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BACKGROUND: Canadian health systems fare poorly in providing timely access to elective surgical care, which is crucial for quality, trust, and satisfaction. METHODS: We conducted a cross-sectional analysis of surgical wait times for adults receiving non-urgent cataract surgery, knee arthroplasty, hip arthroplasty, gallbladder surgery, and non-cancer uterine surgery in Ontario, Canada, between 2013 and 2019. We obtained data from the Wait Times Information System (WTIS) database. Inter- and intra-hospital and surgeon variations in wait time were described graphically with caterpillar plots. We used non-nested 3-level hierarchical random effects models to estimate variation partition coefficients, quantifying the proportion of wait time variance attributable to surgeons and hospitals. RESULTS: A total of 942,605 procedures at 107 healthcare facilities, conducted by 1,834 surgeons, were included in the analysis. We observed significant intra- and inter-provider variations in wait times across all five surgical procedures. Inter-facility median wait time varied between six-fold for gallbladder surgery and 15-fold for knee arthroplasty. Inter-surgeon variation was more pronounced, ranging from a 17-fold median wait time difference for cataract surgery to a 216-fold difference for non-cancer uterine surgery. The proportion of variation in wait times attributable to facilities ranged from 6.2% for gallbladder surgery to 23.0% for cataract surgery. In comparison, surgeon-related variation ranged from 16.0% for non-cancer uterine surgery to 28.0% for cataract surgery. IMPLICATIONS: There is extreme variability in surgical wait times for five common, high-volume, non-urgent surgical procedures. Strategies to address surgical wait times must address the variation between service providers through better coordination of supply and demand. Approaches such as single-entry models could improve surgical system performance.

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Purpose: Dose painting radiation therapy delivers a nonuniform dose to tumors to account for heterogeneous radiosensitivity. With recent and ongoing development of Gamma Knife machines making large-volume brain tumor treatments more practical, it is increasingly feasible to deliver dose painting treatments. The increased prescription complexity means automated treatment planning is greatly beneficial, and the impact of dose painting on stereotactic radiosurgery (SRS) plan quality has not yet been studied. This research investigates the plan quality achievable for Gamma Knife SRS dose painting treatments when using optimization techniques and automated isocenter placement in treatment planning. Methods and Materials: Dose painting prescription functions with varying parameters were applied to convert voxel image intensities to prescriptions for 10 sample cases. To study achievable plan quality and optimization, clinically placed isocenters were used with each dose painting prescription and optimized using a semi-infinite linear programming formulation. To study automated isocenter placement, a grassfire sphere-packing algorithm and a clinically available Leksell gamma plan isocenter fill algorithm were used. Plan quality for each optimized treatment plan was measured with dose painting SRS metrics. Results: Optimization can be used to find high quality dose painting plans, and plan quality is affected by the dose painting prescription method. Polynomial function prescriptions show more achievable plan quality than sigmoid function prescriptions even with high mean dose boost. Automated isocenter placement is shown as a feasible method for dose painting SRS treatment, and increasing the number of isocenters improves plan quality. The computational solve time for optimization is within 5 minutes in most cases, which is suitable for clinical planning. Conclusions: The impact of dose painting prescription method on achievable plan quality is quantified in this study. Optimization and automated isocenter placement are shown as possible treatment planning methods to obtain high quality plans.

