PURPOSE:The purpose of the Radiotherapy Dataset (RTDS) is to collect consistent and comparable data across all providers of National Health Service (NHS)-funded radiotherapy and to provide intelligence for service planning, commissioning, clinical practice and research.PARTICIPANTS:The RTDS is a mandated dataset requiring providers to collect and submit data monthly for patients treated in England. Data is available from 01 April 2009 to 2 months behind the calendar month.The National Disease Registration Service (NDRS) started receiving data from 01 April 2016. Prior to this, the National Clinical Analysis and Specialised Applications Team (NATCANSAT) were responsible for the RTDS. NDRS holds a copy of the NATCANSAT data for English NHS providers.The RTDS contains clinical information on the primary disease being treated, modality and intent of treatment, dose fractionation and hospital appointment details. Due to constraints in RTDS coding, linkage to the English National Cancer Registration dataset is beneficial.FINDINGS TO DATE:The RTDS has been linked to the English National Cancer Registration and Systemic Anti-Cancer Therapy (SACT) datasets and to Hospital Episode Statistics (HES) to provide a more complete picture of the patient cancer pathway. Findings include a study to compare outcomes for patients treated with radical radiotherapy, an investigation of factors influencing 30-day mortality, assessing sociodemographic variation in the use of treatment and a study to assess the service impact of the COVID-19 pandemic. A range of other studies have been completed or are ongoing currently.FUTURE PLANS:The RTDS can be used for a variety of functions including cancer epidemiological studies to investigate inequalities in treatment access; provide service planning intelligence; monitor clinical practice; and support clinical trial design and recruitment. Collection is to continue indefinitely, with regular updates to the data specification to enable capture of more detailed information on radiotherapy planning and delivery.

Background The indirect impact of the COVID-19 pandemic on cancer outcomes is of increasing concern. However, the extent to which key treatment modalities have been affected is unclear. We aimed to assess the impact of the pandemic on radiotherapy activity in England. Methods In this population-based study, data relating to all radiotherapy delivered for cancer in the English NHS, between Feb 4, 2019, and June 28, 2020, were extracted from the National Radiotherapy Dataset. Changes in mean weekly radiotherapy courses, attendances (reflecting fractions), and fractionation patterns following the start of the UK lockdown were compared with corresponding months in 2019 overall, for specific diagnoses, and across age groups. The significance of changes in radiotherapy activity during lockdown was examined using interrupted time-series (ITS) analysis. Findings In 2020, mean weekly radiotherapy courses fell by 19.9% in April, 6.2% in May, and 11.6% in June compared with corresponding months in 2019. A relatively greater fall was observed for attendances (29.1% in April, 31.4% in May, and 31.5% in June). These changes were significant on ITS analysis (p<0.0001). A greater reduction in treatment courses between 2019 and 2020 was seen for patients aged 70 years or older compared with those aged younger than 70 years (34.4% vs 7.3% in April). By diagnosis, the largest reduction from 2019 to 2020 in treatment courses was for prostate cancer (77.0% in April) and non-melanoma skin cancer (72.4% in April). Conversely, radiotherapy courses in April, 2020, compared with April, 2019, increased by 41.2% in oesophageal cancer, 64.2% in bladder cancer, and 36.3% in rectal cancer. Increased use of ultra-hypofractionated (26 Gy in five fractions) breast radiotherapy as a percentage of all courses (0.2% in April, 2019, to 60.6% in April, 2020; ITS p<0.0001) contributed to the substantial reduction in attendances. Interpretation Radiotherapy activity fell significantly, but use of hypofractionated regimens rapidly increased in the English NHS during the first peak of the COVID-19 pandemic. An increase in treatments for some cancers suggests that radiotherapy compensated for reduced surgical activity. These data will assist health-care providers in understanding the indirect consequences of the pandemic and the role of radiotherapy services in minimising these consequences. Copyright (C) 2021 Elsevier Ltd. All rights reserved.

