IMRT QA using machine learning: A multi-institutional validation.

PurposeTo validate a machine learning approach to Virtual intensity-modulated radiation therapy (IMRT) quality assurance (QA) for accurately predicting gamma passing rates using different measurement approaches at different institutions.MethodsA Virtual IMRT QA framework was previously developed using a machine learning algorithm based on 498 IMRT plans, in which QA measurements were performed using diode-array detectors and a 3%local/3 mm with 10% threshold at Institution 1. An independent set of 139 IMRT measurements from a different institution, Institution 2, with QA data based on portal dosimetry using the same gamma index, was used to test the mathematical framework. Only pixels with ≥10% of the maximum calibrated units (CU) or dose were included in the comparison. Plans were characterized by 90 different complexity metrics. A weighted poison regression with Lasso regularization was trained to predict passing rates using the complexity metrics as input.ResultsThe methodology predicted passing rates within 3% accuracy for all composite plans measured using diode-array detectors at Institution 1, and within 3.5% for 120 of 139 plans using portal dosimetry measurements performed on a per-beam basis at Institution 2. The remaining measurements (19) had large areas of low CU, where portal dosimetry has a larger disagreement with the calculated dose and as such, the failure was expected. These beams need further modeling in the treatment planning system to correct the under-response in low-dose regions. Important features selected by Lasso to predict gamma passing rates were as follows: complete irradiated area outline (CIAO), jaw position, fraction of MLC leafs with gaps smaller than 20 or 5 mm, fraction of area receiving less than 50% of the total CU, fraction of the area receiving dose from penumbra, weighted average irregularity factor, and duty cycle.ConclusionsWe have demonstrated that Virtual IMRT QA can predict passing rates using different measurement techniques and across multiple institutions. Prediction of QA passing rates can have profound implications on the current IMRT process

Estimating offsets for avian displacement effects of anthropogenic impacts

Biodiversity offsetting, or compensatory mitigation, is increasingly being used in temperate grassland ecosystems to compensate for unavoidable environmental damage from anthropogenic developments such as transportation infrastructure, urbanization, and energy development. Pursuit of energy independence in the United States will expand domestic energy production. Concurrent with this increased growth is increased disruption to wildlife habitats, including avian displacement from suitable breeding habitat. Recent studies at energy-extraction and energy-generation facilities have provided evidence for behavioral avoidance and thus reduced use of habitat by breeding waterfowl and grassland birds in the vicinity of energy infrastructure. To quantify and compensate for this loss in value of avian breeding habitat, it is necessary to determine a biologically based currency so that the sufficiency of offsets in terms of biological equivalent value can be obtained. We describe a method for quantifying the amount of habitat needed to provide equivalent biological value for avifauna displaced by energy and transportation infrastructure, based on the ability to define five metrics: impact distance, impact area, pre-impact density, percent displacement, and offset density. We calculate percent displacement values for breeding waterfowl and grassland birds and demonstrate the applicability of our avian-impact offset method using examples for wind and oil infrastructure. We also apply our method to an example in which the biological value of the offset habitat is similar to the impacted habitat, based on similarity in habitat type (e.g., native prairie), geographical location, land use, and landscape composition, as well as to an example in which the biological value of the offset habitat is dissimilar to the impacted habitat. We provide a worksheet that informs potential users how to apply our method to their specific developments and a framework for developing decision-support tools aimed at achieving landscape-level conservation goals

Moving Forward as a Family: Crafting a 2-Generation Strategy for Central Texas, PRP 192

United Way for Greater Austin commissioned this policy research project to guide their focus on helping low socioeconomic families achieve greater financial stability through the development of a Two-Generation (2-Gen) strategy for the Central Texas region. Two-Gen programs emphasize the importance of education as a means for better economic outcomes. High-quality early childhood education programs allow children to make critical neural connections during a period of substantial growth and development, ultimately better preparing them for pre-kindergarten programs and academic success in subsequent years. Adults working low-paying jobs encounter barriers to career advancement due to lacking credentials or relevant education. It is not uncommon for parents working long hours for low wages to have at least one child in need of high-quality early childhood education, yet they are unable to enroll their child in such programs due to issues such as cost, transportation, and time away from work. Two-Gen programs seek to resolve the issues complicating this problem of financial instability by providing high-quality educational and training programs for both parents and children, which are even more effective when intentionally coordinated so that the family develops as a single unit in a positive direction. The research consisted of a literature review; a program scan at the local, state, and federal levels; and site visits within Austin, Dallas, and San Antonio, as well as Boston and Miami. Data collected specific to the Central Texas region include a labor market analysis, a needs assessment, and a mapping of current organizational assets. Obtaining and analyzing this data allowed the team to better understand 2-Gen program development, outcomes, impact measurements, and areas for improvement. The research team developed practical applications for the information collected, ultimately contributing to the proposed anti-poverty strategy through the intentional coordination of 2-Gen services by leveraging existing organizational assets to best address the area’s most salient needs. In addition, the team proposed an evaluation strategy involving cost-benefit equations, program evaluation metrics, and a screening tool to predict the likelihood of a program achieving successful outcomes. The report concludes with policy recommendations at the local, state, and federal levels, as well as a summary of the populations affected by financial instability and future directions for this field.United Way for Greater AustinPublic Affair

Aligning Capital With Mission: Lessons from the Annie E. Casey Foundation's Social Investment Program

The Annie E. Casey Foundation engaged InSight at Pacific Community Ventures to conduct the first comprehensive third-party evaluation of the SI Program, with research support from the Center for the Advancement of Social Entrepreneurship (CASE) at Duke University's Fuqua School of Business. The evaluation focused on the social impact of the SI Program and its impact measurement practices, and had the following objectives: ? Provide a comprehensive review of the social impact that has been achieved to date through the SI Program. ? Assess the systems and processes used by the SI Program to measure and report on its impact, identifying the SI Program's strengths in impact measurement and areas for improvement. ? Surface evidence-based findings and lessons that can assist the Foundation and other investors in rigorously examining and enhancing the social impact of their investments, in order to support the continued development of the impact investing field

Framework Programmable Platform for the Advanced Software Development Workstation (FPP/ASDW). Demonstration framework document. Volume 1: Concepts and activity descriptions

The Framework Programmable Software Development Platform (FPP) is a project aimed at effectively combining tool and data integration mechanisms with a model of the software development process to provide an intelligent integrated software development environment. Guided by the model, this system development framework will take advantage of an integrated operating environment to automate effectively the management of the software development process so that costly mistakes during the development phase can be eliminated. The Advanced Software Development Workstation (ASDW) program is conducting research into development of advanced technologies for Computer Aided Software Engineering (CASE)