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BACKGROUND: Dynamic treatment in Gamma Knife (GK) radiosurgery systems delivers radiation continuously with couch movement, as opposed to stationary step-and-shoot treatment where radiation is paused when moving between isocenters. Previous studies have shown the potential for dynamic GK treatment to give faster treatment times and improved dose conformity and homogeneity. However, these studies focused only on computational simulations and lack physical validation. PURPOSE: This study aims conduct dynamic treatment dosimetric validation with physical experimental measurements. The experiments aim to (1) address assumptions made with computational studies, such as the validity of treating a continuous path as discretised points, (2) investigate uncertainties in translating computed plans to actual treatment, and (3) determine ideal treatment planning parameters, such as interval distance for the path discretization, collimator change limitations, and minimum isocenter treatment times. METHODS: This study uses a GK ICON treatment delivery machine, and a motion phantom custom-made to attach to the machine's mask adapter and move in 1D superior-inferior motion. Phantom positioning is first verified through comparisons against couch motion and computed doses. For dynamic treatment experiments, the phantom is moved through a program that first reads the desired treatment plan isocenters' position, time, and collimator sizes, then carries out the motion continuously while the treatment machine delivers radiation. Measurements are done with increasing levels of complexity: varying speed, varying collimator sizes, varying both speed and collimator sizes, then extends the same measurements to simulated 2D motion by combining phantom and couch motion. Dose comparisons between phantom motion radiation measurements and either couch motion measurements or dose calculations are analyzed with 2 mm/2% and 1 mm/2% gamma indices, using both local and global gamma index calculations. RESULTS: Phantom positional experiments show a high accuracy, with global gamma indices for all dose comparisons ≥ $\ge $ 99%. Discretization level to approximate continuous path as discrete points show the good dose matches with dose calculations when using 1 and 2-mm gaps. Complex 1D motion, including varying speed, collimator sizes, or both, as well as 2D motion with the same complexities, all show good dose matches with dose calculations: the scores are ≥ $\ge $ 92.0% for the strictest 1 mm/2% local gamma index calculation, ≥ $\ge $ 99.8% for 2 mm/2% local gamma index, and ≥ $\ge $ 97.0% for all global gamma indices. Five simulated 2D treatments with optimized plans scored highly as well, with all gamma index scores ≥ $\ge $ 95.3% when compared to stationary treatment, and scores ≥ $\ge $ 97.9% when compared to plan calculated dose. CONCLUSIONS: Dynamic treatment computational studies are validated, with dynamic treatment shown to be physically feasible and deliverable with high accuracy. A 2-mm discretization level in treatment planning is proposed as the best option for shorter dose calculation times while maintaining dose accuracy. Our experimental method enables dynamic treatment measurements using the existing clinical workflow, which may be replicated in other centers, and future studies may include 2D or 3D motion experiments, or planning studies to further quantify potential indication-specific benefits.

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BACKGROUND: Equitable access to surgical care has clinical and policy implications. We assess the association between social disadvantage and wait times for elective surgical procedures in Ontario. METHODS: We conducted a cross-sectional analysis using administrative data sets of adults receiving nonurgent inguinal hernia repair, cholecystectomy, hip arthroplasty, knee arthroplasty, arthroscopy, benign uterine surgery and cataract surgery from April 2013 to December 2019. We assessed the relation between exceeding target wait times and the highest versus lowest quintile of marginalization dimensions by use of generalized estimating equations logistic regression. RESULTS: Of the 1 385 673 procedures included, 174 633 (12.6%) exceeded the target wait time. Adjusted analysis for cataract surgery found significantly increased odds of exceeding wait times for residential instability (adjusted odd ratio [OR] 1.16, 95% confidence interval [CI] 1.11-1.21) and recent immigration (adjusted OR 1.12, 95% CI 1.07-1.18). The highest deprivation quintile was associated with 18% (adjusted OR 1.18, 95% CI 1.12-1.24) and 20% (adjusted OR 1.20, 95% CI 1.12-1.28) increased odds of exceeding wait times for knee and hip arthroplasty, respectively. Residence in areas where higher proportions of residents self-identify as being part of a visible minority group was independently associated with reduced odds of exceeding target wait times for hip arthroplasty (adjusted OR 0.82, 95% CI 0.75-0.91), cholecystectomy (adjusted OR 0.68, 95% CI 0.59-0.79) and hernia repair (adjusted OR 0.65, 95% CI 0.56-0.77) with an opposite effect in benign uterine surgery (adjusted OR 1.28, 95% CI 1.17-1.40). INTERPRETATION: Social disadvantage had a small and inconsistent impact on receiving care within wait time targets. Future research should consider these differences as they relate to resource distribution and the organization of clinical service delivery.

