A TPS integrated machine learning tool for predicting patient-specific quality assurance outcomes in volumetric-modulated arc therapy.

PURPOSE: Machine learning (ML) models have been demonstrated to be beneficial for optimizing the workload of patient-specific quality assurance (PSQA). Implementing them in clinical routine frequently requires third-party applications beyond the treatment planning system (TPS), slowing down the workflow. To address this issue, a PSQA outcomes predictive model was carefully selected and validated before being fully integrated into the TPS. MATERIALS AND METHODS: Nine ML algorithms were evaluated using cross-validation. The learning database was built by calculating complexity metrics (CM) and binarizing PSQA results into "pass"/"fail" classes for 1767 VMAT arcs. The predictive performance was evaluated using area under the ROC curve (AUROC), sensitivity, and specificity. The ML model was integrated into the TPS via a C# script. Script-guided reoptimization impact on PSQA and dosimetric results was evaluated on ten VMAT plans with "fail"-predicted arcs. Workload reduction potential was also assessed. RESULTS: The selected model exhibited an AUROC of 0.88, with a sensitivity and specificity exceeding 50 % and 90 %, respectively. The script-guided reoptimization of the ten evaluated plans led to an average improvement of 1.4 ± 0.9 percentage points in PSQA results, while preserving the quality of the dose distribution. A yearly savings of about 140 h with the use of the script was estimated. CONCLUSIONS: The proposed script is a valuable complementary tool for PSQA measurement. It was efficiently integrated into the clinical workflow to enhance PSQA outcomes and reduce PSQA workload by decreasing the risk of failing QA and thereby, the need for repeated replanning and measurements.

Management of per- and polyfluoroalkyl substances (PFAS)-laden wastewater sludge in Maine: Perspectives on a wicked problem.

This article discusses the challenges and potential solutions for managing wastewater sludge that contains per- and polyfluoroalkyl substances (PFAS), using the experience in Maine as a guide toward addressing the issue nationally. Traditional wastewater treatment, designed to remove excess organic waste and nutrients, does not eliminate persistent toxic pollutants like PFAS, instead partitioning the chemicals between discharged effluent and the remaining solids in sludge. PFAS chemistry, the molecular size, the alkyl chain length, fluorine saturation, the charge of the head group, and the composition of the surrounding matrix influence PFAS partitioning between soil and water. Land application of sludge, incineration, and storage in a landfill are the traditional management options. Land application of Class B sludge on agricultural fields in Maine peaked in the 1990s, totaling over 2 × 106 cu yd over a 40-year period and has contaminated certain food crops and animal forage, posing a threat to the food supply and the environment. Additional Class A EQ (Exceptional Quality) composted sludge was also applied to Maine farmland. The State of Maine banned the land application of wastewater sludge in August 2022. Most sludge was sent to the state-owned Juniper Ridge Landfill, which accepted 94 270 tons of dewatered sludge in 2022, a 14% increase over 2019. Between 2019 and 2022, the sum of perfluorooctane sulfonate (PFOS) and perfluorooctanoic acid (PFOA) concentrations in sludge sent to the landfill ranged from 1.2 to 104.9 ng/g dw. In 2022, the landfill generated 71.6 × 106 l of leachate. The concentration of sum of six PFAS in the leachate increased sixfold between 2021 and 2022, reaching 2 441 ng/l. The retention of PFAS within solid-waste landfills and the potential for long-term release of PFAS through liners into groundwater require ongoing monitoring. Thermal treatment, incineration, or pyrolysis can theoretically mineralize PFAS at high temperatures, yet the strong C-F bond and reactivity of fluorine require extreme temperatures for complete mineralization. Future alternatives may include interim options such as preconditioning PFAS with nonpolar solvents prior to immobilization in landfills, removing PFAS from leachate, and interrupting the cycle of PFAS moving from landfill, via leachate, to wastewater treatment, and then back to the landfill via sludge. Long-term solutions may involve destructive technologies such as electron beam irradiation, electrochemical advanced oxidation, or hydrothermal liquefaction. The article highlights the need for innovative and sustainable solutions for managing PFAS-contaminated wastewater sludge.

The effects of recent minimum wage increases on self-reported health in the United States.

