Charles Mayo

RADIATION DOSE-VOLUME EFFECTS OF OPTIC NERVES AND CHIASM

Publications relating radiation toxicity of the optic nerves and chiasm to quantitative dose and dose-volume measures were reviewed. Few studies have adequate data for dose-volume outcome modeling. The risk of toxicity increased markedly at doses >60 Gy at approximately 1.8 Gy/fraction and at >12 Gy for single-fraction radiosurgery. The evidence is strong that radiation tolerance is increased with a reduction in the dose per fraction. Models of threshold tolerance were examined.

Radiation associated brainstem injury.

Publications relating brainstem radiation toxicity to quantitative dose and dose-volume measures derived from three-dimensional treatment planning were reviewed. Despite the clinical importance of brainstem toxicity, most studies reporting brainstem effects after irradiation have fewer than 100 patients. There is limited evidence relating toxicity to small volumes receiving doses above 60-64 Gy using conventional fractionation and no definitive criteria regarding more subtle dose-volume effects or effects after hypofractionated treatment. On the basis of the available data, the entire brainstem may be treated to 54 Gy using conventional fractionation using photons with limited risk of severe or permanent neurological effects. Smaller volumes of the brainstem (1-10 mL) may be irradiated to maximum doses of 59 Gy for dose fractions <or=2 Gy; however, the risk appears to increase markedly at doses >64 Gy.

Initial Experience With Volumetric IMRT (RapidArc) for Intracranial Stereotactic Radiosurgery

PURPOSE Initial experience with delivering frameless stereotactic radiotherapy (SRT) using volumetric intensity-modulated radiation therapy (IMRT) delivered with RapidArc is presented. METHODS AND MATERIALS Treatment details for 12 patients (14 targets) with a mean clinical target volume (CTV) of 12.8 ± 4.0 cm(3) were examined. Dosimetric indices for conformality, homogeneity, and dose gradient were calculated and compared with published results for other frameless, intracranial SRT techniques, including CyberKnife, TomoTherapy, and static-beam IMRT. Statistics on setup and treatment times and per patient dose validations were examined. RESULTS Dose indices compared favorably with other techniques. Mean conformality, gradient, and homogeneity index values were 1.10 ± 0.11, 64.9 ± 14.1, 1.083 ± 0.026, respectively. Median treatment times were 4.8 ± 1.7 min. CONCLUSION SRT using volumetric IMRT is a viable alternative to other techniques and enables short treatment times. This is anticipated to have a positive impact on radiobiological effect and for facilitating wider use of SRT.

Use of a realistic breathing lung phantom to evaluate dose delivery errors

PURPOSE To compare the effect of respiration-induced motion on delivered dose (the interplay effect) for different treatment techniques under realistic clinical conditions. METHODS A flexible resin tumor model was created using rapid prototyping techniques based on a computed tomography (CT) image of an actual tumor. Twenty micro-MOSFETs were inserted into the tumor model and the tumor model was inserted into an anthropomorphic breathing phantom. Phantom motion was programed using the motion trajectory of an actual patient. A four-dimensional CT image was obtained and several treatment plans were created using different treatment techniques and planning systems: Conformal (Eclipse), step-and-shoot intensity-modulated radiation therapy (IMRT) (Pinnacle), step-and-shoot IMRT (XiO), dynamic IMRT (Eclipse), complex dynamic IMRT (Eclipse), hybrid IMRT [60% conformal, 40% dynamic IMRT (Eclipse)], volume-modulated arc therapy (VMAT) [single-arc (Eclipse)], VMAT [double-arc (Eclipse)], and complex VMAT (Eclipse). The complex plans were created by artificially pushing the optimizer to give complex multileaf collimator sequences. Each IMRT field was irradiated five times and each VMAT field was irradiated ten times, with each irradiation starting at a random point in the respiratory cycle. The effect of fractionation was calculated by randomly summing the measured doses. The maximum deviation for each measurement point per fraction and the probability that 95% of the model tumor had dose deviations less than 2% and 5% were calculated as a function of the number of fractions. Tumor control probabilities for each treatment plan were calculated and compared. RESULTS After five fractions, measured dose deviations were less than 2% for more than 95% of measurement points within the tumor model for all plans, except the complex dynamic IMRT, step-and-shoot IMRT (XiO), complex VMAT, and single-arc VMAT plans. Reducing the dose rate of the complex IMRT plans from 600 to 200 MU/min reduced the dose deviations to less than 2%. Dose deviations were less than 5% after five fractions for all plans, except the complex single-arc VMAT plan. CONCLUSIONS Rapid prototyping techniques can be used to create realistic tumor models. For most treatment techniques, the dose deviations averaged out after several fractions. Treatments with unusually complicated multileaf collimator sequences had larger dose deviations. For IMRT treat-ments, dose deviations can be reduced by reducing the dose rate. For VMAT treatments, using two arcs instead of one is effective for reducing dose deviations.

Hybrid IMRT for Treatment of Cancers of the Lung and Esophagus

PURPOSE To report on a hybrid intensity-modulated radiation therapy (IMRT; static plus IMRT beams treated concurrently) technique for lung and esophageal patients to reduce the volume of lung treated to low doses while delivering a conformal dose distribution. METHODS Treatment plans were analyzed for 18 patients (12 lung and 6 esophageal). Patients were treated with a hybrid technique that concurrently combines static (approximately two-thirds dose) and IMRT (approximately one-third dose) beams. These plans were compared with conventional three-dimensional (3D; non-IMRT) plans and all IMRT plans using custom four- and five-field arrangements and nine equally spaced coplanar beams. Plans were optimized to reduce V13 and V5 values. Dose-volume histograms were calculated for the planning target volume, heart, and the ipsilateral, contralateral, and total lung. Lung volumes V5, V13, V20, V30; mean lung dose (MLD); and the generalized equivalent uniform dose (gEUD) were calculated for each plan. RESULTS Hybrid plans treated significantly smaller total and contralateral lung volumes with low doses than nine-field IMRT plans. Largest reductions were for contralateral lung V5, V13, and V20 values for lung (-11%, -15%, -7%) and esophageal (-16%, -20%, -7%) patients. Smaller reductions were found also for 3D and four- and five-field IMRT plans. MLD and gEUDs were similar for all plan types. The 3D plans treated much larger extra planning target volumes to prescribed dose levels. CONCLUSIONS Hybrid IMRT demonstrated advantages for reduction of low-dose lung volumes in the thorax for reducing low dose to lung while also reducing the potential magnitude of dose deviations due to intrafraction motion and small field calculation accuracy.