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INTRODUCTION: Prediction of outcomes following allogeneic hematopoietic cell transplantation (HCT) remains a major challenge. Machine learning (ML) is a computational procedure that may facilitate the generation of HCT prediction models. We sought to investigate the prognostic potential of multiple ML algorithms when applied to a large single-center allogeneic HCT database. METHODS: Our registry included 2697 patients that underwent allogeneic HCT from January 1976 to December 2017, 45 pre-transplant baseline variables were included in the predictive assessment of each ML algorithm on overall survival (OS) as determined by area under the curve (AUC). Pre-transplant variables used in the EBMT machine learning study (Shouval et al, 2015) were used as a benchmark for comparison. RESULTS: On the entire dataset, the random forest (RF) algorithm performed best (AUC 0.71±0.04) compared to the second-best model, logistic regression (LR) (AUC=0.69±0.04) (p<0.001). Both algorithms demonstrated improved AUC scores using all 45 variables compared to the limited variables examined by the EBMT study. Survival at 100 days post-HCT using RF on the full dataset discriminated patients into different prognostic groups with different 2-year OS (p<0.0001). We then examined the ML methods that allow for significant individual variable identification, including LR and RF, and identified matched related donors (HR=0.49, p<0.0001), increasing TBI dose (HR=1.60, p=0.006), increasing recipient age (HR=1.92, p<0.0001), higher baseline Hb (HR=0.59, p=0.0002) and increased baseline FEV1 (HR=0.73, p=0.02), among others. CONCLUSION: The application of multiple ML techniques on single center allogeneic HCT databases warrants further investigation and may provide a useful tool to identify variables with prognostic potential.

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International deployment of remote monitoring and virtual care (RMVC) technologies would efficiently harness their positive impact on outcomes. Since Canada and the United Kingdom have similar populations, health care systems, and digital health landscapes, transferring digital health innovations between them should be relatively straightforward. Yet examples of successful attempts are scarce. In a workshop, we identified 6 differences that may complicate RMVC transfer between Canada and the United Kingdom and provided recommendations for addressing them. These key differences include (1) minority groups, (2) physical geography, (3) clinical pathways, (4) value propositions, (5) governmental priorities and support for digital innovation, and (6) regulatory pathways. We detail 4 broad recommendations to plan for sustainability, including the need to formally consider how highlighted country-specific recommendations may impact RMVC and contingency planning to overcome challenges; the need to map which pathways are available as an innovator to support cross-country transfer; the need to report on and apply learnings from regulatory barriers and facilitators so that everyone may benefit; and the need to explore existing guidance to successfully transfer digital health solutions while developing further guidance (eg, extending the nonadoption, abandonment, scale-up, spread, sustainability framework for cross-country transfer). Finally, we present an ecosystem readiness checklist. Considering these recommendations will contribute to successful international deployment and an increased positive impact of RMVC technologies. Future directions should consider characterizing additional complexities associated with global transfer.

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Purpose: As radiation therapy treatment precision increases with advancements in imaging and radiation delivery, dose painting treatment becomes increasingly feasible, where targets receive a nonuniform radiation dose. The high precision of stereotactic radiosurgery (SRS) makes it a good candidate for dose painting treatments, but no suitable metrics to assess dose painting SRS plans exist. Existing dose painting assessment metrics weigh target overdose and underdose equally but are unsuited for SRS plans, which typically avoid target underdose more. Current SRS metrics also prioritize reducing healthy tissue dose through selectivity and dose fall-off, and these metrics assume single prescriptions. We propose a set of metrics for dose painting SRS that would meet clinical needs and are calculated with nonuniform dose painting prescriptions. Methods and Materials: Sample dose painting SRS prescriptions are first created from Gamma Knife SRS cases, apparent diffusion coefficient magnetic resonance images, and various image-to-prescription functions. Treatment plans are found through semi-infinite linear programming optimization and using clinically determined isocenters, then assessed with existing and proposed metrics. Modified versions of SRS metrics are proposed, including coverage, selectivity, conformity, efficiency, and gradient indices. Quality factor, a current dose painting metric, is applied both without changes and with modifications. A new metric, integral dose ratio, is proposed as a measure of target overdose. Results: The merits of existing and modified metrics are demonstrated and discussed. A modified conformity index using mean or minimum prescription dose would be suitable for dose painting SRS with integral or maximum boost methods, respectively. Either modified efficiency index is a suitable replacement for the existing gradient index. Conclusions: The proposed modified SRS metrics are appropriate measures of plan quality for dose painting SRS plans and have the advantage of giving equal values as the original SRS metrics when applied to single-prescription plans.

