J Palta

The report of Task Group 100 of the AAPM: Application of risk analysis methods to radiation therapy quality management.

The increasing complexity of modern radiation therapy planning and delivery challenges traditional prescriptive quality management (QM) methods, such as many of those included in guidelines published by organizations such as the AAPM, ASTRO, ACR, ESTRO, and IAEA. These prescriptive guidelines have traditionally focused on monitoring all aspects of the functional performance of radiotherapy (RT) equipment by comparing parameters against tolerances set at strict but achievable values. Many errors that occur in radiation oncology are not due to failures in devices and software; rather they are failures in workflow and process. A systematic understanding of the likelihood and clinical impact of possible failures throughout a course of radiotherapy is needed to direct limit QM resources efficiently to produce maximum safety and quality of patient care. Task Group 100 of the AAPM has taken a broad view of these issues and has developed a framework for designing QM activities, based on estimates of the probability of identified failures and their clinical outcome through the RT planning and delivery process. The Task Group has chosen a specific radiotherapy process required for intensity modulated radiation therapy (IMRT) as a case study. The goal of this work is to apply modern risk-based analysis techniques to this complex RT process in order to demonstrate to the RT community that such techniques may help identify more effective and efficient ways to enhance the safety and quality of our treatment processes. The task group generated by consensus an example quality management program strategy for the IMRT process performed at the institution of one of the authors. This report describes the methodology and nomenclature developed, presents the process maps, FMEAs, fault trees, and QM programs developed, and makes suggestions on how this information could be used in the clinic. The development and implementation of risk-assessment techniques will make radiation therapy safer and more efficient.

Monitor unit calculations for external photon and electron beams: Report of the AAPM Therapy Physics Committee Task Group No. 71

A protocol is presented for the calculation of monitor units (MU) for photon and electron beams, delivered with and without beam modifiers, for constant source-surface distance (SSD) and source-axis distance (SAD) setups. This protocol was written by Task Group 71 of the Therapy Physics Committee of the American Association of Physicists in Medicine (AAPM) and has been formally approved by the AAPM for clinical use. The protocol defines the nomenclature for the dosimetric quantities used in these calculations, along with instructions for their determination and measurement. Calculations are made using the dose per MU under normalization conditions, D0, that is determined for each users photon and electron beams. For electron beams, the depth of normalization is taken to be the depth of maximum dose along the central axis for the same field incident on a water phantom at the same SSD, where D0 = 1 cGy/MU. For photon beams, this task group recommends that a normalization depth of 10 cm be selected, where an energy-dependent D0 ≤ 1 cGy/MU is required. This recommendation differs from the more common approach of a normalization depth of dm, with D0 = 1 cGy/MU, although both systems are acceptable within the current protocol. For photon beams, the formalism includes the use of blocked fields, physical or dynamic wedges, and (static) multileaf collimation. No formalism is provided for intensity modulated radiation therapy calculations, although some general considerations and a review of current calculation techniques are included. For electron beams, the formalism provides for calculations at the standard and extended SSDs using either an effective SSD or an air-gap correction factor. Example tables and problems are included to illustrate the basic concepts within the presented formalism.

International Outreach: What Is the Responsibility of ASTRO and the Major International Radiation Oncology Societies?

In this era of globalization and rapid advances in radiation oncology worldwide, the American Society for Radiation Oncology (ASTRO) is committed to help decrease profound regional disparities through the work of the International Education Subcommittee (IES). The IES has expanded its base, reach, and activities to foster educational advances through a variety of educational methods with broad scope, in addition to committing to the advancement of radiation oncology care for cancer patients around the world, through close collaboration with our sister radiation oncology societies and other educational, governmental, and organizational groups.