Information technology resource management in radiation oncology

The ever‐increasing data demands in a radiation oncology (RO) clinic require medical physicists to have a clearer understanding of information technology (IT) resource management issues. Clear lines of collaboration and communication among administrators, medical physicists, IT staff, equipment service engineers, and vendors need to be established. In order to develop a better understanding of the clinical needs and responsibilities of these various groups, an overview of the role of IT in RO is provided. This is followed by a list of IT‐related tasks and a resource map. The skill set and knowledge required to implement these tasks are described for the various RO professionals. Finally, various models for assessing ones IT resource needs are described. The exposition of ideas in this white paper is intended to be broad, in order to raise the level of awareness of the RO community; the details behind these concepts will not be given here and are best left to future task group reports. PACS number: 87.52.Tr, 87.53.St, 87.53.Xd, 87.90.+y

The big data effort in radiation oncology: Data mining or data farming?

Although large volumes of information are entered into our electronic health care records, radiation oncology information systems and treatment planning systems on a daily basis, the goal of extracting and using this big data has been slow to emerge. Development of strategies to meet this goal is aided by examining issues with a data farming instead of a data mining conceptualization. Using this model, a vision of key data elements, clinical process changes, technology issues and solutions, and role for professional societies is presented. With a better view of technology, process and standardization factors, definition and prioritization of efforts can be more effectively directed.

Overview of the American Society for Radiation Oncology–National Institutes of Health–American Association of Physicists in Medicine Workshop 2015: Exploring Opportunities for Radiation Oncology in the Era of Big Data

Big data research refers to the collection and analysis of large sets of data elements and interrelationships that are difficult to process with traditional methods. It can be considered a subspecialty of the medical informatics domain under data science and analytics. This approach has been used in many areas of medicine to address topics such as clinical care and quality assessment (1–3). The need for informatics research in radiation oncology emerged as an important initiative during the 2013 National Institutes of Health (NIH)–National Cancer Institute (NCI), American Society for Radiation Oncology (ASTRO), and American Association of Physicists in Medicine (AAPM) workshop on the topic “Technology for Innovation in Radiation Oncology” (4). Our existing clinical practice generates discrete, quantitative, and structured patient-specific data (eg, images, doses, and volumes) that position us well to exploit and participate in big data initiatives. The well-established electronic infrastructure within radiation oncology should facilitate the retrieval and aggregation of much of the needed data. With additional efforts to integrate structured data collection of patient outcomes and assessments into the clinical workflow, the field of radiation oncology has a tremendous opportunity to generate large, comprehensive patient-specific data sets (5). However, there are major challenges to realizing this goal. For example, existing data are presently housed across different platforms at multiple institutions and are often not stored in a standardized manner or with common terminologies to enable pooling of data. In addition, many important data elements are not routinely discretely captured in clinical practice. There are cultural, structural, and logistical challenges (eg, computer compatibility and workflow demands) that will make the dream of big data research difficult. The big data research workshop provided a forum for leaders in cancer registries, incident report quality-assurance systems, radiogenomics, ontology of oncology, and a wide range of ongoing big data and cloud computing development projects to interact with peers in radiation oncology to develop strategies to harness data for research, quality assessment, and clinical care. The workshop provided a platform to discuss items such as data capture, data infrastructure, and protection of patient confidentiality and to improve awareness of the wide-ranging opportunities in radiation oncology, as well as to enhance the potential for research and collaboration opportunities with NIH on big data initiatives. The goals of the workshop were as follows: To discuss current and future sources of big data for use in radiation oncology research, To identify ways to improve our current data collection methods by adopting new strategies used in fields outside of radiation oncology, and To consider what new knowledge and solutions big data research can provide for clinical decision support for personalized medicine.

How Can We Effect Culture Change Toward Data-Driven Medicine?

The promises of big data efforts for radiation oncology are founded on availability of large, accurate and complete data sets. Assuring that key elements can be routinely and automatically extracted from our current electronic records requires change in the approaches we use to entry and curation of data created as part of routine practice. Definition of common standards and clinically relevant key elements for aggregation and data exchange by ASTRO, AAPM, NIH and NCI are important enabling factors.

Lessons From Large-Scale Collection of Patient-Reported Outcomes: Implications for Big Data Aggregation and Analytics.

Awareness of patient reported outcomes (PROs) as vital elements for big data efforts to incorporate measures of patient’s quality of life (QOL) into analysis of efficacy of care is emerging[1,2]. PRO domains can include functional status, symptoms (intensity, frequency), satisfaction (with medication), multiple domains of well-being, and global satisfaction with life. Today, virtually all validity issues for PROs have been either resolved or clear guidelines have been established [3–5]. Although the value of using PRO’s has been established, the challenge remains to measure QOL through PRO with the observational rigor of any other vital sign or lab test, yet with the ease of administration and rapid processing the clinical environment demands (Figure 1). Open in a separate window Figure 1 Vision for use of PROs as part of clinical decision frameworks