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Although seasonal influenza disease spread is a spatio-temporal phenomenon, public surveillance systems aggregate data only spatially, and are rarely predictive. We develop a hierarchical clustering-based machine learning tool to anticipate flu spread patterns based on historical spatio-temporal flu activity, where we use historical influenza-related emergency department records as a proxy for flu prevalence. This analysis replaces conventional geographical hospital clustering with clusters based on both spatial and temporal distance between hospital flu peaks to generate a network illustrating whether flu spreads between pairs of clusters (direction) and how long that spread takes (magnitude). To overcome data sparsity, we take a model-free approach, treating hospital clusters as a fully-connected network, where arcs indicate flu transmission. We perform predictive analysis on the clusters' time series of flu ED visits to determine direction and magnitude of flu travel. Detection of recurrent spatio-temporal patterns may help policymakers and hospitals better prepare for outbreaks. We apply this tool to Ontario, Canada using a five-year historical dataset of daily flu-related ED visits, and find that in addition to expected flu spread between major cities/airport regions, we were able to illuminate previously unsuspected patterns of flu spread between non-major cities, providing new insights for public health officials. We showed that while a spatial clustering outperforms a temporal clustering in terms of the direction of the spread (81% spatial v. 71% temporal), the opposite is true in terms of the magnitude of the time lag (20% spatial v. 70% temporal).

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Radiotherapy is a common treatment modality for the treatment of cancer, where treatments must be carefully designed to deliver appropriate dose to targets while avoiding healthy organs. The comprehensive multi-disciplinary quality assurance (QA) process in radiotherapy is designed to ensure safe and effective treatment plans are delivered to patients. However, the plan QA process is expensive, often time-intensive, and requires review of large quantities of complex data, potentially leading to human error in QA assessment. We therefore develop an automated machine learning algorithm to identify 'acceptable' plans (plans that are similar to historically approved plans) and 'unacceptable' plans (plans that are dissimilar to historically approved plans). This algorithm is a supervised extension of projective adaptive resonance theory, called SuPART, that learns a set of distinctive features, and considers deviations from them indications of unacceptable plans. We test SuPART on breast and prostate radiotherapy datasets from our institution, and find that SuPART outperforms common classification algorithms in several measures of accuracy. When no falsely approved plans are allowed, SuPART can correctly auto-approve 34% of the acceptable breast and 32% of the acceptable prostate plans, and can also correctly reject 53% of the unacceptable breast and 56% of the unacceptable prostate plans. Thus, usage of SuPART to aid in QA could potentially yield significant time savings.

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The complexity of generating radiotherapy treatments demands a rigorous quality assurance (QA) process to ensure patient safety and to avoid clinically significant errors. Machine learning classifiers have been explored to augment the scope and efficiency of the traditional radiotherapy treatment planning QA process. However, one important gap in relying on classifiers for QA of radiotherapy treatment plans is the lack of understanding behind a specific classifier prediction. We develop explanation methods to understand the decisions of two automated QA classifiers: (1) a region of interest (ROI) segmentation/labeling classifier, and (2) a treatment plan acceptance classifier. For each classifier, a local interpretable model-agnostic explanation (LIME) framework and a novel adaption of team-based Shapley values framework are constructed. We test these methods in datasets for two radiotherapy treatment sites (prostate and breast), and demonstrate the importance of evaluating QA classifiers using interpretable machine learning approaches. We additionally develop a notion of explanation consistency to assess classifier performance. Our explanation method allows for easy visualization and human expert assessment of classifier decisions in radiotherapy QA. Notably, we find that our team-based Shapley approach is more consistent than LIME. The ability to explain and validate automated decision-making is critical in medical treatments. This analysis allows us to conclude that both QA classifiers are moderately trustworthy and can be used to confirm expert decisions, though the current QA classifiers should not be viewed as a replacement for the human QA process.