We developed and evaluated a novel inverse optimization (IO) model to estimate objective function weights from clinical dose-volume histograms (DVHs). These weights were used to solve a treatment planning problem to generate ‘inverse plans’ that had similar DVHs to the original clinical DVHs. Our methodology was applied to 217 clinical head and neck cancer treatment plans that were previously delivered at Princess Margaret Cancer Centre in Canada. Inverse plan DVHs were compared to the clinical DVHs using objective function values, dose-volume differences, and frequency of clinical planning criteria satisfaction. Median differences between the clinical and inverse DVHs were within 1.1 Gy. For most structures, the difference in clinical planning criteria satisfaction between the clinical and inverse plans was at most 1.4%. For structures where the two plans differed by more than 1.4% in planning criteria satisfaction, the difference in average criterion violation was less than 0.5 Gy. Overall, the inverse plans were very similar to the clinical plans. Compared with a previous inverse optimization method from the literature, our new inverse plans typically satisfied the same or more clinical criteria, and had consistently lower fluence heterogeneity. Overall, this paper demonstrates that DVHs, which are essentially summary statistics, provide sufficient information to estimate objective function weights that result in high quality treatment plans. However, as with any summary statistic that compresses three-dimensional dose information, care must be taken to avoid generating plans with undesirable features such as hotspots; our computational results suggest that such undesirable spatial features were uncommon. Our IO-based approach can be integrated into the current clinical planning paradigm to better initialize the planning process and improve planning efficiency. It could also be embedded in a knowledge-based planning or adaptive radiation therapy framework to automatically generate a new plan given a predicted or updated target DVH, respectively.

Current practice for treatment planning optimization can be both inefficient and time consuming. In this paper, we propose an automated planning methodology that aims to combine both explorative and prescriptive approaches for improving the efficiency and the quality of the treatment planning process. Given a treatment plan, our explorative approach explores trade-offs between different objectives and finds an acceptable region for objective function weights via inverse optimization. Intuitively, the shape and size of these regions describe how 'sensitive' a patient is to perturbations in objective function weights. We then develop an integer programming-based prescriptive approach that exploits the information encoded by these regions to find a set of five representative objective function weight vectors such that for each patient there exists at least one representative weight vector that can produce a high quality treatment plan. Using 315 patients from Princess Margaret Cancer Centre, we show that the produced treatment plans are comparable and, for [Formula: see text] of cases, improve upon the inversely optimized plans that are generated from the historical clinical treatment plans.

This paper offers best practice recommendations for the maintenance and retention of radiotherapy health records and technical information for cancer programmes. The recommendations are based on a review of the published and grey literature, feedback from key informants from seven countries and expert consensus. Ideally, complete health records should be retained for 5 years beyond the patient's lifetime, regardless of where they are created and maintained. Technical information constituting the radiotherapy plan should also be retained beyond the patient's lifetime for 5 years, including the primary images, contours of delineated targets and critical organs, dose distributions and other radiotherapy plan objects. There have been increased data storage and access requirements to support modern image-guided radiotherapy. Therefore, the proposed recommendations represent an ideal state of radiotherapy record retention to facilitate ongoing safe and effective care for patients as well as meaningful and informed retrospective research and policy development.

PurposeTo determine the value of preoperative adaptive radiotherapy (ART) for soft tissue sarcoma patients (STS) by modeling the dosimetric consequences of tumour volume changes (TVC) using different external beam radiotherapy techniques.Methods and materialsA subset of 22 STS patients from a recent trial (NCT00188175) underwent a repeat CT scan (CT2) prompted by TVC>1cm during IMRT; 14 tumours grew, 8 shrank. Conformal and conventional plans were modelled in addition to IMRT replicating original criteria from the initial planning dataset (CT1):95% PTV encompassed by 97% prescribed dose. CT1 RT parameters for all plans were applied to CT2 for dosimetric assessment of TVC. Co-registration of CT1 and CT2 permitted comparison of original and new contours.ResultsMean TVC was 45% for growing and 33% for the shrinking cohort with TVC prompting CT2 at a mean of 13 fractions. For growers, the lack of target coverage on CT2 was statistically significant but was adequate for shrinkers.ConclusionGTV expansion of >1cm during RT may result in target underdosage independent of RT technique. ART applied offline for TV increases >1cm is a practical adaptive strategy to ensure tumour coverage during RT. TV shrinkage may allow for normal tissue sparing, which should be investigated prospectively.