American Association of Physicists in Medicine Task Group 263: Standardizing Nomenclatures in Radiation Oncology

A substantial barrier to the single- and multi-institutional aggregation of data to supporting clinical trials, practice quality improvement efforts, and development of big data analytics resource systems is the lack of standardized nomenclatures for expressing dosimetric data. To address this issue, the American Association of Physicists in Medicine (AAPM) Task Group 263 was charged with providing nomenclature guidelines and values in radiation oncology for use in clinical trials, data-pooling initiatives, population-based studies, and routine clinical care by standardizing: (1) structure names across image processing and treatment planning system platforms; (2) nomenclature for dosimetric data (eg, dose–volume histogram [DVH]-based metrics); (3) templates for clinical trial groups and users of an initial subset of software platforms to facilitate adoption of the standards; (4) formalism for nomenclature schema, which can accommodate the addition of other structures defined in the future. A multisociety, multidisciplinary, multinational group of 57 members representing stake holders ranging from large academic centers to community clinics and vendors was assembled, including physicists, physicians, dosimetrists, and vendors. The stakeholder groups represented in the membership included the AAPM, American Society for Radiation Oncology (ASTRO), NRG Oncology, European Society for Radiation Oncology (ESTRO), Radiation Therapy Oncology Group (RTOG), Children’s Oncology Group (COG), Integrating Healthcare Enterprise in Radiation Oncology (IHE-RO), and Digital Imaging and Communications in Medicine working group (DICOM WG); A nomenclature system for target and organ at risk volumes and DVH nomenclature was developed and piloted to demonstrate viability across a range of clinics and within the framework of clinical trials. The final report was approved by AAPM in October 2017. The approval process included review by 8 AAPM committees, with additional review by ASTRO, European Society for Radiation Oncology (ESTRO), and American Association of Medical Dosimetrists (AAMD). This Executive Summary of the report highlights the key recommendations for clinical practice, research, and trials.

Development of a golden beam data set for the commissioning of a proton double‐scattering system in a pencil‐beam dose calculation algorithm

PURPOSEnThe purpose of this investigation is to determine if a single set of beam data, described by a minimal set of equations and fitting variables, can be used to commission different installations of a proton double-scattering system in a commercial pencil-beam dose calculation algorithm.nnnMETHODSnThe beam model parameters required to commission the pencil-beam dose calculation algorithm (virtual and effective SAD, effective source size, and pristine-peak energy spread) are determined for a commercial double-scattering system. These parameters are measured in a first room and parameterized as function of proton energy and nozzle settings by fitting four analytical equations to the measured data. The combination of these equations and fitting values constitutes the golden beam data (GBD). To determine the variation in dose delivery between installations, the same dosimetric properties are measured in two additional rooms at the same facility, as well as in a single room at another facility. The difference between the room-specific measurements and the GBD is evaluated against tolerances that guarantee the 3D dose distribution in each of the rooms matches the GBD-based dose distribution within clinically reasonable limits. The pencil-beam treatment-planning algorithm is commissioned with the GBD. The three-dimensional dose distribution in water is evaluated in the four treatment rooms and compared to the treatment-planning calculated dose distribution.nnnRESULTSnThe virtual and effective SAD measurements fall between 226 and 257 cm. The effective source size varies between 2.4 and 6.2 cm for the large-field options, and 1.0 and 2.0 cm for the small-field options. The pristine-peak energy spread decreases from 1.05% at the lowest range to 0.6% at the highest. The virtual SAD as well as the effective source size can be accurately described by a linear relationship as function of the inverse of the residual energy. An additional linear correction term as function of RM-step thickness is required for accurate parameterization of the effective SAD. The GBD energy spread is given by a linear function of the exponential of the beam energy. Except for a few outliers, the measured parameters match the GBD within the specified tolerances in all of the four rooms investigated. For a SOBP field with a range of 15 g/cm2 and an air gap of 25 cm, the maximum difference in the 80%-20% lateral penumbra between the GBD-commissioned treatment-planning system and measurements in any of the four rooms is 0.5 mm.nnnCONCLUSIONSnThe beam model parameters of the double-scattering system can be parameterized with a limited set of equations and parameters. This GBD closely matches the measured dosimetric properties in four different rooms.

Treatment data and technical process challenges for practical big data efforts in radiation oncology

The term Big Data has come to encompass a number of concepts and uses within medicine. This paper lays out the relevance and application of large collections of data in the radiation oncology community. We describe the potential importance and uses in clinical practice. The important concepts are then described and how they have been or could be implemented are discussed. Impediments to progress in the collection and use of sufficient quantities of data are also described. Finally, recommendations for how the community can move forward to achieve the potential of big data in radiation oncology are provided.

