A Cull

Older Driver Estimates of Driving Exposure Compared to In-Vehicle Data in the Candrive II Study

Objective: Most studies on older adults’ driving practices have relied on self-reported information. With technological advances it is now possible to objectively measure the everyday driving of older adults in their own vehicles over time. The purpose of this study was to examine the ability of older drivers to accurately estimate their kilometers driven over one year relative to objectively measured driving exposure. Methods: A subsample (n = 159 of 928; 50.9% male) of Candrive II participants (age ≥ 70 years of age) was used in these analyses based on strict criteria for data collected from questionnaires as well as an OttoView-CD Autonomous Data Logging Device installed in their vehicle, over the first year of the prospective cohort study. Results: Although there was no significant difference overall between the self-reported and objectively measured distance categories, only moderate agreement was found (weighted kappa = 0.57; 95% confidence interval, 0.47–0.67). Almost half (45.3%) chose the wrong distance category, and some people misestimated their distance driven by up to 20,000 km. Those who misjudged in the low mileage group (≤5000 km) consistently underestimated, whereas the reverse was found for those in the high distance categories (≥ 20,000); that is, they always overestimated their driving distance. Conclusions: Although self-reported driving distance categories may be adequate for studies entailing broad group comparisons, caution should be used in interpreting results. Use of self-reported estimates for individual assessments should be discouraged.

PARETO: A novel evolutionary optimization approach to multiobjective IMRT planning

PURPOSE In radiation therapy treatment planning, the clinical objectives of uniform high dose to the planning target volume (PTV) and low dose to the organs-at-risk (OARs) are invariably in conflict, often requiring compromises to be made between them when selecting the best treatment plan for a particular patient. In this work, the authors introduce Pareto-Aware Radiotherapy Evolutionary Treatment Optimization (pareto), a multiobjective optimization tool to solve for beam angles and fluence patterns in intensity-modulated radiation therapy (IMRT) treatment planning. METHODS pareto is built around a powerful multiobjective genetic algorithm (GA), which allows us to treat the problem of IMRT treatment plan optimization as a combined monolithic problem, where all beam fluence and angle parameters are treated equally during the optimization. We have employed a simple parameterized beam fluence representation with a realistic dose calculation approach, incorporating patient scatter effects, to demonstrate feasibility of the proposed approach on two phantoms. The first phantom is a simple cylindrical phantom containing a target surrounded by three OARs, while the second phantom is more complex and represents a paraspinal patient. RESULTS pareto results in a large database of Pareto nondominated solutions that represent the necessary trade-offs between objectives. The solution quality was examined for several PTV and OAR fitness functions. The combination of a conformity-based PTV fitness function and a dose-volume histogram (DVH) or equivalent uniform dose (EUD) -based fitness function for the OAR produced relatively uniform and conformal PTV doses, with well-spaced beams. A penalty function added to the fitness functions eliminates hotspots. Comparison of resulting DVHs to those from treatment plans developed with a single-objective fluence optimizer (from a commercial treatment planning system) showed good correlation. Results also indicated that pareto shows promise in optimizing the number of beams. CONCLUSIONS This initial evaluation of the evolutionary optimization software tool pareto for IMRT treatment planning demonstrates feasibility and provides motivation for continued development. Advantages of this approach over current commercial methods for treatment planning are many, including: (1) fully automated optimization that avoids human controlled iterative optimization and potentially improves overall process efficiency, (2) formulation of the problem as a true multiobjective one, which provides an optimized set of Pareto nondominated solutions refined over hundreds of generations and compiled from thousands of parameter sets explored during the run, and (3) rapid exploration of the final nondominated set accomplished by a graphical interface used to select the best treatment option for the patient.