OBJECTIVE Craniospinal irradiation damages the white matter in children treated for medulloblastoma, but the treatment-intensity effects are unclear. In a cross-sectional retrospective study, the effects of treatment with the least intensive radiation protocol versus protocols that delivered more radiation to the brain, in addition to the effects of continuous radiation dose, on white matter architecture were evaluated. METHODS Diffusion tensor imaging was used to assess fractional anisotropy, mean diffusivity, radial diffusivity, and axial diffusivity. First, regional white matter analyses and tract-based spatial statistics were conducted in 34 medulloblastoma patients and 38 healthy controls. Patients were stratified according to those treated with 1) the least intensive radiation protocol, specifically reduced-dose craniospinal irradiation plus a boost to the tumor bed only (n = 17), or 2) any other dose and boost combination that delivered more radiation to the brain, which was also termed the "all-other-treatments" group (n = 17), and comprised patients treated with standard-dose craniospinal irradiation plus a posterior fossa boost, standard-dose craniospinal irradiation plus a tumor bed boost, or reduced-dose craniospinal irradiation plus a posterior fossa boost. Second, voxel-wise dose-distribution analyses were conducted on a separate cohort of medulloblastoma patients (n = 15). RESULTS The all-other-treatments group, but not the reduced-dose craniospinal irradiation plus tumor bed group, had lower fractional anisotropy and higher radial diffusivity than controls in all brain regions (all p < 0.05). The reduced-dose craniospinal irradiation plus tumor bed boost group had higher fractional anisotropy (p = 0.05) and lower radial diffusivity (p = 0.04) in the temporal region, and higher fractional anisotropy in the frontal region (p = 0.04), than the all-other-treatments group. Linear mixed-effects modeling revealed that the dose and age at diagnosis together 1) better predicted fractional anisotropy in the temporal region than models with either alone (p < 0.005), but 2) did not better predict fractional anisotropy in comparison with dose alone in the occipital region (p > 0.05). CONCLUSIONS Together, the results show that white matter damage has a clear association with increasing radiation dose, and that treatment with reduced-dose craniospinal irradiation plus tumor bed boost appears to preserve white matter in some brain regions.

S17 _________________________________________________________________________________________________________and between countries.2) This variation will relate to patient factors, disease-related factors, and treatment factors.3) That regional variation in need for radiotherapy for lung cancer predicted by the MALTHUS model will be greater than that seen with a benchmark approach to optimal utilization.Methods: The MALTHUS model for radiotherapy demand will be utilized in order to investigate factors associated with regional variation in need for radiotherapy for lung cancer patients.MALTHUS decision trees for 23 disease sites have been established.The proximal branches of the tree encodes for cancer site, stage distribution, and treatment modality indication.Distal branches contain detailed information about how radiation is delivered, such as fractionation.MALTHUS takes into account local variation in cancer incidence, disease stage, performance status, and co-morbidity.It draws on high-quality cancer incidence data collected from national Cancer Registries and the National Cancer Intelligence Network (NCIN).It simulates demand at local, regional and national levels, and draws comparisons with actual radiotherapy activity from the British National Health Service's (NHS) Radiotherapy Dataset (RTDS).The resulting data can be used to study the effects of differences in clinical opinion over best practice, and can assist local oncologists and service managers in developing and assessing business plans.We will update the British data with direct access to the National Cancer Intelligence Database that, from previous work, has been properly curated for the use of this model.Data will be stratified for age, tumor characteristics, stage distribution, and region subdivided at the county level.The model will then be adapted to the appropriate treatment indications and dose fractionations using national evidence based best practice guidelines.We will quantify the influence of patient-related factors (age, sex, comorbidity, functional status), disease-related factors (cancer incidence, cancer stage), and treatment factors (hypofractionation including usage of SBRT) on regional (NHS Primary Care Trust level) demand for radiotherapy for lung cancer.In univariate analysis, factors will be defined categorically and demand for radiotherapy by variable category will be described.The influence of these factors will be further investigated in univariate sensitivity analysis.The consequences of applying regional extremes in each variable's distribution on national radiotherapy demand will be considered.For multivariate sensitivity analysis, a Monte Carlo (MC) simulation method will be utilized.Confidence intervals from MC simulation will be compared against ranges of proposed evidence-based benchmarks to compare model performance and precision.Potential differences in the influence of key factors influencing demand may exist between countries.As a second phase, we anticipate to adapt the model to the population of lung cancer patients in Ontario for comparison of outcomes.Populationbased data from the Ontario Cancer Registry (OCR) will be utilized.This will include data on cancer incidence, and collaborative stage information.Data on age distribution by county from OCR will be used to estimate regional differences in performance status and comorbidity.Significance: A primary benefit of this model is its potential to elucidate what influences demand for radiotherapy patients.Demand, and by the same token, wait lists, are affected by an increase in the incidence of cancer, by the increase in the referral of patients for radiotherapy, and by an increase in dose fractionation per course of radiotherapy.All of these factors are reflected in the MALTHUS.As the public system has a fixed global budget and lacks the reserve needed to expand capacity quickly in radiotherapy, an accurate prediction of future demand is vital in our situation.Comparing Canadian and British data utilizing MALTHUS should increase generalizability in its projections and may identify national differences in the impact of key factors driving demand.Reliable, well-characterized models are needed, as it can take years in working closely with policy makers in order to influence officials to provide adequate capacity for high-precision radiotherapy.Radiation therapy has major oncological benefits, and small

