No abstract

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 (ℓEUD) (conventional ‘ℓEUD model’), the so-called perfusion-weighted ℓEUD (fEUD) (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 ℓEUD, fEUD, and GLF plans delivering the same target ℓEUD 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 4.6%(7.5%) more liver function than the fEUD (ℓEUD) plan does in 2D cases, and up to 4.5%(5.6%) in 3D cases. The GLF and fEUD plans worsen in ℓEUD 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 ℓEUD model optimization does, the GLF model directly optimizes a more clinically relevant metric and can further improve fEUD plan quality.

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.