Minimizing Metastatic Risk in Radiotherapy Fractionation Schedules

Metastasis is the process by which cells from a primary tumor disperse and form new tumors at distant anatomical locations. The treatment and prevention of metastatic cancer remains an extremely challenging problem. This work introduces a novel biologically motivated objective function to the radiation optimization community that takes into account metastatic risk instead of the status of the primary tumor. In this work, we consider the problem of developing fractionated irradiation schedules that minimize production of metastatic cancer cells while keeping normal tissue damage below an acceptable level. A dynamic programming framework is utilized to determine the optimal fractionation scheme. We evaluated our approach on a breast cancer case using the heart and the lung as organs-at-risk (OAR). For small tumor $\alpha/\beta$ values, hypo-fractionated schedules were optimal, which is consistent with standard models. However, for relatively larger $\alpha/\beta$ values, we found the type of schedule depended on various parameters such as the time when metastatic risk was evaluated, the $\alpha/\beta$ values of the OARs, and the normal tissue sparing factors. Interestingly, in contrast to standard models, hypo-fractionated and semi-hypo-fractionated schedules (large initial doses with doses tapering off with time) were suggested even with large tumor $\alpha$/$\beta$ values. Numerical results indicate potential for significant reduction in metastatic risk.Comment: 12 pages, 3 figures, 2 table

Optimization of spatiotemporally fractionated radiotherapy treatments with bounds on the achievable benefit

Spatiotemporal fractionation schemes, that is, treatments delivering different dose distributions in different fractions, may lower treatment side effects without compromising tumor control. This is achieved by hypofractionating parts of the tumor while delivering approximately uniformly fractionated doses to the healthy tissue. Optimization of such treatments is based on biologically effective dose (BED), which leads to computationally challenging nonconvex optimization problems. Current optimization methods yield only locally optimal plans, and it has been unclear whether these are close to the global optimum. We present an optimization model to compute rigorous bounds on the normal tissue BED reduction achievable by such plans. The approach is demonstrated on liver tumors, where the primary goal is to reduce mean liver BED without compromising other treatment objectives. First a uniformly fractionated reference plan is computed using convex optimization. Then a nonconvex quadratically constrained quadratic programming model is solved to local optimality to compute a spatiotemporally fractionated plan that minimizes mean liver BED subject to the constraints that the plan is no worse than the reference plan with respect to all other planning goals. Finally, we derive a convex relaxation of the second model in the form of a semidefinite programming problem, which provides a lower bound on the lowest achievable mean liver BED. The method is presented on 5 cases with distinct geometries. The computed spatiotemporal plans achieve 12-35% mean liver BED reduction over the reference plans, which corresponds to 79-97% of the gap between the reference mean liver BEDs and our lower bounds. This indicates that spatiotemporal treatments can achieve substantial reduction in normal tissue BED, and that local optimization provides plans that are close to realizing the maximum potential benefit

Radiobiology of stereotactic body radiation therapy (SBRT)

Recent advances in the technology of radiotherapy have enabled the development of new therapeutic modalities that deliver radiation with very high accuracy, reduced margins and high dose conformation, allowing the reduction of healthy tissue irradiated and therefore minimizing the risk of toxicity. The next step was to increase the total tumor dose using conventional fractionation (which remains the best way to relatively radioprotect healthy tissues when large volumes are treated) or to use new fractionation schemes with greater biological effectiveness. Based on the experience gained in radiosurgery, the latter way was chosen for small and well-defined tumors in the body. Stereotactic body radiotherapy delivers high doses of radiation to small and well-defined targets in an extreme hypofractionated (and accelerated) scheme with a very high biological effectiveness obtaining very good initial clinical results in terms of local tumor control and acceptable rate of late complications. In fact, we realize a posteriori that it was not feasible to administer such biologically equivalent dose in a conventional fractionation because the treatment could last several months. So far, these new therapeutic modalities have been developed due to technologic advances in image guidance and treatment delivery but without a solid biological basis. It is the role of traditional radiobiology (and molecular radiobiology) to explain the effects of high doses of ionizing radiation on tumor and normal tissues. Only through a better understanding of how high doses of ionizing radiation act, clinicians will know exactly what we do, allowing us in the future to refine our treatments. This article attempts to describe through simple and understandable concepts the known aspects of the biological action of high doses of radiation on tumor and normal tissues, but it is clear that we need much more basic research to better understand the biology of high doses of radiation

Beyond The Dvh - Spatial And Biological Radiotherapy Treatment Planning

Purpose: Both spatial and biological information are necessary in order to perform true optimization of a treatment plan and for predicting clinical outcome. The goal of this work is to develop an enhanced treatment plan evaluation tool which incorporates biological parameters and retains spatial dose information. Methods: A software system named SABER (Spatial And Biological Evaluation for Radiotherapy) is developed which provides biological plan evaluation with a novel combination of features. It incorporates hyperradiosensitivity using the induced-repair model and applies the new concept of Dose Convolution Filter (DCF) to simulate dose wash-out effects due to cell migration, bystander effect, and tissue motion during treatment. Further, the concept of Spatial DVH (sDVH) is introduced to evaluate and potentially optimize the spatial dose distribution in the target volume. Finally, generalized equivalent uniform dose is derived from both physical dose distribution (gEUD) and EQD2 distribution (gEUD2), and the software provides three models for calculation of Tumor Control Probability (TCP), Normal Tissue Complication Probability (NTCP), and Complication-free TCP (P+). TCP, NTCP and P+ are provided as a function of prescribed dose and multi-variable TCP, NTCP and P+ plots are provided to illustrate the dependence upon individual parameters used to calculate these quantities. Results: By retaining both spatial and biological information about the dose distribution, SABER is able to distinguish features of radiotherapy treatment plans not discernible using commercial systems. Plans that have similar DVHs may have different spatial and biological characteristics, and the application of novel tools such as sDVH and DCF within SABER and the choice of radiobiological models may substantially change the predicted plan metrics such as TCP and NTCP, and thus change the relative plan ranking. The voxel-by-voxel TCP model makes it feasible to incorporate spatial variations of clonogen densities, radiosensitivities, and fractionation sensitivities as those data become available. Conclusions: The SABER software incorporates both spatial and biological information into the treatment planning process. This may significantly alter the predicted TCP and NTCP and thus the choice of treatment plan. Thus SABER can help the planner compare and choose more biologically optimal treatment plans and potentially predict treatment outcome more accurately