Purpose:The increased sparing of normal tissues in intensity modulated proton therapy (IMPT) in pediatric brain tumor treatments should translate into improved neurocognitive outcomes. Models were used to estimate the intelligence quotient (IQ) and the risk of hearing loss 5 years post radiotherapy and to compare outcomes of proton against photon in pediatric brain tumors.Methods:Patients who had received intensity modulated radiotherapy (IMRT) were randomly selected from our retrospective database. The existing planning CT and contours were used to generate IMPT plans. The RBE‐corrected dose was calculated for both IMPT and IMRT. For each patient, the IQ was estimated via a Monte Carlo technique, whereas the reported incidence of hearing loss as a function of cochlear dose was used to estimate the probability of occurrence.Results:The integrated brain dose was reduced in all IMPT plans, translating into a gain of 2 IQ points on average for protons for the whole cohort at 5 years post‐treatment. In terms of specific diseases, the gains in IQ ranged from 0.8 points for medulloblastoma, to 2.7 points for craniopharyngioma. Hearing loss probability was evaluated on a per‐ear‐basis and was found to be systematically less for proton versus photon: overall 2.9% versus 7.2%.Conclusions:A method was developed to predict IQ and hearing outcomes in pediatric brain tumor patients on a case‐by‐case basis. A modest gain was systematically observed for proton in all patients. Given the uncertainties within the model used and our reinterpretation, these gains may be underestimated.

PurposePeer review of radiation treatment (RT) plans is a key component of quality assurance programs in radiation medicine. A 2011 current state assessment identified considerable variation in the percentage of RT plans peer reviewed across Ontario's 14 cancer centers. In response, Cancer Care Ontario launched an initiative to increase peer review of plans for patients receiving radical intent RT.MethodsThe initiative was designed consistent with the Kotter eight-step process for organizational transformation. A multidisciplinary team conducted site visits to promote and guide peer review and to develop education and implementation processes in collaboration with the centers. A centralized reporting infrastructure enabled the monitoring of the percentage of RT courses peer reviewed and the timing of peer review (before completion of 25% of treatment visits, after completion of > 25% treatment visits).ResultsThe initiative is ongoing, but early results indicate that the proportion of radical intent RT courses peer reviewed province wide increased from 43.5% (April 2013) to 68.0% (March 2015). This proportion is now a quality metric in Ontario and is publicly reported through the Cancer System Quality Index. The performance target for this metric was initially set at 50% (cases treated with radical intent) and revised to 60% in 2014. Provincial performance exceeded targets in both years (58.2% and 68.2%, respectively). Considerable variation was observed, however, in rates and timing of peer review among Cancer Care Ontario centers.ConclusionThis initiative demonstrates that a change management framework can be useful for planning and achieving substantial increases in jurisdictional peer review activities.

Purpose:To develop an automated planning methodology that exploits patient sensitivity to objective function weights.Methods:Given a treatment plan, we first create an acceptable treatment region that encompasses a set of treatment plans with similar clinical performance (e.g., +/−1% at V70Gy). We use inverse optimization to map this region in criterion space to the weight space and find a corresponding region of acceptable weight vectors (W). The shape and size of W describes how sensitive a patient is to perturbations in objective function weights. To exploit the information encoded by these regions, we approximate W for each patient by a polyhedron and we cluster patients using a novel integer programming model with cluster sizes from k=1,2,…,10. Each cluster centroid is a representative objective function weight vector and we use these weight vectors to generate k treatment plans for each patient (AUTO plans). Using 315 prostate cancer plans, we determine the number of patients that would have received an improved treatment plan using our automated approach.Results:Clustering patients into five groups produced a global set of representative weights such that for 88% of patients there exists at least one AUTO plan that improves upon the clinical treatment plan in terms of organ‐at‐risk mean dose and clinical acceptability criteria satisfaction (i.e., V54Gy50% and V70Gy30%). The AUTO plans provided bladder or rectum mean dose improvement over the clinical treatment plans for 296 (94%) patients, bladder mean dose improvement for 185 (59%) patients, rectum mean dose improvement for 273 (87%) patients, and mean dose improvements for both bladder and rectum in 162 (51%) patients. The AUTO plans provided fewer violations for bladder/rectum V54Gy50% and slightly more for bladder/rectum V70Gy30% when compared to clinical plans.Conclusion:A method combining inverse optimization and clustering automatically produces prostate treatment plans for 88% of patients.