Seasonal and Weather Effects on Older Drivers' Trip Distances

Le but de cette étude a été de déterminer si la saison ou la météo, mesurées objectivement, ont affecté les distances des trajets parcourus par les conducteurs âgés (≥ 70 ans, n = 279) à travers sept sites canadiens. Pendant l’hiver, pour tous les voyages effectués, la distance était de 7 pour cent plus court, lors du contrôle pour le site et si le voyage a eu lieu pendant la journée. En outre, pour les déplacements effectués dans les limites de la ville, la distance était de 1 pour cent plus courte en hiver et 5 pour cent plus en cas de pluie, par rapport à aucune précipitation, tout en contrôlant pour le temps (ou la saison, respectivement), le moment de la journée, et le site. La nuit, contrairement à toute attente, la distance des voyages était d’environ 30 pour cent plus, lors de la commande pour la saison et le site (et la météo). Pris ensemble, ces résultats suggèrent que les conducteurs canadiens âgés modifient leurs distances de déplacement basé sur la saison, les conditions météorologiques, et l’heure de la journée, mais pas toujours dans le sens attendu. The purpose of this study was to determine if season or weather affected the objectively measured trip distances of older drivers (≥ 70 years; n = 279) at seven Canadian sites. During winter, for all trips taken, trip distance was 7 per cent shorter when controlling for site and whether the trip occurred during the day. In addition, for trips taken within city limits, trip distance was 1 per cent shorter during winter and 5 per cent longer during rain when compared to no precipitation when controlling for weather (or season respectively), time of day, and site. At night, trip distance was about 30 per cent longer when controlling for season and site (and weather), contrary to expectations. Together, these results suggest that older Canadian drivers alter their trip distances based on season, weather conditions, and time of day, although not always in the expected direction.

Naturalistic driving patterns of older adults before and after cataract surgery

OBJECTIVE Cataract surgery can have many benefits for older adults, including enabling continued ability to drive. However, it is not known how objectively measured driving patterns change after cataract surgery. The purpose of this study was to examine how participants drove before and after cataract surgery. DESIGN Longitudinal study. PARTICIPANTS Individuals from the Winnipeg site of Candrive (a longitudinal study of older drivers in Canada). METHODS An in-vehicle device monitored all trips taken in 1-second intervals, allowing for the analysis of distances driven, number of trips, time of trips, speeding, excessive braking/accelerating, and types of roadways. RESULTS Over the 4 years of data collection, there were 16 cases of participants having cataract surgery, whereby there was also suitable driving data for analyses. Participants drove 28% further after surgery (p = 0.022). They also drove further from home and more on primary roads (p < 0.05) and had fewer episodes of hard braking per distance travelled (p < 0.001). No other variables significantly changed. CONCLUSIONS This study suggests that older drivers changed some of their driving patterns after cataract surgery. Future studies could explore the effects of increased driving exposure, in conjunction with potentially safer driving behaviors, on overall driving safety after cataract surgery.

Sci—Thur AM: Planning ‐ 04: Evaluation of the fluence complexity, solution quality, and run efficiency produced by five fluence parameterizations implemented in PARETO multiobjective radiotherapy treatment planning software

PURPOSE PARETO (Pareto-Aware Radiotherapy Evolutionary Treatment Optimization) is a novel multiobjective treatment planning system that performs beam orientation and fluence optimization simultaneously using an advanced evolutionary algorithm. In order to reduce the number of parameters involved in this enormous search space, we present several methods for modeling the beam fluence. The parameterizations are compared using innovative tools that evaluate fluence complexity, solution quality, and run efficiency. METHODS A PARETO run is performed using the basic weight (BW), linear gradient (LG), cosine transform (CT), beam group (BG), and isodose-projection (IP) methods for applying fluence modulation over the projection of the Planning Target Volume in the beams-eye-view plane. The solutions of each run are non-dominated with respect to other trial solutions encountered during the run. However, to compare the solution quality of independent runs, each run competes against every other run in a round robin fashion. Score is assigned based on the fraction of solutions that survive when a tournament selection operator is applied to the solutions of the two competitors. To compare fluence complexity, a modulation index, fractal dimension, and image gradient entropy are calculated for the fluence maps of each optimal plan. RESULTS We have found that the LG method results in superior solution quality for a spine phantom, lung patient, and cauda equina patient. The BG method produces solutions with the highest degree of fluence complexity. Most methods result in comparable run times. CONCLUSION The LG method produces superior solution quality using a moderate degree of fluence modulation.

