A Dynamic Programming Approach to Adaptive Fractionation

We conduct a theoretical study of various solution methods for the adaptive fractionation problem. The two messages of this paper are: (i) dynamic programming (DP) is a useful framework for adaptive radiation therapy, particularly adaptive fractionation, because it allows us to assess how close to optimal different methods are, and (ii) heuristic methods proposed in this paper are near-optimal, and therefore, can be used to evaluate the best possible benefit of using an adaptive fraction size. The essence of adaptive fractionation is to increase the fraction size when the tumor and organ-at-risk (OAR) are far apart (a "favorable" anatomy) and to decrease the fraction size when they are close together. Given that a fixed prescribed dose must be delivered to the tumor over the course of the treatment, such an approach results in a lower cumulative dose to the OAR when compared to that resulting from standard fractionation. We first establish a benchmark by using the DP algorithm to solve the problem exactly. In this case, we characterize the structure of an optimal policy, which provides guidance for our choice of heuristics. We develop two intuitive, numerically near-optimal heuristic policies, which could be used for more complex, high-dimensional problems. Furthermore, one of the heuristics requires only a statistic of the motion probability distribution, making it a reasonable method for use in a realistic setting. Numerically, we find that the amount of decrease in dose to the OAR can vary significantly (5 - 85%) depending on the amount of motion in the anatomy, the number of fractions, and the range of fraction sizes allowed. In general, the decrease in dose to the OAR is more pronounced when: (i) we have a high probability of large tumor-OAR distances, (ii) we use many fractions (as in a hyper-fractionated setting), and (iii) we allow large daily fraction size deviations.Comment: 17 pages, 4 figures, 1 tabl

The Perils of Adapting to Dose Errors in Radiation Therapy

We consider adaptive robust methods for lung cancer that are also dose-reactive, wherein the treatment is modified after each treatment session to account for the dose delivered in prior treatment sessions. Such methods are of interest because they potentially allow for errors in the delivered dose to be corrected as the treatment progresses, thereby ensuring that the tumor receives a sufficient dose at the end of the treatment. We show through a computational study with real lung cancer patient data that while dose reaction is beneficial with respect to the final dose distribution, it may lead to exaggerated daily underdose and overdose relative to non-reactive methods that grows as the treatment progresses. However, by combining dose reaction with a mechanism for updating an estimate of the uncertainty, the magnitude of this growth can be mitigated substantially. The key finding of this paper is that reacting to dose errors – an adaptation strategy that is both simple and intuitively appealing – may backfire and lead to treatments that are clinically unacceptable.Natural Sciences and Engineering Research Council of Canada (Canadian Institutes of Health Research Collaborative Health Research Project Grant 398106-2011

Development of a Novel Technique for Predicting Tumor Response in Adaptive Radiation Therapy

This dissertation concentrates on the introduction of Predictive Adaptive Radiation Therapy (PART) as a potential method to improve cancer treatment. PART is a novel technique that utilizes volumetric image-guided radiation therapy treatment (IGRT) data to actively predict the tumor response to therapy and estimate clinical outcomes during the course of treatment. To implement PART, a patient database containing IGRT image data for 40 lesions obtained from patients who were imaged and treated with helical tomotherapy was constructed. The data was then modeled using locally weighted regression. This model predicts future tumor volumes and masses and the associated confidence intervals based on limited observations during the first two weeks of treatment. All predictions were made using only 8 days worth of observations from early in the treatment and were all bound by a 95% confidence interval. Since the predictions were accurate with quantified uncertainty, they could eventually be used to optimize and adapt treatment accordingly, hence the term PART (Predictive Adaptive Radiation Therapy). A challenge in implementing PART in a clinical setting is the increased quality assurance that it will demand. To help ease this burden, a technique was developed to automatically evaluate helical tomotherapy treatments during delivery using exit detector data. This technique uses an auto-associative kernel regression (AAKR) model to detect errors in tomotherapy delivery. This modeling scheme is especially suited for the problem of monitoring the fluence values found in the exit detector data because it is able to learn the complex detector data relationships. Several AAKR models were tested using tomotherapy detector data from deliveries that had intentionally inserted errors and different attenuations from the sinograms that were used to develop the model. The model proved to be robust and could predict the correct “error-free” values for a projection in which the opening time of a single MLC leaf had been decreased by 10%. The model also was able to determine machine output errors. The automation of this technique should significantly ease the QA burden that accompanies adaptive therapy, and will help to make the implementation of PART more feasible

Leksell Gamma Knife Treatment Planning via Kernel Regression Data Mining Initialization and Genetic Algorithm Optimization

