Direct incorporation of patient-specific efficacy and toxicity estimates in radiation therapy plan optimization.

PURPOSE: Current radiation therapy (RT) treatment planning relies mainly on pre-defined dose-based objectives and constraints to develop plans that aim to control disease while limiting damage to normal tissues during treatment. These objectives and constraints are generally population-based, in that they are developed from the aggregate response of a broad patient population to radiation. However, correlations of new biologic markers and patient-specific factors to treatment efficacy and toxicity provide the opportunity to further stratify patient populations and develop a more individualized approach to RT planning. We introduce a novel intensity-modulated radiation therapy (IMRT) optimization strategy that directly incorporates patient-specific dose response models into the planning process. In this strategy, we integrate the concept of utility-based planning where the optimization objective is to maximize the predicted value of overall treatment utility, defined by the probability of efficacy (e.g., local control) minus the weighted sum of toxicity probabilities. To demonstrate the feasibility of the approach, we apply the strategy to treatment planning for non-small cell lung cancer (NSCLC) patients. METHODS AND MATERIALS: We developed a prioritized approach to patient-specific IMRT planning. Using a commercial treatment planning system (TPS), we calculate dose based on an influence matrix of beamlet-dose contributions to regions-of-interest. Then, outside of the TPS, we hierarchically solve two optimization problems to generate optimal beamlet weights that can then be imported back to the TPS. The first optimization problem maximizes a patient's overall plan utility subject to typical clinical dose constraints. In this process, we facilitate direct optimization of efficacy and toxicity trade-off based on individualized dose-response models. After optimal utility is determined, we solve a secondary optimization problem that minimizes a conventional dose-based objective subject to the same clinical dose constraints as the first stage but with the addition of a constraint to maintain the optimal utility from the first optimization solution. We tested this method by retrospectively generating plans for five previously treated NSCLC patients and comparing the prioritized utility plans to conventional plans optimized with only dose metric objectives. To define a plan utility function for each patient, we utilized previously published correlations of dose to local control and grade 3-5 toxicities that include patient age, stage, microRNA levels, and cytokine levels, among other clinical factors. RESULTS: The proposed optimization approach successfully generated RT plans for five NSCLC patients that improve overall plan utility based on personalized efficacy and toxicity models while accounting for clinical dose constraints. Prioritized utility plans demonstrated the largest average improvement in local control (16.6%) when compared to plans generated with conventional planning objectives. However, for some patients, the utility-based plans resulted in similar local control estimates with decreased estimated toxicity. CONCLUSION: The proposed optimization approach, where the maximization of a patient's RT plan utility is prioritized over the minimization of standardized dose metrics, has the potential to improve treatment outcomes by directly accounting for variability within a patient population. The implementation of the utility-based objective function offers an intuitive, humanized approach to biological optimization in which planning trade-offs are explicitly optimized.

PNaV: A tool for generating a high-dose-rate brachytherapy treatment plan by navigating the Pareto surface guided by the visualization of multidimensional trade-offs.

PURPOSE: A Pareto Navigation and Visualization (PNaV) tool is presented for interactively constructing a high-dose-rate (HDR) brachytherapy treatment plan by navigating and visualizing the multidimensional Pareto surface. PNaV aims to improve treatment planning time and quality and is generalizable to any number of dose-volume histogram (DVH) and convex dose metrics. METHODS AND MATERIALS: Pareto surface visualization and navigation were demonstrated for prostate, breast, and cervix HDR brachytherapy sites. A library of treatment plans was created to span the Pareto surfaces over a 30% range of doses in each of five DVH metrics. The PNaV method, which uses a nonnegative least-squares model to interpolate the library plans, was compared against pure optimization for 11,250 navigated plans using data envelopment analysis. The visualization of the metric trade-offs was accomplished using numerically estimated partial derivatives to plot the local curvature of the Pareto surface. PNaV enables the user to control both the magnitude and direction of the trade-off during navigation. RESULTS: Proof of principle of PNaV was demonstrated using a graphical user interface with visualization tools to enabled rapid plan selection and a quantitative review of metric trade-offs. PNaV produced deliverable plans with DVH metrics within < 0.4%, 0.6%, and 1.1% (95% confidence interval) of the Pareto surface using plan libraries with nominal plan spacing of 10%, 15%, and 30% in each metric dimension, respectively. The interpolation used for the navigation executed in 0.1 s. The fast interpolation allows for quick and efficient exploration of trade-off options by the physician, after an initial preprocessing step to generate the library. CONCLUSIONS: Generation, visualization, and navigation of the Pareto surface were validated for brachytherapy treatment planning. The PNaV method enables efficient and informed decision-making for radiotherapy.

A new optimization algorithm for HDR brachytherapy that improves DVH-based planning: Truncated Conditional Value-at-Risk (TCVaR).

Purpose:To introduce a new optimization algorithm that improves DVH results and is designed for the type of heterogeneous dose distributions that occur in brachytherapy.Methods:The new optimization algorithm is based on a prior mathematical approach that uses mean doses of the DVH metric tails. The prior mean dose approach is referred to as conditional value-at-risk (CVaR), and unfortunately produces noticeably worse DVH metric results than gradient-based approaches. We have improved upon the CVaR approach, using the so-called Truncated CVaR (TCVaR), by excluding the hottest or coldest voxels in the structure from the calculations of the mean dose of the tail. Our approach applies an iterative sequence of convex approximations to improve the selection of the excluded voxels. Data Envelopment Analysis was used to quantify the sensitivity of TCVaR results to parameter choice and to compare the quality of a library of 256 TCVaR plans created for each of prostate, breast, and cervix treatment sites with commercially-generated plans.Results:In terms of traditional DVH metrics, TCVaR outperformed CVaR and the improvements increased monotonically as more iterations were used to identify and exclude the hottest/coldest voxels from the optimization problem. TCVaR also outperformed the Eclipse-Brachyvision TPS, with an improvement in PTVD95% (for equivalent organ-at-risk doses) of up to 5% (prostate), 3% (breast), and 1% (cervix).Conclusions:A novel optimization algorithm for HDR treatment planning produced plans with superior DVH metrics compared with a prior convex optimization algorithm as well as Eclipse-Brachyvision. The algorithm is computationally efficient and has potential applications as a primary optimization algorithm or quality assurance for existing optimization approaches.