PURPOSE To determine how training set size affects the accuracy of knowledge-based treatment planning (KBP) models. METHODS The authors selected four models from three classes of KBP approaches, corresponding to three distinct quantities that KBP models may predict: dose-volume histogram (DVH) points, DVH curves, and objective function weights. DVH point prediction is done using the best plan from a database of similar clinical plans; DVH curve prediction employs principal component analysis and multiple linear regression; and objective function weights uses either logistic regression or K-nearest neighbors. The authors trained each KBP model using training sets of sizes n = 10, 20, 30, 50, 75, 100, 150, and 200. The authors set aside 100 randomly selected patients from their cohort of 315 prostate cancer patients from Princess Margaret Cancer Center to serve as a validation set for all experiments. For each value of n, the authors randomly selected 100 different training sets with replacement from the remaining 215 patients. Each of the 100 training sets was used to train a model for each value of n and for each KBT approach. To evaluate the models, the authors predicted the KBP endpoints for each of the 100 patients in the validation set. To estimate the minimum required sample size, the authors used statistical testing to determine if the median error for each sample size from 10 to 150 is equal to the median error for the maximum sample size of 200. RESULTS The minimum required sample size was different for each model. The DVH point prediction method predicts two dose metrics for the bladder and two for the rectum. The authors found that more than 200 samples were required to achieve consistent model predictions for all four metrics. For DVH curve prediction, the authors found that at least 75 samples were needed to accurately predict the bladder DVH, while only 20 samples were needed to predict the rectum DVH. Finally, for objective function weight prediction, at least 10 samples were needed to train the logistic regression model, while at least 150 samples were required to train the K-nearest neighbor methodology. CONCLUSIONS In conclusion, the minimum required sample size needed to accurately train KBP models for prostate cancer depends on the specific model and endpoint to be predicted. The authors' results may provide a lower bound for more complicated tumor sites.

To determine the feasibility of multi-institutional Soft tissue sarcoma (STS) Real Time Radiotherapy Quality Assurance (RT QA) Rounds, standardize collected metrics and process, and report our initial experience on the efficiency and effectiveness of these rounds. Regional centers involved in multidisciplinary STS RT treatment were invited to attend biweekly RT QA rounds, which aligned with Provincial Sarcoma Services guidelines. Data was reviewed from July to December 2015. Real time review was conducted according to provincial privacy regulations using a tele-videoconferencing network approved by all participating institutions. Radical and complex palliative cases were discussed. Metrics collected include number of cases reviewed, timeliness of review, case complexity (follows protocol or highly individualized plan), intent/management, prescription, target volumes, Organs at Risk (OARs), pre-tx and on-tx imaging, DVHs, and RT plan. "Learning moments" were defined as significant discussion about an issue that led to improved team knowledge, standardization of practice, and/or contributed to practice improvement. Discussion included issues on practice/management, dose/fractionation, plan quality and DVHs, target volumes and RT technique. The group must approve or suggest minor/major plan adjustments, and reach a consensus for each case reviewed. Plan improvements were recorded. Three large provincial centers consistently attend RTQA rounds. Seventy-two cases were presented during the study timeframe (75% of all cases). Seventy-four percent of cases were reviewed prior to RT or within one week from the onset of RT. The remainder were reviewed over one week from the start of RT. Table 1 summarizes peer review metrics discussed and 'Learning moments'. 4 plan adjustments have resulted; 3 CTV modifications and one addition of bolus.Tabled 1Abstract 54; Table 1Peer review metrics% of cases discussed (radical and complex palliative)Intent/Management92Prescription100Targets100OARs99Pre-tx Imaging93On-Tx Imaging1DVHs99Plan97LEARNING MOMENTS:Practice/management29Dose/fractionation54Plan Quality32Target Volume51Technique34 Open table in a new tab Multi-institutional STS real time RTQA rounds increase the critical mass of specialists evaluating treatment plans for a rare disease. They are feasible and are now routine practice. Chosen quality metrics' effectiveness was demonstrated by discussion in 92-100% of cases, excepting on-treatment imaging. 'Learning moments' discussion reflects successful knowledge translation and communication among centers engaged in this provincial initiative. 4 clinically important changes were made before the commencement of RT. Efficiency metrics are expected to improve with automated RTQA programs which are being explored.

The beam orientation optimization (BOO) problem for intensity-modulated radiation therapy (IMRT) is the selection of beams for radiation delivery. Conventionally, it is desirable for beams to be spatially separated to ensure a homogeneous dose. However, many BOO approaches yield clustered beams. This issue is especially prevalent for total marrow irradiation (TMI), where the target is very large and spread throughout the patient's body. Based on previous set-cover formulations of the BOO problem for TMI-IMRT, we propose an extension that enforces geometric beam constraints by iteratively removing beams violating geometric constraints within the set-cover framework. After beams are selected, they are used as input to a fluence map optimization solver to obtain optimal fluence maps. Results for a clinical TMI case meet clinical guidelines for target coverage and differentiation of organ and target doses.

