Quantification of the environmental impact of radiotherapy and associated secondary human health effects: a multi-institutional retrospective analysis and simulation.

BACKGROUND: The health-care industry is a substantial contributor to global greenhouse gas emissions, yet the specific environmental impact of radiotherapy, a cornerstone of cancer treatment, remains under-explored. We aimed to quantify the emissions associated with the delivery of radiotherapy in the USA and propose a framework for reducing the environmental impact of oncology care. METHODS: In this multi-institutional retrospective analysis and simulation study, we conducted a lifecycle assessment of external beam radiotherapy (EBRT) for ten anatomical disease sites, adhering to the International Organization for Standardization's standards ISO 14040 and ISO 14044. We analysed retrospective data from Jan 1, 2017, to Oct 1, 2023, encompassing patient and staff travel, medical supplies, and equipment and building energy use associated with the use of EBRT at four academic institutions in the USA. The primary objective was to measure the environmental impacts across ten categories: greenhouse gases (expressed as kg of carbon dioxide equivalents [CO2e]), ozone depletion, smog formation, acidification, eutrophication, carcinogenic and non-carcinogenic potential, respiratory effects, fossil fuel depletion, and ecotoxicity. Human health effects secondary to these environmental impacts were also estimated as disability-adjusted life years. We also assessed the potential benefits of hypofractionated regimens for breast and genitourinary (ie, prostate and bladder) cancers on US greenhouse gas emissions using an analytic model based on the 2014 US National Cancer Database for fractionation patterns and patient commute distances. FINDINGS: We estimated that the mean greenhouse gas emissions associated with a standard 25-fraction EBRT course were 4310 kg CO2e (SD 2910), which corresponded to 0·0035 disability-adjusted life years per treatment course. Transit and building energy usage accounted for 25·73% (1110 kg CO2e) and 73·95% of (3190 kg CO2e) of total greenhouse gas emissions, respectively, whereas supplies contributed only 0·32% (14 kg CO2e). Across the other environmental impact categories, most of the environmental impact also stemmed from patient transit and energy use within facilities, with little environmental impact contributed by supplies used. Hypofractionated treatment simulations suggested a substantial reduction in greenhouse gas emissions-by up to 42% for breast and 77% for genitourinary cancer-and environmental impacts more broadly. INTERPRETATION: This comprehensive lifecycle assessment of EBRT delineates the environmental and secondary health impacts of radiotherapy, and underscores the urgent need for sustainable practices in oncology. The findings serve as a reference for future decarbonisation efforts in cancer care and show the potential environmental benefits of modifying treatment protocols (when clinical equipoise exists). They also highlight strategic opportunities to mitigate the ecological footprint in an era of escalating climate change and increasing cancer prevalence. FUNDING: Mount Zion Health Fund.

Tracking and Reducing SF6 Usage in Radiation Oncology: A Step Toward Net-Zero Health Care Emissions.

Sulfur hexafluoride (SF6) is a widely used insulating gas in medical linear accelerators (LINACs) due to its high dielectric strength, heat transfer capabilities, and chemical stability. However, its long lifespan and high Global Warming Potential (GWP) make it a significant contributor to the environmental impact of radiation oncology. SF6 has an atmospheric lifespan of 3200 years and a GWP 23,000 times that of carbon dioxide. The amount of SF6 that can be emitted through leakage from machines is also concerning. It is estimated that the approximate 15,042 LINACs globally may leak up to 64,884,185.9 carbon dioxide equivalent per year, which is the equivalent greenhouse gas emissions of 13,981 gasoline-powered passenger vehicles driven for 1 year. Despite being regulated as a greenhouse gas under the United Nations Framework Convention on Climate Change, SF6 use within health care is often exempt from regulation, and only a few states in the United States have specific SF6 management regulations. This article highlights the need for radiation oncology centers and LINAC manufacturers to take responsibility for minimizing SF6 emissions. Programs that track usage and disposal, conduct life-cycle assessments, and implement leakage detection can help identify SF6 sources and promote recovery and recycling. Manufacturers are investing in research and development to identify alternative gases, improve leak detection, and minimize SF6 gas leakage during operation and maintenance. Alternative gases with lower GWP, such as nitrogen, compressed air, and perfluoropropane, may be considered as replacements for SF6; however, more research is needed to evaluate their feasibility and performance in radiation oncology. The article emphasizes the need for all sectors, including health care, to reduce their emissions to meet the goals of the Paris Agreement and ensure the sustainability of health care and our patients. Although SF6 is practical in radiation oncology, its environmental impact and contribution to the climate crisis cannot be ignored. Radiation oncology centers and manufacturers must take responsibility for reducing SF6 emissions by implementing best practices and promoting research and development around alternatives. To meet global emissions reduction goals and protect both planetary and patient health, the reduction of SF6 emissions will be essential.

Evaluating the Environmental Impact of Radiation Therapy Using Life Cycle Assessments: A Critical Review.

