Clay Holdsworth

Toward Patient-Specific, Biologically Optimized Radiation Therapy Plans for the Treatment of Glioblastoma

Purpose To demonstrate a method of generating patient-specific, biologically-guided radiotherapy dose plans and compare them to the standard-of-care protocol. Methods and Materials We integrated a patient-specific biomathematical model of glioma proliferation, invasion and radiotherapy with a multiobjective evolutionary algorithm for intensity-modulated radiation therapy optimization to construct individualized, biologically-guided plans for 11 glioblastoma patients. Patient-individualized, spherically-symmetric simulations of the standard-of-care and optimized plans were compared in terms of several biological metrics. Results The integrated model generated spatially non-uniform doses that, when compared to the standard-of-care protocol, resulted in a 67% to 93% decrease in equivalent uniform dose to normal tissue, while the therapeutic ratio, the ratio of tumor equivalent uniform dose to that of normal tissue, increased between 50% to 265%. Applying a novel metric of treatment response (Days Gained) to the patient-individualized simulation results predicted that the optimized plans would have a significant impact on delaying tumor progression, with increases from 21% to 105% for 9 of 11 patients. Conclusions Patient-individualized simulations using the combination of a biomathematical model with an optimization algorithm for radiation therapy generated biologically-guided doses that decreased normal tissue EUD and increased therapeutic ratio with the potential to improve survival outcomes for treatment of glioblastoma.

A hierarchical evolutionary algorithm for multiobjective optimization in IMRT

PURPOSE The current inverse planning methods for intensity modulated radiation therapy (IMRT) are limited because they are not designed to explore the trade-offs between the competing objectives of tumor and normal tissues. The goal was to develop an efficient multiobjective optimization algorithm that was flexible enough to handle any form of objective function and that resulted in a set of Pareto optimal plans. METHODS A hierarchical evolutionary multiobjective algorithm designed to quickly generate a small diverse Pareto optimal set of IMRT plans that meet all clinical constraints and reflect the optimal trade-offs in any radiation therapy plan was developed. The top level of the hierarchical algorithm is a multiobjective evolutionary algorithm (MOEA). The genes of the individuals generated in the MOEA are the parameters that define the penalty function minimized during an accelerated deterministic IMRT optimization that represents the bottom level of the hierarchy. The MOEA incorporates clinical criteria to restrict the search space through protocol objectives and then uses Pareto optimality among the fitness objectives to select individuals. The population size is not fixed, but a specialized niche effect, domination advantage, is used to control the population and plan diversity. The number of fitness objectives is kept to a minimum for greater selective pressure, but the number of genes is expanded for flexibility that allows a better approximation of the Pareto front. RESULTS The MOEA improvements were evaluated for two example prostate cases with one target and two organs at risk (OARs). The population of plans generated by the modified MOEA was closer to the Pareto front than populations of plans generated using a standard genetic algorithm package. Statistical significance of the method was established by compiling the results of 25 multiobjective optimizations using each method. From these sets of 12-15 plans, any random plan selected from a MOEA population had a 11.3% +/- 0.7% chance of dominating any random plan selected by a standard genetic package with 0.04% +/- 0.02% chance of domination in reverse. By implementing domination advantage and protocol objectives, small and diverse populations of clinically acceptable plans that approximated the Pareto front could be generated in a fraction of 1 h. Acceleration techniques implemented on both levels of the hierarchical algorithm resulted in short, practical runtimes for multiobjective optimizations. CONCLUSIONS The MOEA produces a diverse Pareto optimal set of plans that meet all dosimetric protocol criteria in a feasible amount of time. The final goal is to improve practical aspects of the algorithm and integrate it with a decision analysis tool or human interface for selection of the IMRT plan with the best possible balance of successful treatment of the target with low OAR dose and low risk of complication for any specific patient situation.