Optimizing global liver function in radiation therapy treatment planning.

Liver stereotactic body radiation therapy (SBRT) patients differ in both pre-treatment liver function (e.g. due to degree of cirrhosis and/or prior treatment) and radiosensitivity, leading to high variability in potential liver toxicity with similar doses. This work investigates three treatment planning optimization models that minimize risk of toxicity: two consider both voxel-based pre-treatment liver function and local-function-based radiosensitivity with dose; one considers only dose. Each model optimizes different objective functions (varying in complexity of capturing the influence of dose on liver function) subject to the same dose constraints and are tested on 2D synthesized and 3D clinical cases. The normal-liver-based objective functions are the linearized equivalent uniform dose ([Formula: see text]) (conventional '[Formula: see text] model'), the so-called perfusion-weighted [Formula: see text] ([Formula: see text]) (proposed 'fEUD model'), and post-treatment global liver function (GLF) (proposed 'GLF model'), predicted by a new liver-perfusion-based dose-response model. The resulting [Formula: see text], fEUD, and GLF plans delivering the same target [Formula: see text] are compared with respect to their post-treatment function and various dose-based metrics. Voxel-based portal venous liver perfusion, used as a measure of local function, is computed using DCE-MRI. In cases used in our experiments, the GLF plan preserves up to [Formula: see text] more liver function than the fEUD ([Formula: see text]) plan does in 2D cases, and up to [Formula: see text] in 3D cases. The GLF and fEUD plans worsen in [Formula: see text] of functional liver on average by 1.0 Gy and 0.5 Gy in 2D and 3D cases, respectively. Liver perfusion information can be used during treatment planning to minimize the risk of toxicity by improving expected GLF; the degree of benefit varies with perfusion pattern. Although fEUD model optimization is computationally inexpensive and often achieves better GLF than [Formula: see text] model optimization does, the GLF model directly optimizes a more clinically relevant metric and can further improve fEUD plan quality.

VMATc: VMAT with constant gantry speed and dose rate.

This article considers the treatment plan optimization problem for Volumetric Modulated Arc Therapy (VMAT) with constant gantry speed and dose rate (VMATc). In particular, we consider the simultaneous optimization of multi-leaf collimator leaf positions and a constant gantry speed and dose rate. We propose a heuristic framework for (approximately) solving this optimization problem that is based on hierarchical decomposition. Specifically, an iterative algorithm is used to heuristically optimize dose rate and gantry speed selection, where at every iteration a leaf position optimization subproblem is solved, also heuristically, to find a high-quality plan corresponding to a given dose rate and gantry speed. We apply our framework to clinical patient cases, and compare the resulting VMATc plans to idealized IMRT, as well as full VMAT plans. Our results suggest that VMATc is capable of producing treatment plans of comparable quality to VMAT, albeit at the expense of long computation time and generally higher total monitor units.

A new column-generation-based algorithm for VMAT treatment plan optimization.

We study the treatment plan optimization problem for volumetric modulated arc therapy (VMAT). We propose a new column-generation-based algorithm that takes into account bounds on the gantry speed and dose rate, as well as an upper bound on the rate of change of the gantry speed, in addition to MLC constraints. The algorithm iteratively adds one aperture at each control point along the treatment arc. In each iteration, a restricted problem optimizing intensities at previously selected apertures is solved, and its solution is used to formulate a pricing problem, which selects an aperture at another control point that is compatible with previously selected apertures and leads to the largest rate of improvement in the objective function value of the restricted problem. Once a complete set of apertures is obtained, their intensities are optimized and the gantry speeds and dose rates are adjusted to minimize treatment time while satisfying all machine restrictions. Comparisons of treatment plans obtained by our algorithm to idealized IMRT plans of 177 beams on five clinical prostate cancer cases demonstrate high quality with respect to clinical dose-volume criteria. For all cases, our algorithm yields treatment plans that can be delivered in around 2 min. Implementation on a graphic processing unit enables us to finish the optimization of a VMAT plan in 25-55 s.

Ideal spatial radiotherapy dose distributions subject to positional uncertainties.

In radiotherapy a common method used to compensate for patient setup error and organ motion is to enlarge the clinical target volume (CTV) by a 'margin' to produce a 'planning target volume' (PTV). Using weighted power loss functions as a measure of performance for a treatment plan, a simple method can be developed to calculate the ideal spatial dose distribution (one that minimizes expected loss) when there is uncertainty. The spatial dose distribution is assumed to be invariant to the displacement of the internal structures and the whole patient. The results provide qualitative insights into the suitability of using a margin at all, and (if one is to be used) how to select a 'good' margin size. The common practice of raising the power parameters in the treatment loss function, in order to enforce target dose requirements, is shown to be potentially counter-productive. These results offer insights into desirable dose distributions and could be used, in conjunction with well-established inverse radiotherapy planning techniques, to produce dose distributions that are robust against uncertainties.