Treatment planning systems (TPS) are a cornerstone of modern radiation therapy. Errors in their commissioning or use can have a devastating impact on many patients. To support safe and high quality care, medical physicists must conduct efficient and proper commissioning, good clinical integration, and ongoing quality assurance (QA) of the TPS. AAPM Task Group 53 and related publications have served as seminal benchmarks for TPS commissioning and QA over the past two decades. Over the same time, continuing innovations have made the TPS even more complex and more central to the clinical process. Medical goals are now expressed in terms of the dose and margins around organs and tissues that are delineated from multiple imaging modalities (CT, MR and PET); and even temporally resolved (i.e., 4D) imaging. This information is passed on to optimization algorithms to establish accelerator movements that are programmed directly for IMRT, VMAT and stereotactic treatments. These advances have made commissioning and QA of the TPS much more challenging. This education session reviews up-to-date experience and guidance on this subject; including the recently published AAPM Medical Physics Practice Guideline (MPPG) #5 “Commissioning and QA of Treatment Planning Dose Calculations: Megavoltage Photon and Electron Beams”. Treatment Planning System Commissioning and QA: Challenges and Opportunities (Greg Salomons) This session will provide some key background and review publications describing prominent incidents relating to TPS commissioning and QA. Traditional approaches have been hardware and feature oriented. They aim to establish a functional configuration and establish specifications for regular testing of features (like dose calculation) to assure stable operation and detect failures. With the advent of more complex systems, more patient-specific testing has also been adopted. A number of actual TPS defects will be presented along with heuristics for identifying similar defects in the future. Finally, the Gamma test has become a popular metric for reporting TPS Commissioning and QA results. It simplifies complex testing into a numerical index, but noisy data and casual application can make it misleading. A brief review of the issues around the use of the Gamma test will be presented. TPS commissioning and QA: A process orientation and application of control charts (Michael Sharpe) A framework for commissioning a treatment planning system will be presented, focusing on preparations, practical aspects of configuration, priorities, specifications, and establishing performance. The complexity of the modern TPS make modular testing of features inadequate, and modern QA tools can provide “too much information” about the performance of techniques like IMRT and VMAT. We have adopted a process orientation and quality tools, like control charts, for ongoing TPS QA and assessment of patient-specific tests. The trending nature of these tools reveals the overall performance of the TPS system, and quantifies the variations that arise from individual plans, discrete calculations, and experimentation based on discrete measurements. Examples demonstrating application of these tools to TPS QA will be presented. TPS commissioning and QA: Incorporating the entire planning process (Sasa Mutic) The TPS and its features do not perform in isolation. Instead, the features and modules are key components in a complex process that begins with CT Simulation and extends to treatment delivery, along with image guidance and verification. Most importantly, the TPS is used by people working in a multi-disciplinary environment. It is very difficult to predict the outcomes of human interactions with software. Therefore, an interdisciplinary approach to training, commissioning and QA will be presented, along with an approach to the physics chart check and end-to-end testing as a tool for TPS QA. The role of standardization and automation in QA will also be discussed. The recommendations of MPPG #5 and practical implementation strategies (Jennifer Smilowitz) The recently published recommendations from Task Group No. 244, Medical Physics Practice Guideline on Commissioning and QA of Treatment Planning Dose Calculations: Megavoltage Photon and Electron Beams will be presented. The recommendations focus on the validation of commissioning data and dose calculations. Tolerance values for non-IMRT beam configurations are summarized based on established criteria and data collected by the IROC. More stringent evaluation criteria for IMRT dose calculations are suggested to test the limitations of the TPS dose algorithms for advanced delivery conditions. The MPPG encourages users to create a suite of validation tests for dose calculation for various conditions for static photon beams, heterogeneities, IMRT/VMAT and electron beams. This test suite is intended to be used for subsequent testing, including TPS software upgrades. In the past, the recommendations of some reports have not been widely implemented due to practical limitations. Implementation strategies, tools and processes developed by multiple centers for efficient and “do-able” MPPG #5 testing will be presented, as well as a discussion on the overall validation experience. Learning Objectives: 1. Identify some of the key documents relevant for TPS commissioning and QA 2. Understand strategies for testing TPS software 3. Gain a practical knowledge of the Gamma test criteria 4. Increase familiarity with the process of commissioning a TPS 5. Learn about the use of Control Charts for TPS QA 6. Review the role of the TPS in the overall planning process 7. Increase awareness of the link between TPS QA and chart checking 8. Gain an increased appreciation for the importance of interdisciplinary communication 9. Understand the new recommendations from MPPG #5 on TPS Dose Algorithm Commissioning and QC/QA 10. Learn practical implementation processes and tools for MPPG #5 validation recommendations

