision of an HN radiation oncologist. We trained a 3D Dense Dilated U-Net architecture to predict 3-dimensional dose distributions using 3-fold cross-validation on 90 plans. Model inputs included computed tomography images, target prescriptions, and contours for targets and organs at risk (OARs). The model\u27s performance was assessed on the remaining 22 test plans. We then tested the application of the dose prediction model for automated review of plan quality. Dose distributions were predicted on 14 clinical plans. The predicted versus clinical OAR dose metrics were compared to flag OARs with suboptimal normal tissue sparing using a 2 Gy dose difference or 3% dose-volume threshold. OAR flags were compared with manual flags by 3 HN radiation oncologists. RESULTS: The predicted dose distributions were of comparable quality to the KBP plans. The differences between the predicted and KBP-planned D CONCLUSIONS: Deep learning can predict high-quality dose distributions, which can be used as comparative dose distributions for automated, individualized assessment of HN plan quality

Automated Contouring and Planning in Radiation Therapy: What Is \u27Clinically Acceptable\u27?

Developers and users of artificial-intelligence-based tools for automatic contouring and treatment planning in radiotherapy are expected to assess clinical acceptability of these tools. However, what is \u27clinical acceptability\u27? Quantitative and qualitative approaches have been used to assess this ill-defined concept, all of which have advantages and disadvantages or limitations. The approach chosen may depend on the goal of the study as well as on available resources. In this paper, we discuss various aspects of \u27clinical acceptability\u27 and how they can move us toward a standard for defining clinical acceptability of new autocontouring and planning tools