When is better best? A multiobjective perspective

PURPOSE To identify the most informative methods for reporting results of treatment planning comparisons. METHODS Seven articles from the past year of International Journal of Radiation Oncology Biology Physics reported on comparisons of treatment plans for IMRT and IMAT. The articles were reviewed to identify methods of comparisons. Decision theoretical concepts were used to evaluate the study methods and highlight those that provide the most information. RESULTS None of the studies examined the correlation between objectives. Statistical comparisons provided some information but not enough to provide support for a robust decision analysis. CONCLUSIONS The increased use of treatment planning studies to evaluate different methods in radiation therapy requires improved standards for designing the studies and reporting the results.

Investigation of effective decision criteria for multiobjective optimization in IMRT

PURPOSE To investigate how using different sets of decision criteria impacts the quality of intensity modulated radiation therapy (IMRT) plans obtained by multiobjective optimization. METHODS A multiobjective optimization evolutionary algorithm (MOEA) was used to produce sets of IMRT plans. The MOEA consisted of two interacting algorithms: (i) a deterministic inverse planning optimization of beamlet intensities that minimizes a weighted sum of quadratic penalty objectives to generate IMRT plans and (ii) an evolutionary algorithm that selects the superior IMRT plans using decision criteria and uses those plans to determine the new weights and penalty objectives of each new plan. Plans resulting from the deterministic algorithm were evaluated by the evolutionary algorithm using a set of decision criteria for both targets and organs at risk (OARs). Decision criteria used included variation in the target dose distribution, mean dose, maximum dose, generalized equivalent uniform dose (gEUD), an equivalent uniform dose (EUD(alpha,beta) formula derived from the linear-quadratic survival model, and points on dose volume histograms (DVHs). In order to quantatively compare results from trials using different decision criteria, a neutral set of comparison metrics was used. For each set of decision criteria investigated, IMRT plans were calculated for four different cases: two simple prostate cases, one complex prostate Case, and one complex head and neck Case. RESULTS When smaller numbers of decision criteria, more descriptive decision criteria, or less anti-correlated decision criteria were used to characterize plan quality during multiobjective optimization, dose to OARs and target dose variation were reduced in the final population of plans. Mean OAR dose and gEUD (a = 4) decision criteria were comparable. Using maximum dose decision criteria for OARs near targets resulted in inferior populations that focused solely on low target variance at the expense of high OAR dose. Target dose range, (D(max) - D(min)), decision criteria were found to be most effective for keeping targets uniform. Using target gEUD decision criteria resulted in much lower OAR doses but much higher target dose variation. EUD(alpha,beta) based decision criteria focused on a region of plan space that was a compromise between target and OAR objectives. None of these target decision criteria dominated plans using other criteria, but only focused on approaching a different area of the Pareto front. CONCLUSIONS The choice of decision criteria implemented in the MOEA had a significant impact on the region explored and the rate of convergence toward the Pareto front. When more decision criteria, anticorrelated decision criteria, or decision criteria with insufficient information were implemented, inferior populations are resulted. When more informative decision criteria were used, such as gEUD, EUD(alpha,beta), target dose range, and mean dose, MOEA optimizations focused on approaching different regions of the Pareto front, but did not dominate each other. Using simple OAR decision criteria and target EUD(alpha,beta) decision criteria demonstrated the potential to generate IMRT plans that significantly reduce dose to OARs while achieving the same or better tumor control when clinical requirements on target dose variance can be met or relaxed.