Gamma Knife is a medical procedure that is used to treat several types of intracranial disease. The system utilizes gamma rays from Cobalt-60 radiation sources focused at an isocenter and a stereotactic frame system that serves as an immobilization device coordinate system. Treatment is performed by localizing the patient’s disease with a medical imaging study and positioning the diseased area at the focused intersection of the beams. Patient treatment may require multiple treatment positions and varying beam sizes. The treatment position, time, and beam size is determined through a treatment planning process. Traditionally Gamma Knife treatment planning is performed manually by an expert planner. This process can be time consuming and arrival at an optimal plan may depend on the skill of the planner. This work automates the treatment planning process with a multi-module optimization system. First, a kernel regression data mining module compares the treatment volume to a database of past treatment plans to create a set of initial plans. These plans seed a genetic algorithm optimizer that produces an optimized plan. The cost function for the optimization is a weighted average of several traditional metric for assessing stereotactic radiosurgery plan quality. A gradient descent optimizer is utilized to further refine the optimized treatment plan. The developed system was applied to three Gamma Knife planning cases; a solitary metastasis, an acoustic schwannoma, and a pituitary adenoma. The system produced an average percent isodose coverage for the three plans of 94.5% and the average Paddick Conformity index was 0.76 in an average time of 17.16 minutes for the three plans. The system was compared to an expert planner and an optimizer included with the Gamma Knife planning software. The developed system and expert planner performance was essentially equivalent (average percent isodose coverage 95.8%, average Paddick Conformity index 0.70, optimization time 20.52). The developed system performed much better than the Gamma Plan optimizer (average percent isodose coverage 85.8%, average Paddick Conformity index 0.71) however the Gamma Plan optimizer result was obtained quicker (optimization time about 1 minute). The developed system can be utilized for efficient high-quality Gamma Knife treatment planning

Adaptive Intensity Modulated Radiation Therapy Planning Optimization with Changing Tumor Geometry and Biology Enforcing Both Cumulative and Fraction Size Dose Constraints

Intensity Modulated Radiation Therapy (IMRT) is a modern technique of delivering radiation treatments to cancer patients. In IMRT technology, intensities must be chosen for the many small unit grids into which the beams are divided to produce a desired distribution of dose at points throughout the body with the goal of maximizing dose delivered to the tumor while sparing healthy tissues from excessive radiation and keeping dose homogeneous across the tumor. Although IMRT plans are optimized as a single overall treatment plan, they are delivered over 30-50 treatment sessions (fractions) and both cumulative and per-fraction dose constraints apply. The extended time period of treatment allows for periodic re-imaging of the changing tumor geometry and for adapting the treatment plan accordingly. This research presents promising iterative optimization approaches that re-optimize and update the treatment plans periodically by incorporating the latest tumor geometry information. Two realistic lung cases simulating practice, based on anonymized archive datasets, are used to test the effectiveness of the proposed adaptive planning approaches. The computed optimal plans both satisfy cumulative and per-session dose constraints while improving the objective (average tumor dose) as compared to non-adaptive treatment. In addition to tracking tumor geometrical changes through the treatment, recent advances in imaging technology also provide more insight on tumor biology which has been traditionally disregarded in planning. The current practice of delivering homogeneous physical dose distributions across the tumor can be improved by nonhomogeneous distributions guided by the biological responses of the tumor points. This research is one of the first efforts in developing radiation therapy planning optimization methods with tumor biology information while maintaining both cumulative and per-fraction dose constraints. The proposed biological optimization models generate treatment plans reacting to the tumor biology prior to the treatment as well as the changing tumor biology throughout the treatment. The optimization models are tested on a simulated head and neck test case. Results show computed biologically optimized plans improve on tumor control obtained by traditional plans ignoring biology, and also with adaptive over non-adaptive methods

Optimization Methods for Volumetric Modulated Arc Therapy and Radiation Therapy under Uncertainty.

Treatment plan optimization is crucial in the success of radiation therapy treatments. In order to deliver the prescribed radiation dose to the tumor without damaging the healthy organs, plans must be carefully designed so that collectively the dose delivered from different angles achieves the desired treatment outcome. The development of Volumetric Modulated Arc Therapy (VMAT) enables planners to explore additional benefits compared to traditional Intensity Modulated Radiation Therapy (IMRT). By rotating the gantry and attached source continuously, VMAT treatments can be delivered in a short period of time. While clinics can benefit from improved equipment utilization and less patient discomfort, treatment planning for VMAT is challenging because of the restrictions associated with the continuous gantry motion. We propose a new column generation based algorithm that explicitly considers the physical constraints, and constructs the treatment plan in an iterative process that searches for the maximum marginal improvement in each iteration. Implemented with GPU-based parallel computing, our algorithm is very efficient in generating high quality plans compared to idealized 177-angle IMRT plans. While treatments can benefit from VMAT in many ways, the capital expenditure in upgrading to a dedicated VMAT system is an important factor for clinics. Conventional IMRT machines can deliver VMAT treatments with constant rate of radiation output and gantry speed (VMATC). The absence of the ability to dynamically change the dose rate and gantry speed makes VMATC different in nature from VMAT. We propose two optimization frameworks for optimizing the machine parameters in the treatment, and recommend one configuration that consistently produces high quality plans compared to VMAT treatments. Finally, we consider uncertainties in radiation therapy treatments associated with errors in the daily setup process. We propose a stochastic programming based model that explicitly incorporates the range of uncertain outcomes in both the daily and cumulative dose distributions. While the problem is difficult to solve directly, we use a dynamic sampling procedure that can guarantee close to optimal solutions by establishing bounds on the optimal objective. Experiments with clinical cases show that the stochastic plans outperform the conventional approach, and reveal important information for planning adaptive treatments.PHDIndustrial & Operations EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/99813/1/feipeng_1.pd