TU‐A‐BRB‐06: Evolving and Optimizing IMRT Treatment Plans with PARETO ‐ a Novel Treatment Planning System Based on a Multi‐Objective Genetic Algorithm

Purpose: We introduce a novel multi‐objective treatment planning system called PARETO (Pareto‐Aware Radiotherapy Evolutionary Treatment Optimization), which simultaneously optimizes beam angles and fluence patterns by treating the PTV conformity and dose to OARs as separate objectives that are optimized by a powerful multi‐objective parallel genetic algorithm (GA).Methods: PARETO treats radiotherapytreatment planning as a single monolithic optimization problem, in which beam angle configurations and fluence patterns are explored to simultaneously optimize the PTV dose conformity and a dose objective for each OAR. We use a Pareto‐ranking scheme to discover necessary compromises between objectives. A final non‐dominated database of solutions is compiled from all plans evaluated during the run such that no solution in the database is superior to any other solution in all objectives. A graphical tool allows rapid navigation of the pre‐optimized database to select a final treatment option for the patient.Results: PARETO is at the stage of a working prototype. Solutions are of high quality, as judged by DVH curves and dose distribution, and consistent results are obtained between runs. Only minor differences in trade‐off surfaces result from four different fluence parameterizations of varying complexity, but they differ by a factor of ∼4 in speed. A novel feature allows optimization of the number of beams and a realistic test case shows that no further improvements in conformity are found for more than ∼9 beams. A newly implemented GPU‐based ray tracer, including convolution with a patient dose kernel, results in a speedup factor of ∼3 compared to CPU‐based computation. Conclusions: This work demonstrates PARETOs feasibility as a treatment‐planning tool, which replaces manual iterative optimization of treatment plans with a rapid graphical exploration of pre‐optimized solutions. Clinically acceptable run times of less than an hour appear within reach by combining several of our GPU‐based systems in parallel. J. Fiege discloses authorship of the Ferret GA (used by PARETO) and ownership of nQube Technical Computing Corporation, which distributes this optimization software.

SU‐E‐T‐856: Investigation of Fluence Parameterization Methods for PARETO Multi‐Objective Radiation Therapy Treatment Planning Software

Purpose: To determine the best parameterization of fluence modulation for PARETO (Pareto‐Aware Radiotherapy Evolutionary Treatment Optimization) multi‐objective treatment planningsoftware, as judged by the complexity of each parameterization, the efficiency, and the quality of the plans produced. Methods: PARETO simultaneously optimizes the number of beams, beam orientations, and fluence modulation. For this investigation, we have tested four fluence parameterizations on a paraspinal tumour phantom and a spine patient data set using a pre‐determined number of beams. The first method applies linear gradients over the projection of Planning Target Volume (PTV) in the beams‐eye‐view plane. The second method applies the two‐dimensional inverse cosine transform of a few low‐frequency parameters. Another method interpolates between the intensities of a coarse grid of square beamlets. Also, an isodose‐based contour method defines regions on the fluence maps that are assigned different intensities. Methods which produce plans of the best quality (as judged by dose‐volume histograms) will have a similar distribution of non‐dominated solutions. The complexity of each parameterization is determined by the number of parameters, and the efficiency is determined by the run time. Results: For both geometries, we found that the set of non‐dominated solutions of each parameterization overlapped in the projection of the PTV conformity fitness function and the quadrature‐averaged Organs‐At‐Risk (OAR) fitness function. The beam group and cosine transform methods produced some plans which did slightly better in simultaneously achieving good PTV conformity and OAR dose sparing. The cosine transform and linear gradient methods proved to be the most efficient. The cosine transform method also has the least number of parameters per beam. Conclusions: We have found that several different parameterizations of fluence modulation produce non‐dominated solutions of similar quality. The cosine transform method is the best choice for efficiently producing solutions that do well in PTV conformity and OAR sparing.