Minimum wage policy continues to receive considerable popular and legislative attention in the United States. Despite a fast-growing empirical literature on the relationship between the minimum wage and health, previous studies generally use data from the 1990s to 2014. In this study, we estimate the impact of recent changes in state-level minimum wages on the self-reported health of adults in the post-Great Recession era. Using data from the Behavioral Risk Factor Surveillance System and a difference-in-differences design, we exploit more than 150 state-level increases in the minimum wage between 2011 and 2019. Our results indicate that a higher minimum wage increases men's physical and mental health burdens but has an ambiguous effect on a more general measure of health. Among women, the minimum wage improves general health and reduces their physical and mental health burdens. Compared to past studies, our work indicates that the relationship between the minimum wage and health has changed over time. It also illustrates the need for policymakers to consider the broader consequences of minimum wage policy, including health effects and gender differences therein.

Implementation of volumetric-modulated arc therapy for locally advanced breast cancer patients: Dosimetric comparison with deliverability consideration of planning techniques and predictions of patient-specific QA results via supervised machine learning.

PURPOSE: The aim of this study was to implement a clinically deliverable VMAT planning technique dedicated to advanced breast cancer, and to predict failed QA using a machine learning (ML) model to optimize the QA workload. METHODS: For three planning methods (2A: 2-partial arc, 2AS: 2-partial arc with splitting, 4A: 4-partial arc), dosimetric results were compared with patient-specific QA performed with the electronic portal imaging device of the linac. A dataset was built with the pass/fail status of the plans and complexity metrics. It was divided into training and testing sets. An ML metamodel combining predictions from six base classifiers was trained on the training set to predict plans as 'pass' or 'fail'. The predictive performances were evaluated using the unseen data of the testing set. RESULTS: The dosimetric comparison highlighted that 4A was the highest dosimetric performant method but also the poorest performant in the QA process. 2AS spared the best heart, but provided the highest dose to the contralateral breast and lowest node coverage. 2A provides a dosimetric compromise between organ at risk sparing and PTV coverage with satisfactory QA results. The metamodel had a median predictive sensitivity of 73% and a median specificity of 91%. CONCLUSIONS: The 2A method was selected to calculate clinically deliverable VMAT plans; however, the 2AS method was maintained when the heart was of particular importance and breath-hold techniques were not applicable. The metamodel provides promising predictive performance, and it is intended to be improved as a larger dataset becomes available.

Exercise training improves radiotherapy efficiency in a murine model of prostate cancer.

Engaging in exercise while undergoing radiotherapy (RT) has been reported to be safe and achievable. The impact of exercise training (ET) on RT efficiency is however largely unknown. Our study aims to investigate the interactions between ET and RT on prostate cancer growth. Athymic mice received a subcutaneous injection of PPC-1 cells and were randomly assigned to either cancer control, cancer ET, cancer RT, or cancer RT combined with ET (CaRT-ET). Mice were sacrificed 24 days post-injection. All three intervention groups had reduced tumor size, the most important decrease being observed in CaRT-ET mice. Apoptotic marker cleaved caspase-3 was not modified by ET, but enhanced with RT. Importantly, this increase was the highest when the two strategies were combined. Furthermore, NK1.1 staining and gene expression of natural killer (NK) cell receptors Klrk1 and Il2rß were not affected by ET alone but were increased with RT, this effect being potentiated when combined with ET. Overall, our study shows that (a) ET enhances RT efficiency by potentiating NK cell infiltration, and (b) while ET alone and ET combined with RT both reduce tumor growth, the mechanisms mediating these effects are different.

A GATE/Geant4 Monte Carlo toolkit for surface dose calculation in VMAT breast cancer radiotherapy.