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Ontario has shown an increasing number of emergency department (ED) visits, particularly for mental health and addiction (MHA) complaints. Given the current opioid crises Canada is facing and the legalization of recreational cannabis in October 2018, the number of MHA visits to the ED is expected to grow even further. In face of these events, we examine capacity planning alternatives for the ED of an academic hospital in Toronto. We first quantify the volume of ED visits the hospital has received in recent years (from 2012 to 2016) and use forecasting techniques to predict future ED demand for the hospital. We then employ a discrete-event simulation model to analyze the impacts of the following scenarios: (a) increasing overall demand to the ED, (b) increasing or decreasing number of ED visits due to substance abuse, and (c) adjusting resource capacity to address the forecasted demand. Key performance indicators used in this analysis are the overall ED length of stay (LOS) and the total number of patients treated in the Psychiatric Emergency Services Unit (PESU) as a percentage of the total number of MHA visits. Our results showed that if resource capacity is not adjusted, ED LOS will deteriorate considerably given the expected growth in demand; programs that aim to reduce the number of alcohol and/or opioid visits can greatly aid in reducing ED wait times; the legalization of recreational use of cannabis will have minimal impact, and increasing the number of PESU beds can provide great aid in reducing ED pressure.

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OBJECTIVE: The objective of this study was to examine temporal trends in mental health visits to the emergency department (ED) and to determine differences in length of stay (LOS) between mental health visits and visits for non-mental health conditions. METHODS: A population-based retrospective study was conducted for patients who visited the ED of an academic hospital located in Toronto, ON, between fiscal years 2012 and 2016. Trends in the number of visits and descriptive statistics were calculated for both mental health and non-mental health groups. Quantile regression was used to compare the median and 90th percentile LOS. RESULTS: In five years, the absolute increase in the number of mental health visits to the ED was 55.7%. The 90th percentile LOS was similar for mental and non-mental health visits that were internally transferred (10.7 hours v. 8.3 hours) but significantly higher for those who were discharged (11.4 hours v. 7.3 hours), admitted (52.6 hours v. 29.3 hours), and externally transferred (21.9 hours v. 10.0 hours). After adjusting for other variables, the 90th percentile LOS was 3.3 hours longer for mental health visits resulting in discharge (p<0.001), 24.5 hours longer for those admitted (p<0.001), and 12.7 hours longer for those externally transferred (p<0.001). CONCLUSION: The number of mental health visits to the ED is linearly increasing over time, and the LOS in the ED is significantly longer for mental health visits for almost all discharge dispositions. Thus, systematic changes are needed to address the ED capacity to provide care for the growing mental health population.

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PURPOSE: To test the use of well-studied and widely used classification methods alongside newly developed data-filtering techniques specifically designed for imbalanced-data classification in order to demonstrate proof of principle for an automated radiation therapy (RT) quality assurance process on prostate cancer treatment. METHODS: A series of acceptable (majority class, n = 61) and erroneous (minority class, n = 12) RT plans as well as a disjoint set of acceptable plans used to develop features (n = 273) were used to develop a dataset for testing. A series of five widely used imbalanced-data classification algorithms were tested with a modularized guided undersampling procedure that includes ensemble-outlier filtering and normalized-cut sampling. RESULTS: Hybrid methods including either ensemble-outlier filtering or both filtering and normalized-cut sampling yielded the strongest performance in identifying unacceptable treatment plans. Specifically, five methods demonstrated superior performance in both area under the receiver operating characteristics curve and false positive rate when the true positive rate is equal to one. Furthermore, ensemble-outlier filtering significantly improved results in all but one hybrid method (p < 0.01). Finally, ensemble-outlier filtering methods identified four minority instances that were considered outliers in over 96% of cross-validation iterations. Such instances may be considered distinct planning errors and merit additional inspection, providing potential areas of improvement for the planning process. CONCLUSIONS: Traditional imbalanced-data classification methods combined with ensemble-outlier filtering and normalized-cut sampling provide a powerful framework for identifying erroneous RT treatment plans. The proposed methodology yielded strong classification performance and identified problematic instances with high accuracy.