Performance/outcomes data and physician process challenges for practical big data efforts in radiation oncology

It is an exciting time for big data efforts in radiation oncology. The use of big data to help aid both outcomes and decision-making research is becoming a reality. However, there are true challenges that exist in the space of gathering and utilizing performance and outcomes data. Here, we summarize the current state of big data in radiation oncology with respect to outcomes and discuss some of the efforts and challenges in radiation oncology big data.

SU-F-T-223: Radiotherapy Incident Reporting and Analysis System (RIRAS):Early Experience

BACKGROUND & PURPOSEnRIRAS is a web-based information system deployed on the Veterans Health Administration intranet in early 2014 to collect adverse events and good catch data; analyze the causes and contributing factors; and find ways to prevent future occurrences.nnnMATERIAL AND METHODSnIncident learning consists of a feedback loop which starts with reporting an event, followed by analysis of contributing factors, and culminates in the development of a patient safety work product (PSWP) to prevent recurrence. RIRAS permits both anonymous and non-anonymous reporting. Each report is analyzed by a team of medical physicists who are independent of the reporting facility. The analysts usually contact the reporting facilities for additional information. We analyzed all reports and held telephonic interviews (when necessary) with the reporters. We then generated PSWPs with corrective/preventive and learning actions. Anonymous reporting is handled in the same manner, except without the ability to further interview the reporter.nnnRESULTSnIn a significant number of reports, the causes and recommended preventive actions were considerably altered by the independent analysis and additional information from the facility. 130 reports have been entered in RIRAS; 9 misadministrations, 83 good catches, 3 anonymous good catches, and 35 earlier reported incidents from FY2005-14. 45% of the reported incidents occurred in the treatment delivery stages, 19% in on-treatment management, and 16% in pre-treatment verification. 80% of the good catches were found in the treatment delivery workflow. Majority of these incidents were due to inconsistent patient setup instructions or documentation, nonadherence to policies and procedures, lax time-out policy, distracted RTTs, and inadequate RTT staffing.nnnCONCLUSIONnRIRAS has identified many areas for improvement and elevated the quality and safety of radiation treatments in the VHA. We found that the ability to learn is significantly diminished when the analysts do not have the ability to request additional information.

A smart healthcare portal for clinical decision making and precision medicine

There has been an unprecedented generation of healthcare data at clinical practices. With the availability of advanced computing frameworks and the ability to electronically mine data from disparate sources (e.g. demographics, genetics, imaging, treatment, clinical decisions, and outcomes) big data research in medicine has become a very active field of interest. In this paper, we discuss the challenges associated with designing clinical decision support systems that try to leverage such disparate data sources and create smart healthcare tools to aid medical practitioners for better patient care and treatment plans. We next propose an integrated data curation, storage and analytics portal, called HINGE (the Health Information Gateway and Exchange application), that can effectively address many of the outstanding challenges in this domain. HINGE specifically caters to healthcare data from radiation oncology patients however, the underlying formalisms and principles, as discussed here, are readily extendible to other disease types making it an attractive tool for the design of next generation clinical decision support systems.

Quality Improvements of Veterans Health Administration Radiation Oncology Services Through Partnership for Accreditation With the ACR

Approximately 20,000 US veterans receive radiation oncology services at a Veterans Healthcare Administration (VHA) medical facility each year. They currently have access to advanced technologies, which include image-guided intensity-modulated radiotherapy, stereotactic radiosurgery, and stereotactic body radiation therapy. Although this provides access to cancer therapies that are modern, safe, and efficient, the technical complexities of these treatments and clinical decision making that goes into the patient selection and prescriptions demand quality assurances at each VHA practice. To meet the challenges of this need, the VHA established a partnership in 2008 with the ACRs Radiation Oncology Practice Accreditation Program (ACR-ROPA). This report summarizes the experience of this ongoing partnership and demonstrates the combined impact of the VHAs mandate for ACR-ROPA accreditation and internal monitoring of all identified corrective actions at each of its radiation oncology practices.