The accuracy of superficial dose calculations for breast cancer treatments with Volumetric Modulated Arc Therapy (VMAT) is of major importance. For target volumes close to the surface, the inverse dosimetric planning can lead to very high fluences in the build-up region to properly cover the volume to be treated. Various radiotherapy modalities are currently used in parallel with additional protocols to enable a better control on the dose delivery (bolus, target volume margins). One of the difficulties currently facing medical physicists is the lack of available tools to test the impact of these different solutions on the superficial dose distribution. We present a new open source toolkit to assist medical physicists in evaluating the 3D distributions of superficial dose in VMAT breast cancer treatments. This tool is based on the GATE Monte Carlo software, a Geant4 application dedicated to medical physics. A set of macros has been developed to simulate in an easy way a full VMAT plan from the information available in the DICOM-RT files (image, plan, structure and dose). The toolkit has been tested on a 6 MV Varian NovalisTx™ accelerator. The paper presents a precise comparison of 3D surface dose distributions from experimental measurements (EBT3 films), TPS (Varian Eclipse) and Monte Carlo simulation (GATE). The comparison made it possible to highlight both the TPS biases for the surface dose calculation and the good performances of the developed toolkit. The simulation of surface dose distributions on a real patient has also been performed to illustrate the potential clinical applications.

Comparison of Automated Atlas-Based Segmentation Software for Postoperative Prostate Cancer Radiotherapy.

Automated atlas-based segmentation (ABS) algorithms present the potential to reduce the variability in volume delineation. Several vendors offer software that are mainly used for cranial, head and neck, and prostate cases. The present study will compare the contours produced by a radiation oncologist to the contours computed by different automated ABS algorithms for prostate bed cases, including femoral heads, bladder, and rectum. Contour comparison was evaluated by different metrics such as volume ratio, Dice coefficient, and Hausdorff distance. Results depended on the volume of interest showed some discrepancies between the different software. Automatic contours could be a good starting point for the delineation of organs since efficient editing tools are provided by different vendors. It should become an important help in the next few years for organ at risk delineation.

An Agent-Based Model of Private Woodland Owner Management Behavior Using Social Interactions, Information Flow, and Peer-To-Peer Networks.

Privately owned woodlands are an important source of timber and ecosystem services in North America and worldwide. Impacts of management on these ecosystems and timber supply from these woodlands are difficult to estimate because complex behavioral theory informs the owner's management decisions. The decision-making environment consists of exogenous market factors, internal cognitive processes, and social interactions with fellow landowners, foresters, and other rural community members. This study seeks to understand how social interactions, information flow, and peer-to-peer networks influence timber harvesting behavior using an agent-based model. This theoretical model includes forested polygons in various states of 'harvest readiness' and three types of agents: forest landowners, foresters, and peer leaders (individuals trained in conservation who use peer-to-peer networking). Agent rules, interactions, and characteristics were parameterized with values from existing literature and an empirical survey of forest landowner attitudes, intentions, and demographics. The model demonstrates that as trust in foresters and peer leaders increases, the percentage of the forest that is harvested sustainably increases. Furthermore, peer leaders can serve to increase landowner trust in foresters. Model output and equations will inform forest policy and extension/outreach efforts. The model also serves as an important testing ground for new theories of landowner decision making and behavior.

Trends in U.S. consumers' safe handling and consumption of food and their risk perceptions, 1988 through 2010.

Although survey results measuring the safety of consumers' food handling and risky food consumption practices have been published for over 20 years, evaluation of trends is impossible because the designs of published studies are not comparable. The Food Safety Surveys used comparable methods to interview U.S. adults by telephone in 1988, 1993, 2001, 2006, and 2010 about food handling (i.e., cross-contamination prevention) and risky consumption practices (eating raw or undercooked foods from animals) and perceived risk from foodborne illness. Sample sizes ranged from 1,620 to 4,547. Responses were analyzed descriptively, and four indices measuring meat, chicken, and egg cross-contamination, fish cross-contamination, risky consumption, and risk perceptions were analyzed using generalized linear models. The extent of media coverage of food safety issues was also examined. We found a substantial improvement in food handling and consumption practices and an increase in perceived risk from foodborne illness between 1993 and 1998. All indices were stable or declined between 1998 and 2006. Between 2006 and 2010, the two safe food handling practice indices increased significantly, but risk perceptions did not change, and safe consumption declined. Women had safer food handling and consumption practices than men. The oldest and youngest respondents and those with the highest education had the least safe food handling behaviors. Changes in safety of practices over the survey years are consistent with the change in the number of media stories about food safety in the periods between surveys. This finding suggests that increased media attention to food safety issues may raise awareness of food safety hazards and increase vigilance in food handling by consumers.