Arguing against the Proposition is Kevin L. Moore, Ph.D. Dr. Moore obtained his Ph.D. in Physics from the University of California, Berkeley, subsequently training and working at Washington University in St. Louis before moving back west to the University of California, San Diego. He is certified in Therapeutic Radiological Physics by the American Board of Radiology and is currently Associate Physics Director and Medical Physics Residency Director in the UC San Diego Department of Radiation Medicine and Applied Sciences. Dr. Moore's major research interests lie in knowledge-based treatment planning, treatment plan quality control, and informatics applications in clinical radiotherapy. He has published nearly 30 peer-reviewed papers and one book chapter, and is lead inventor on a patent regarding knowledge-based dosimetric prediction. Within a decade, radiation treatment planning will become fully automated without the need for human intervention because (i) we will exploit pertinent trends in the manufacturing and informatics industries, (ii) the precedent is already established, and (iii) it is imperative to improving quality and continuing advancements in care. Impressive technological advances have occurred in radiation oncology over the past two decades. Image-based planning, optimization, and guidance progressed rapidly from compelling concepts to routine tools because outside influences like high-performance computing, networking, and robotics became widespread and affordable. IMRT and IGRT have become ubiquitous tools and have altered the paradigm of treatment. But we wish to do more for our patients. The "adaptive" concept was also described more than 15 years ago. The concept has been developed extensively and now includes biologically motivated adaptation.1 However, more effort is needed to overcome the complexities and impact on workflow to realize adaptation as it was conceived. Future efforts will benefit surely from the "third wave of computing" from which image processing and information technologies will produce insights from large quantities of unstructured treatment planning data. The need to estimate dose distributions made automation central to the earliest developments of computerized treatment planning. Today, even more advanced functions are automated, such as image registration, organ delineation, and dose optimization. Using commercial tools, it is now possible to control workflow so as to fully create, evaluate and document a plan with minimal intervention.2–4 Interestingly, applications involving tangential breast irradiation remain controversial: In spite of the obvious improvements in personalization and efficiency afforded by IMRT and related automation techniques, modern innovation is discouraged because entrenched reimbursement guidelines confuse the technologies and the "modality" they enable.5 Providing healthcare is one of the most complex and demanding of human endeavors. Radiation oncology treatment relies on distributed decisions and tasks that are shared across highly skilled medical and technical staff. We strive to assess and respond to each patient's personal needs; but our tools, skills, and processes are stretched to the limit. Procedures can become error prone, sometimes with tragic and very public consequences.6 Consequently, practice guidance is limited to the structures and inspections required to achieve safety today.7 The dynamic nature of patients and their response to treatment were recognized long ago as a control problem. Adaptive control provides a means to account for anatomical and physiological variations and supports highly personalized treatment.1 Adaptive radiation therapy must become "more than safe." It must embrace a broader definition of quality to ensure that clinical decisions and technical procedures are evidence based, effective, equitable, timely, and highly tailored to each patient.8 A higher level of robust quality is required and we must do more than embrace automation. Robust quality is achieved by design rather than through organic innovation followed by inspection for quality control.9 Adaptation requires a framework to achieve robust quality that is safe, consistent, and highly customized. Within such a framework, care will become more complex unless automation is used to make it "merely complicated." As someone who intends to spend the next decade working to advance the proposition, I nonetheless contend that the odds of fully automated treatment planning being the norm in ten years' time must be rated as extremely unlikely. A close read of the proposition could make my task relatively easy, i.e., interpreting "fully automated" treatment planning to imply end-to-end automation, whereby all steps between radiotherapy simulation and first treatment are performed without human intervention. Impressive though the last decade has been for the field of autosegmentation, it strains credulity that a decade's time would be enough to herald a universal autosegmentation platform that not only identifies all normal anatomical structures across all imaging modalities but also flawlessly incorporates every patient's unique clinical circumstances into fully automated tumor volume contouring. Making my task somewhat more difficult, we could interpret the proposition to "merely" imply full automation from segmentation to treatment. Both my opponent3 and I10,11 have clinically implemented automated treatment planning using present-day technologies, and, undoubtedly, research and commercial offerings in this space will advance in the next ten years. However, we should appreciate the enormity of the challenge in effecting universal automation for all clinical scenarios. Using the impressive work of my opponent as an example, tangents in early-stage breast cancer can clearly be automated to a great effect, but I am skeptical that this algorithm can be easily extended to all breast cancer treatments, e.g., bilateral postimplant chest wall irradiation