Concurrent increases in global cancer burden and the climate crisis pose an unprecedented threat to public health and human well-being. Today, the health care sector greatly contributes to greenhouse gas emissions, with the future demand for health care services expected to rise. Life cycle assessment (LCA) is an internationally standardized tool that analyzes the inputs and outputs of products, processes, and systems to quantify associated environmental impacts. This critical review explains the use of LCA methodology and outlines its application to external beam radiation therapy (EBRT) with the aim of providing a robust methodology to quantify the environmental impact of radiation therapy care practices today. The steps of an LCA are outlined and explained as defined by the International Organization for Standardization (ISO 14040 and 14044) guidelines: (1) definition of the goal and scope of the LCA, (2) inventory analysis, (3) impact assessment, and (4) interpretation. The existing LCA framework and its methodology is described and applied to the field of radiation oncology. The goal and scope of its application to EBRT is the evaluation of the environmental impact of a single EBRT treatment course within a radiation oncology department. The methodology for data collection via mapping of the resources used (inputs) and the end-of-life processes (outputs) associated with EBRT is explained, with subsequent explanation of the LCA analysis steps. Finally, the importance of appropriate sensitivity analysis and the interpretations that can be drawn from LCA results are reviewed. This critical review of LCA protocol provides and evaluates a methodological framework to scientifically establish baseline environmental performance measurements within a health care setting and assists in identifying targets for emissions mitigation. Future LCAs in the field of radiation oncology and across medical specialties will be crucial in informing best practices for equitable and sustainable care in a changing climate.

Tungsten Filled 3-Dimensional Printed Lung Blocks for Total Body Irradiation.

PURPOSE: Lung blocks for total-body irradiation are commonly used to reduce lung dose and prevent radiation pneumonitis. Currently, molten Cerrobend containing toxic materials, specifically lead and cadmium, is poured into molds to construct blocks. We propose a streamlined method to create 3-dimensional (3D)-printed lung block shells and fill them with tungsten ball bearings to remove lead and improve overall accuracy in the block manufacturing workflow. METHODS AND MATERIALS: 3D-printed lung block shells were automatically generated using an inhouse software, printed, and filled with 2 to 3 mm diameter tungsten ball bearings. Clinical Cerrobend blocks were compared with the physician drawn blocks as well as our proposed tungsten filled 3D-printed blocks. Physical and dosimetric comparisons were performed on a linac. Dose transmission through the Cerrobend and 3D-printed blocks were measured using point dosimetry (ion-chamber) and the on-board Electronic-Portal-Imaging-Device (EPID). Dose profiles from the EPID images were used to compute the full-width-half-maximum and to compare with the treatment-planning-system. Additionally, the coefficient-of-variation in the central 80% of full-width-half-maximum was computed and compared between Cerrobend and 3D-printed blocks. RESULTS: The geometric difference between treatment-planning-system and 3D-printed blocks was significantly lower than Cerrobend blocks (3D: -0.88 ± 2.21 mm, Cerrobend: -2.28 ± 2.40 mm, P = .0002). Dosimetrically, transmission measurements through the 3D-printed and Cerrobend blocks for both ion-chamber and EPID dosimetry were between 42% to 48%, compared with the open field. Additionally, coefficient-of-variation was significantly higher in 3D-printed blocks versus Cerrobend blocks (3D: 4.2% ± 0.6%, Cerrobend: 2.6% ± 0.7%, P < .0001). CONCLUSIONS: We designed and implemented a tungsten filled 3D-printed workflow for constructing total-body-irradiation lung blocks, which serves as an alternative to the traditional Cerrobend based workflow currently used in clinics. This workflow has the capacity of producing clinically useful lung blocks with minimal effort to facilitate the removal of toxic materials from the clinic.

Operational Ontology for Oncology (O3): A Professional Society-Based, Multistakeholder, Consensus-Driven Informatics Standard Supporting Clinical and Research Use of Real-World Data From Patients Treated for Cancer.

PURPOSE: The ongoing lack of data standardization severely undermines the potential for automated learning from the vast amount of information routinely archived in electronic health records (EHRs), radiation oncology information systems, treatment planning systems, and other cancer care and outcomes databases. We sought to create a standardized ontology for clinical data, social determinants of health, and other radiation oncology concepts and interrelationships. METHODS AND MATERIALS: The American Association of Physicists in Medicine's Big Data Science Committee was initiated in July 2019 to explore common ground from the stakeholders' collective experience of issues that typically compromise the formation of large inter- and intra-institutional databases from EHRs. The Big Data Science Committee adopted an iterative, cyclical approach to engaging stakeholders beyond its membership to optimize the integration of diverse perspectives from the community. RESULTS: We developed the Operational Ontology for Oncology (O3), which identified 42 key elements, 359 attributes, 144 value sets, and 155 relationships ranked in relative importance of clinical significance, likelihood of availability in EHRs, and the ability to modify routine clinical processes to permit aggregation. Recommendations are provided for best use and development of the O3 to 4 constituencies: device manufacturers, centers of clinical care, researchers, and professional societies. CONCLUSIONS: O3 is designed to extend and interoperate with existing global infrastructure and data science standards. The implementation of these recommendations will lower the barriers for aggregation of information that could be used to create large, representative, findable, accessible, interoperable, and reusable data sets to support the scientific objectives of grant programs. The construction of comprehensive "real-world" data sets and application of advanced analytical techniques, including artificial intelligence, holds the potential to revolutionize patient management and improve outcomes by leveraging increased access to information derived from larger, more representative data sets.