Sci—Sat AM(1): Planning — 02: Validation of IMRT Solutions for PARETO Multi‐Objective Beam Angle Optimization Software

Multi‐objective radiotherapy beam angle optimization is currently unavailable in commercial treatment planningsoftware. The PARETO (Pareto‐Aware Radiotherapy Evolutionary Treatment Optimization)software package presented here uses a sophisticated evolutionary algorithm that is capable of handling this difficult computational problem, while simultaneously incorporating IMRT fluence optimization. In this work, we focus on establishing the validity of the PARETO software in obtaining reasonable beam fluence maps for fixed gantry IMRT. PARETO IMRT solutions for a simple cylindrical phantom containing various structures (one planning target volume and one‐to‐three organs‐at‐risk) are compared to resulting fluence patterns at fixed beam angles generated by a commercial treatment planning system. We find that PARETO solutions agree well with those of the commercial system, with differences mainly attributable to the differences in the underlying dose calculation algorithm. Our approach to fluence modulation will allow PARETO to generate a database of optimal solutions for beam angles and IMRT plans that represent the best possible trade‐offs between competing treatment objectives.

Poster — Thur Eve — 58: Beam Orientation Optimization for IMRT Treatment Planning Using PARETO

Intensity-modulated radiation therapy (IMRT) treatment planning requires tradeoffs to be made between delivering a prescribed dose to the planning target volume (PTV) and sparing the organs-at-risk (OARs). Traditionally in clinical practice, treatment planners manually optimize beam orientations, objectives, and/or weights in a time-consuming, trial-and-error process to find some acceptable compromise, with no guarantee that this solution is actually optimal. We propose a novel and powerful fluence and beam orientation optimization package for radiotherapy optimization, called PARETO (Pareto-Aware Radiotherapy Evolutionary Treatment Optimization), which consists of a multi-objective genetic algorithm capable of optimizing several objective functions simultaneously and mapping the structure of their trade-off surface efficiently and in detail. Fitness functions, based on mean dose for the OARs and PTV, as well as fluence gradients are optimized. PARETO intelligently varies all beam orientations and beam fluence to simultaneously optimize all objectives. Over many generations, the entire family of Pareto-optimal treatment plans, spanning a multi-dimensional trade-off surface, is mapped out. Pareto-optimal solutions are stored in a database and trade-offs between the competing objectives can be visualized graphically and explored. The efficacy of the solutions provided by PARETO was evaluated using a commercial treatment planning system with five coplanar IMRT treatment plans for a homogenous phantom consisting of three OARs surrounding a central PTV. This work demonstrated that the fitness functions within PARETO have a strong correlation to the dose distribution. Thus, from many Pareto-optimal plans, the clinician may select the plan which they decide is the most appropriate multi-objective compromise for a patient.

Sci—Sat AM(1): Planning — 04: PARETO: A Novel Evolutionary Optimization Approach to Multi‐Objective Radiotherapy Planning

Intensity modulated radiation therapy(IMRT) aims to deliver a uniform prescribed dose of radiation to a planning target volume (PTV), while also minimizing the dose to each nearby organ at risk (OAR). IMRTtreatment planning is therefore a multi‐objective optimization problem, which can be addressed by powerful multi‐objective optimization techniques from the field of evolutionary computing. We introduce a software package called PARETO (Pareto‐Aware Radiotherapy Evolutionary Optimization), under development at the University of Manitoba and CancerCare Manitoba, which uses a sophisticated multi‐objective genetic algorithm called Ferret to find Pareto‐optimal beam orientations and fluence maps for IMRTtreatment planning. We discuss our fitness functions and a novel geometric method for parameterizing fluence maps for use with the Ferret Genetic Algorithm. We show that our method replaces manual iterative optimization methods currently used with a faster navigation of a pre‐optimized database of solutions. We present an illustrative example that applies the code to a geometric phantom with three OARs, and show that the PARETO finds two distinct classes of solutions.