with internal mammary chain and axillary lymph node involvement, including electron scar boosts, for a patient with cardiac comorbidities. Such a case is both complicated and outside of normative experience, making the work of algorithmic development simultaneously more difficult, more time consuming, and less beneficial (in a utilitarian sense). As automated treatment planning advances, by necessity it will expand from common and standardized treatment sites to infrequent and nonstandardized cases. To automate everything we treat in radiotherapy will take time, and ten years is simply not enough of it. In fairness to the spirit of the proposition, I feel I should stake my own claim for 2024. Semiautomated (i.e., computer-assisted) treatment planning will be used in the large majority cases, with some form of knowledge-based and/or computer-aided multicriterial optimization removing most of the present-day human variability from the optimization process.12,13 The clinical expertise of humans will still be regularly employed to evaluate and adjust plans for patients whose circumstances fall outside of normative treatments. Automated software systems will be commonly available for online plan adaptation. Some reductions in treatment planning staff will occur, although job descriptions might expand to encompass increased needs in clinical informatics and adaptive plan management. Ironically, human-driven planning will probably retain the largest foothold in 3D-conformal/palliative treatments, where patient anatomical variations can be very large and nonstandard clinical considerations are a frequent occurrence. These changes will be breathtaking and practice altering, but will fall short of delivering fully automated treatment planning by the year 2024. As for 2034… I appreciate Dr. Moore's flexible viewpoint and the challenges he presents. Indeed, image segmentation is a major hurdle; contours are vital for communicating decisions and intent. We are poised to exploit vast stores of images and manually delineated organs,14 but current clinical practices may not provide what is required for algorithm training. We do build on "shifting sands" to some degree as technologies and practice standards evolve. But, the proposition does not "strain credulity" if manual contouring is approached with consensus and consistency. Dr. Moore believes clinical variation limits our capacity to automate planning. I agree to the extent that the "Pareto Rule" governs progress; i.e., 20% of our efforts will succeed for 80% of the cases. Clearly outliers require significant human effort, but reducing arbitrary variation and building anatomically related evidence to support continuing development could help. Dr. Moore also speculates that automation will reduce staffing, but concedes it could free skilled staff to add value to challenging cases and to advance appropriately personalized adaptation. As automation is introduced, it influences the tasks remaining, i.e., what staff are asked to do. Will we continue in familiar territory, or will new tasks differ qualitatively or become disconnected? We must also assure that it is possible to monitor and compensate for system deficiencies. If these deficiencies are ignored, there is a risk of new types of errors and system failures. In short, automation does not solve all problems.15 In his opening statement, Dr. Moore contends that the "odds" of fully automated treatment planning being the norm in ten years' time must be rated as extremely unlikely. In my opinion, the future should not be left to chance. We must move to achieve robust quality by design. I conclude by quoting Dr. Dennis Gabor, the Nobel Laureate who invented holography: "The future cannot be predicted, but futures can be invented."16 I absolutely align myself with the large portion of Dr. Sharpe's statement dedicated to how automation could improve radiotherapy, and thus will focus on the narrow portion of my opponent's argument that attempts to explain how automated treatment planning might come to pass. Relying on a third wave of computing to make this happen perhaps confuses more data with more knowledge. I would respond that analyzing prior information is a necessary but not sufficient condition, and while we must exploit prior information, we cannot expect that the mere possession of large quantities of data will herald miraculous gains. The recent advance of knowledge-based planning yielded useful predictions only when new techniques were brought to widely available data.2,11,13,17,18 Extending automation will rely on further research and development and, as argued in my opening statement, this will take time to expand to all clinical scenarios. As for the precedents that Dr. Sharpe introduced—image registration, organ delineation, and dose optimization—I would argue that none of these are yet fully automated. Image registration comes close, but in my experience automatic registrations are ultimately adjusted more often than not. I have contended (with great respect) that autosegmentation is not fully automated even after more than a decade's development. As for optimization, this is unfortunately the least automated of all, demanding further technological development to eliminate human-caused variability.12 Examples of full automation in radiotherapy are actually very few. One example is beam aperture definition, now automated by programmed multileaf collimators. Arguably the elimination of human block cutters did occur on a decade's timescale, but mere citation of this does not inform predictions for other technologies. In closing, I am not at all pessimistic about the future of automation in treatment planning; deployed in tandem with the clinical expertise of highly trained human beings, automation will improve patient care and make radiotherapy more efficient. We should, however, work toward this future with eyes open about the significant effort that remains to achieve fully automated treatment planning.