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PURPOSE: Continuous dose delivery in radiation therapy treatments has been shown to decrease total treatment time while improving the dose conformity and distribution homogeneity over the conventional step-and-shoot approach. The authors develop an inverse treatment planning method for Gamma Knife® Perfexion™ that continuously delivers dose along a path in the target. METHODS: The authors' method is comprised of two steps: find a path within the target, then solve a mixed integer optimization model to find the optimal collimator configurations and durations along the selected path. Robotic path-finding techniques, specifically, simultaneous localization and mapping (SLAM) using an extended Kalman filter, are used to obtain a path that travels sufficiently close to selected isocentre locations. SLAM is novelly extended to explore a 3D, discrete environment, which is the target discretized into voxels. Further novel extensions are incorporated into the steering mechanism to account for target geometry. RESULTS: The SLAM method was tested on seven clinical cases and compared to clinical, Hamiltonian path continuous delivery, and inverse step-and-shoot treatment plans. The SLAM approach improved dose metrics compared to the clinical plans and Hamiltonian path continuous delivery plans. Beam-on times improved over clinical plans, and had mixed performance compared to Hamiltonian path continuous plans. The SLAM method is also shown to be robust to path selection inaccuracies, isocentre selection, and dose distribution. CONCLUSIONS: The SLAM method for continuous delivery provides decreased total treatment time and increased treatment quality compared to both clinical and inverse step-and-shoot plans, and outperforms existing path methods in treatment quality. It also accounts for uncertainty in treatment planning by accommodating inaccuracies.

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Patients undergoing a bone marrow stem cell transplant (BMT) face various risk factors. Analyzing data from past transplants could enhance the understanding of the factors influencing success. Records up to 120 measurements per transplant procedure from 1751 patients undergoing BMT were collected (Shariati Hospital). Collaborative filtering techniques allowed the processing of highly sparse records with 22.3% missing values. Ten-fold cross-validation was used to evaluate the performance of various classification algorithms trained on predicting the survival status. Modest accuracy levels were obtained in predicting the survival status (AUC = 0.69). More importantly, however, operations that had the highest chances of success were shown to be identifiable with high accuracy, e.g., 92% or 97% when identifying 74 or 31 recipients, respectively. Identifying the patients with the highest chances of survival has direct application in the prioritization of resources and in donor matching. For patients where high-confidence prediction is not achieved, assigning a probability to their survival odds has potential applications in probabilistic decision support systems and in combination with other sources of information.

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PURPOSE: The purpose of this work is to advance the two-step approach for Gamma Knife(®) Perfexion™ (PFX) optimization to account for dose homogeneity and overlap between the planning target volume (PTV) and organs-at-risk (OARs). METHODS: In the first step, a geometry-based algorithm is used to quickly select isocentre locations while explicitly accounting for PTV-OARs overlaps. In this approach, the PTV is divided into subvolumes based on the PTV-OARs overlaps and the distance of voxels to the overlaps. Only a few isocentres are selected in the overlap volume, and a higher number of isocentres are carefully selected among voxels that are immediately close to the overlap volume. In the second step, a convex optimization is solved to find the optimal combination of collimator sizes and their radiation duration for each isocentre location. RESULTS: This two-step approach is tested on seven clinical cases (comprising 11 targets) for which the authors assess coverage, OARs dose, and homogeneity index and relate these parameters to the overlap fraction for each case. In terms of coverage, the mean V99 for the gross target volume (GTV) was 99.8% while the V95 for the PTV averaged at 94.6%, thus satisfying the clinical objectives of 99% for GTV and 95% for PTV, respectively. The mean relative dose to the brainstem was 87.7% of the prescription dose (with maximum 108%), while on average, 11.3% of the PTV overlapped with the brainstem. The mean beam-on time per fraction per dose was 8.6 min with calibration dose rate of 3.5 Gy/min, and the computational time averaged at 205 min. Compared with previous work involving single-fraction radiosurgery, the resulting plans were more homogeneous with average homogeneity index of 1.18 compared to 1.47. CONCLUSIONS: PFX treatment plans with homogeneous dose distribution can be achieved by inverse planning using geometric isocentre selection and mathematical modeling and optimization techniques. The quality of the obtained treatment plans are clinically satisfactory while the homogeneity index is improved compared to conventional PFX plans.