Prospective Clinical Validation of Virtual Patient-Specific Quality Assurance of Volumetric Modulated Arc Therapy Radiation Therapy Plans.

PURPOSE: Performing measurement-based patient-specific quality assurance (PSQA) is recognized as a resource-intensive and time inefficient task in the radiation therapy treatment workflow. Paired with technological refinements in modern radiation therapy, research toward measurement-free PSQA has seen increased interest during the past 5 years. However, these efforts have not been clinically implemented or prospectively validated in the United States. We propose a virtual QA (VQA) system and workflow to assess the safety and workload reduction of measurement-free PSQA. METHODS: An XGBoost machine learning model was designed to predict PSQA outcomes of volumetric modulated arc therapy plans, represented as percent differences between the measured ion chamber point dose in a phantom and the corresponding planned dose. The final model was deployed within a web application to predict PSQA outcomes of clinical plans within an existing clinical workflow. The application also displays relevant feature importance and plan-specific distribution analyses relative to database plans for documentation and to aid physicist interpretation and evaluation. VQA predictions were prospectively validated over 3 months of measurements at our clinic to assess safety and efficiency gains. RESULTS: Over 3 months, VQA predictions for 445 volumetric modulated arc therapy plans were prospectively validated at our institution. VQA predictions for these plans had a mean absolute error of 1.08% ± 0.77%, with a maximum absolute error of 2.98%. Using a 1% prediction threshold (ie, plans predicted to have an absolute error <1% would not require a measurement) would yield a 69.2% reduction in QA workload, saving 32.5 hours per month on average, with 81.5% sensitivity, 72.4% specificity, and an area under the curve of 0.81 at a 3% clinical threshold and 100% sensitivity, 70% specificity, and an area under the curve of 0.93 at a 4% clinical threshold. CONCLUSIONS: This is the first prospective clinical implementation and validation of VQA in the United States, which we observed to be efficient. Using a conservative threshold, VQA can substantially reduce the number of required measurements for PSQA, leading to more effective allocation of clinical resources.

Quality Assurance for AI-Based Applications in Radiation Therapy.

Recent advancements in artificial intelligence (AI) in the domain of radiation therapy (RT) and their integration into modern software-based systems raise new challenges to the profession of medical physics experts. These AI algorithms are typically data-driven, may be continuously evolving, and their behavior has a degree of (acceptable) uncertainty due to inherent noise in training data and the substantial number of parameters that are used in the algorithms. These characteristics request adaptive, and new comprehensive quality assurance (QA) approaches to guarantee the individual patient treatment quality during AI algorithm development and subsequent deployment in a clinical RT environment. However, the QA for AI-based systems is an emerging area, which has not been intensively explored and requires interactive collaborations between medical doctors, medical physics experts, and commercial/research AI institutions. This article summarizes the current QA methodologies for AI modules of every subdomain in RT with further focus on persistent shortcomings and upcoming key challenges and perspectives.

Integration of AI and Machine Learning in Radiotherapy QA.

The use of machine learning and other sophisticated models to aid in prediction and decision making has become widely popular across a breadth of disciplines. Within the greater diagnostic radiology, radiation oncology, and medical physics communities promising work is being performed in tissue classification and cancer staging, outcome prediction, automated segmentation, treatment planning, and quality assurance as well as other areas. In this article, machine learning approaches are explored, highlighting specific applications in machine and patient-specific quality assurance (QA). Machine learning can analyze multiple elements of a delivery system on its performance over time including the multileaf collimator (MLC), imaging system, mechanical and dosimetric parameters. Virtual Intensity-Modulated Radiation Therapy (IMRT) QA can predict passing rates using different measurement techniques, different treatment planning systems, and different treatment delivery machines across multiple institutions. Prediction of QA passing rates and other metrics can have profound implications on the current IMRT process. Here we cover general concepts of machine learning in dosimetry and various methods used in virtual IMRT QA, as well as their clinical applications.

Gold nanoparticles in radiation research: potential applications for imaging and radiosensitization.

The potential of gold nanoparticles (GNPs) in therapeutic and diagnostic cancer applications is becoming increasingly recognized. These biologically compatible particles can be easily synthesized, tuned to different sizes, and functionalized by conjugation to various biologically useful materials. Efficient and specific delivery to tumor tissue can then be accomplished either by passive accumulation in leaky tumor vessels and tissue, or by directly targeting tumor-specific biomarkers. Tumor-localized GNPs can serve as both adjuvants for enhancing the efficacy of radiation therapy and also as contrast agents for various imaging modalities. In this review, we will discuss recent advancements and future potential in the application of GNP as both a radiosensitizer and an imaging contrast agent. Due to their versatility and biocompatibility, gold nanoparticles may represent a novel theranostic adjuvant for radiation applications in cancer management.