Personalized treatment planning with a model of radiation therapy outcomes for use in multiobjective optimization of IMRT plans for prostate cancer

PurposeTo build a new treatment planning approach that extends beyond radiation transport and IMRT optimization by modeling the radiation therapy process and prognostic indicators for more outcome-focused decision making.MethodsAn in-house treatment planning system was modified to include multiobjective inverse planning, a probabilistic outcome model, and a multi-attribute decision aid. A genetic algorithm generated a set of plans embodying trade-offs between the separate objectives. An influence diagram network modeled the radiation therapy process of prostate cancer using expert opinion, results of clinical trials, and published research. A Markov model calculated a quality adjusted life expectancy (QALE), which was the endpoint for ranking plans.ResultsThe Multiobjective Evolutionary Algorithm (MOEA) was designed to produce an approximation of the Pareto Front representing optimal tradeoffs for IMRT plans. Prognostic information from the dosimetrics of the plans, and from patient-specific clinical variables were combined by the influence diagram. QALEs were calculated for each plan for each set of patient characteristics. Sensitivity analyses were conducted to explore changes in outcomes for variations in patient characteristics and dosimetric variables. The model calculated life expectancies that were in agreement with an independent clinical study.ConclusionsThe radiation therapy model proposed has integrated a number of different physical, biological and clinical models into a more comprehensive model. It illustrates a number of the critical aspects of treatment planning that can be improved and represents a more detailed description of the therapy process. A Markov model was implemented to provide a stronger connection between dosimetric variables and clinical outcomes and could provide a practical, quantitative method for making difficult clinical decisions.

SU-E-T-239: In Vivo Dosimetry with Surface Diodes during Total Body Irradiation: A Patient Thickness Factor to Correct Midline Dose

PURPOSE In vivo dosimetry (IVD) assessment of treatment dose is important when delivering total body irradiation (TBI). One method is to average AP and PA surface diode measurements and compare them to prescribed midline doses. We designed phantom studies to examine the impact of patient thickness on surface IVD measurements under TBI conditions. METHODS Phantom studies were designed to assess the effects of patient thickness on diode IVD. Sun Nuclear QED diodes with inherent buildup were placed on anterior and posterior surfaces of a solid water phantom. Phantom thickness was varied between 20 and 40 cm. A PTW farmer chamber was inserted in the center of the phantom at 425 SSD to reflect prescribed midline dose, and 50 cGy was delivered to midline with 18 MV photons. Averaged entrance and exit diode doses were then compared to farmer chamber measurements of phantom midline dose. RESULTS A trend of increased deviation with increasing umbilicus thickness was observed between averaged surface diodes and midline farmer chamber measurements. Averaged surface diode dose ranged from 49.6 cGy (20 cm thickness) to 52.1 cGy (40 cm thickness). Interpolation of diode measurements to midline resulted in linear overestimation of delivered dose relative to farmer chamber measurements at midline, up to 6.8% at 40 cm umbilicus thickness. CONCLUSION Accurate in vivo dosimetry at time of patient TBI is important to allow individual correction of MU exposure and tissue compensation. Without patient thickness correction, overresponse of surface diodes may lead to unnecessary clinical intervention to treatment MU or compensation and insufficient midline dose. Additionally, SAD setup is preferable to SSD setup to minimize thickness non-linearity. In conclusion, thickness correction factors should be used to generate expected diode readings for patients with thickness greater than 30 cm.

SU‐E‐T‐865: Biologically Optimized 4D Dose Distributions for the Treatment of Incurable Glioblastoma

Purpose: A patient‐specific method to optimize IMRTtreatment of incurable disease by maximizing tumorcell death and minimizing dose to normal tissue is proposed. Methods: A multiobjective evolutionary algorithm was used to optimize IMRT dose distributions on an example patient throughout treatment using a published patient‐specific 4D mathematical model of glioblastoma proliferation and invasion [Rockne 2010] to calculate the distribution of diffusely invaded tumorcells, hypoxia, and radiosensitivity and recalculate distributions after each week of treatment. Each optimized IMRT plan was designed to maximize EUD for tumorcells while holding constant the EUD for normal tissue. Dose distributions were scaled such that the EUD delivered to the normal tissue for each fraction was equal to the EUD delivered from one fraction using the current clinical protocol of 1.8 Gy to the frank tumor with a 2.5 cm margin. Results: The mathematical model predicted that the total volume of tumor was reduced by 41.0% after one week and 72.5% after two weeks of treatment using the proposed protocol. The current clinical protocol was shown to reduce volume by only 5.5% after one week and 11.2% after two weeks for the same EUD delivered to normal tissue. After 7 weeks of treatment using the clinical protocol and 250% higher normal tissue EUD, the tumor burden was only reduced by 40.4%. Conclusions: A patient‐specific mathematical model showed that a 4D treatment plan that maximizes tumorcell killing while sparing normal tissue reduces the tumor volume more in one week than in seven weeks using current clinical protocol for the same EUD delivered to normal tissue. This approach has the potential to optimize and tailor IMRTtreatment planning to individual patients disease through an iterative dialog between a mathematical model for disease and response to therapy with objective‐based treatment optimization.