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BACKGROUND: In Canadian hospitals, which are typically financed by global annual budgets, overuse of operating rooms is a financial risk that is frequently managed by cancelling elective surgical procedures. It is uncertain how different scheduling rules affect the rate of elective surgery cancellations. METHODS: We used discrete event simulation modelling to represent perioperative processes at a hospital in Toronto, Canada. We tested the effects of the following 3 scenarios on the number of surgical cancellations: scheduling surgeons' operating days based on their patients' average length of stay in hospital, sequencing surgical procedures by average duration and variance, and increasing the number of postsurgical ward beds. RESULTS: The number of elective cancellations was reduced by scheduling surgeons whose patients had shorter average lengths of stay in hospital earlier in the week, sequencing shorter surgeries and those with less variance in duration earlier in the day, and by adding up to 2 additional beds to the postsurgical ward. CONCLUSION: Discrete event simulation modelling can be used to develop strategies for improving efficiency in operating rooms.

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PURPOSE: The purpose of this work is to develop a framework to the inverse problem for radiosurgery treatment planning on the Gamma Knife(®) Perfexion™ (PFX) for intracranial targets. METHODS: The approach taken in the present study consists of two parts. First, a hybrid grassfire and sphere-packing algorithm is used to obtain shot positions (isocenters) based on the geometry of the target to be treated. For the selected isocenters, a sector duration optimization (SDO) model is used to optimize the duration of radiation delivery from each collimator size from each individual source bank. The SDO model is solved using a projected gradient algorithm. This approach has been retrospectively tested on seven manually planned clinical cases (comprising 11 lesions) including acoustic neuromas and brain metastases. RESULTS: In terms of conformity and organ-at-risk (OAR) sparing, the quality of plans achieved with the inverse planning approach were, on average, improved compared to the manually generated plans. The mean difference in conformity index between inverse and forward plans was -0.12 (range: -0.27 to +0.03) and +0.08 (range: 0.00-0.17) for classic and Paddick definitions, respectively, favoring the inverse plans. The mean difference in volume receiving the prescribed dose (V(100)) between forward and inverse plans was 0.2% (range: -2.4% to +2.0%). After plan renormalization for equivalent coverage (i.e., V(100)), the mean difference in dose to 1 mm(3) of brainstem between forward and inverse plans was -0.24 Gy (range: -2.40 to +2.02 Gy) favoring the inverse plans. Beam-on time varied with the number of isocenters but for the most optimal plans was on average 33 min longer than manual plans (range: -17 to +91 min) when normalized to a calibration dose rate of 3.5 Gy/min. In terms of algorithm performance, the isocenter selection for all the presented plans was performed in less than 3 s, while the SDO was performed in an average of 215 min. CONCLUSIONS: PFX inverse planning can be performed using geometric isocenter selection and mathematical modeling and optimization techniques. The obtained treatment plans all meet or exceed clinical guidelines while displaying high conformity.

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The beam orientation optimization (BOO) problem in intensity modulated radiation therapy (IMRT) treatment planning is a nonlinear problem, and existing methods to obtain solutions to the BOO problem are time consuming due to the complex nature of the objective function and size of the solution space. These issues become even more difficult in total marrow irradiation (TMI), where many more beams must be used to cover a vastly larger treatment area than typical site-specific treatments (e.g., head-and-neck, prostate, etc). These complications result in excessively long computation times to develop IMRT treatment plans for TMI, so we attempt to develop methods that drastically reduce treatment planning time. We transform the BOO problem into the classical set cover problem (SCP) and use existing methods to solve SCP to obtain beam solutions. Although SCP is NP-Hard, our methods obtain beam solutions that result in quality treatments in minutes. We compare our approach to an integer programming solver for the SCP to illustrate the speed advantage of our approach.

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