Scripting in Radiation Therapy: An Automatic 3D Beam-Naming System

Scripts can be executed within the radiation treatment planning software framework to reduce human error, increase treatment planning efficiency, reduce confusion, and promote consistency within an institution or even among institutions. Scripting is versatile, and one application is an automatic 3D beam-naming system that describes the position of the beam relative to the patient in 3D space. The naming system meets the need for nomenclature that is conducive for clear and accurate communication of beam entry relative to patient anatomy. In radiation oncology in particular, where miscommunication can cause significant harm to patients, a system that minimizes error is essential. Frequent sharing of radiation treatment information occurs not only among members within a department but also between different treatment centers. Descriptions of treatment beams are perhaps the most commonly shared information about a patients course of treatment in radiation oncology. Automating the naming system by the use of a script reduces the potential for human error, improves efficiency, enforces consistency, and would allow an institution to convert to a new naming system with greater ease. This script has been implemented in the Department of Radiation Oncology at the University of Washington Medical Center since December 2009. It is currently part of the dosimetry protocol and is accessible by medical dosimetrists, radiation oncologists, and medical physicists. This paper highlights the advantages of using an automatic 3D beam-naming script to flawlessly and quickly identify treatment beams with unique names. Scripting in radiation treatment planning software has many uses and great potential for improving clinical care.

SU‐EE‐A1‐04: Multiobjective Evolutionary Algorithm for IMRT Optimization: Development and Clinical Comparisons

Purpose: To evaluate the clinical effectiveness of a multiobjective evolutionary algorithm (MOEA) for IMRT plan generation.Methods: A MOEA was developed that generates a set of IMRT plans that approximates the clinical Pareto front in a time comparable to current commercial inverse planning systems. The stochastic nature of the algorithm permits the use of any objective function regardless of convexity. Selected plans generated by the MOEA were compared with those generated clinically using a commercial planning system (Pinnacle 8.0, Philips Electronics, N.V.). Cases of head & neck cancer and prostate cancer were planned for IMRT on both systems. MOEA plans were evaluated by comparing their performance in meeting and exceeding objectives used in generating plans using conventional methods. Characteristics of the MOEA‐generated solutions were compared using a range of commonly used objective functions. Results: Results are classified into three groups: (1) establishing the ability of the MOEA to approximate the clinical Pareto front and optimizing speed, (2) evaluating objective functions, and(3) comparing MOEA plans with current clinical IMRT methods. The effect of modifications and different objective functions on the algorithm were judged by assessing the fraction of plans generated with one algorithm that Pareto dominated those of another. Results of Aim (3) showed that plans selected from the MOEA performed better than the commercial algorithm. Conclusion:IMRT planning is inherently multiobjective and treatment planning decisions should be made using multiobjective systems. We describe an evolutionary algorithm that provides a set of plans that consistently contain multiple plans superior to those achieved using a conventional optimization algorithm and meets clinical requirements for speed and performance. Using mean dose objective for OARs and range objective for targets demonstrated better performance than using EUD. This work was supported by NIH RO1 CA112505.