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

Fraction-variant beam orientation optimization for non-coplanar IMRT

Conventional beam orientation optimization (BOO) algorithms for IMRT assume that the same set of beam angles is used for all treatment fractions. In this paper we present a BOO formulation based on group sparsity that simultaneously optimizes non-coplanar beam angles for all fractions, yielding a fraction-variant (FV) treatment plan. Beam angles are selected by solving a multi-fraction FMO problem involving 500-700 candidate beams per fraction, with an additional group sparsity term that encourages most candidate beams to be inactive. The optimization problem is solved using the Fast Iterative Shrinkage-Thresholding Algorithm. Our FV BOO algorithm is used to create non-coplanar, five-fraction treatment plans for prostate and lung cases, as well as a non-coplanar 30-fraction plan for a head and neck case. A homogeneous PTV dose coverage is maintained in all fractions. The treatment plans are compared with fraction-invariant plans that use a fixed set of beam angles for all fractions. The FV plans reduced mean and max OAR dose on average by 3.3% and 3.7% of the prescription dose, respectively. Notably, mean OAR dose was reduced by 14.3% of prescription dose (rectum), 11.6% (penile bulb), 10.7% (seminal vesicle), 5.5% (right femur), 3.5% (bladder), 4.0% (normal left lung), 15.5% (cochleas), and 5.2% (chiasm). Max OAR dose was reduced by 14.9% of prescription dose (right femur), 8.2% (penile bulb), 12.7% (prox. bronchus), 4.1% (normal left lung), 15.2% (cochleas), 10.1% (orbits), 9.1% (chiasm), 8.7% (brainstem), and 7.1% (parotids). Meanwhile, PTV homogeneity defined as D95/D5 improved from .95 to .98 (prostate case) and from .94 to .97 (lung case), and remained constant for the head and neck case. Moreover, the FV plans are dosimetrically similar to conventional plans that use twice as many beams per fraction. Thus, FV BOO offers the potential to reduce delivery time for non-coplanar IMRT

Spatiotemporal Fractionation in Radiotherapy

In current clinical practice, radiotherapy treatments are often fractionated, i.e. the total radiation dose is equally divided into small fractions to be delivered daily over a period of few days or weeks. It has recently been shown in silico that spatiotemporal fractionation schemes, i.e. delivering distinct dose distributions in different fractions, can potentially improve the treatment. This is possible if these dose distributions are designed such that different fractions deliver similar doses to normal tissues (i.e. exploit the fractionation effect), but each fraction delivers high single-fraction doses to alternating parts of the tumor (i.e. achieve partial hypofractionation in the tumor). Thereby, the ratio of biological dose in the tumor versus the normal tissue can be improved. In this project, we further developed this innovative and novel concept. In particular, we focused on: 1. Developing new treatment planning algorithms for spatiotemporal fractionation 2. Identifying potential clinical applications of spatiotemporal fractionation with the aim of bringing spatiotemporal fractionation towards the design and implementation of a phase I clinical trial. Spatiotemporal fractionation is associated with higher complexity in treatment planning and delivery. Different plans with distinct dose distributions for different fractions must be designed such that all fractions together deliver the prescribed biological dose to the tumor. To that end, novel mathematical optimization methods for treatment planning have been developed, which are based on the cumulative biological dose rather than the physical dose. In particular, we developed robust treatment planning methods to account for geometric uncertainty in the patient setup and biological uncertainty in the fractionation sensitivity, which may lead to a degradation of the resulting treatment if not accounted for. It was shown that spatiotemporally fractionated treatments can be obtained which are robust against setup errors and uncertainty in the fractionation sensitivity. At the same time, these robust plans maintain most of their dosimetric benefit over uniformly fractionated plans. Besides liver cancer patients and patients with large arteriovenous malformations, patients with multiple brain metastases were identified to be especially well suited for spatiotemporal fractionation, because of the high accuracy in patient positioning. For theses patients, delivering high doses to different metastases in different fractions allows for fractionation of the normal brain dose in between the metastases while increasing the biological dose within the metastases. In addition, novel extensions of spatiotemporal fractionation were investigated. Spatiotemporal fractionation has been combined with other degrees of freedom that can be exploited in fractionated radiotherapy treatments, i.e. the combination of different particle types and treatment techniques, and the use of different beam orientations in different fractions. We showed that in the context of combined proton-photon therapy, spatiotemporal fractionation can be used to determine the optimal dose contribution of the proton and photon fractions to the tumor, thereby improving on simple proportional combination of intensity modulated radiotherapy and intensity modulated proton therapy plans. Also, we demonstrated that the quality of spatiotemporally fractionated treatments can be boosted by selecting fraction-specific beam orientations that are beneficial to treat specific regions of the tumor. To that end, a treatment planning algorithm was developed that allows for simultaneous optimization of multiple non-coplanar arc treatments. Finally, the simultaneous optimization of multiple dose distributions based on the cumulative biological dose is not supported by any commercial treatment planning system. To this end, we implemented a method which allows to import treatment plans optimized using our in-house research treatment planning system into a commercial treatment planning system. Thereby, it is possible to deliver spatiotemporally fractionated treatments in the clinics

Spatiotemporal fractionation schemes for stereotactic radiosurgery of multiple brain metastases

BACKGROUND Stereotactic radiosurgery (SRS) is an established treatment for patients with brain metastases (BMs). However, damage to the healthy brain may limit the tumor dose for patients with multiple lesions. PURPOSE In this study, we investigate the potential of spatiotemporal fractionation schemes to reduce the biological dose received by the healthy brain in SRS of multiple BMs, and also demonstrate a novel concept of spatiotemporal fractionation for polymetastatic cancer patients that faces less hurdles for clinical implementation. METHODS Spatiotemporal fractionation (STF) schemes aim at partial hypofractionation in the metastases along with more uniform fractionation in the healthy brain. This is achieved by delivering distinct dose distributions in different fractions, which are designed based on their cumulative biologically effective dose ( ) such that each fraction contributes with high doses to complementary parts of the target volume, while similar dose baths are delivered to the normal tissue. For patients with multiple brain metastases, a novel constrained approach to spatiotemporal fractionation (cSTF) is proposed, which is more robust against setup and biological uncertainties. The approach aims at irradiating entire metastases with possibly different doses, but spatially similar dose distributions in every fraction, where the optimal dose contribution of every fraction to each metastasis is determined using a new planning objective to be added to the BED-based treatment plan optimization problem. The benefits of spatiotemporal fractionation schemes are evaluated for three patients, each with >25 BMs. RESULTS For the same tumor BED$_{10}$ and the same brain volume exposed to high doses in all plans, the mean brain BED$_{2}$ can be reduced compared to uniformly fractionated plans by 9%-12% with the cSTF plans and by 13%-19% with the STF plans. In contrast to the STF plans, the cSTF plans avoid partial irradiation of the individual metastases and are less sensitive to misalignments of the fractional dose distributions when setup errors occur. CONCLUSION Spatiotemporal fractionation schemes represent an approach to lower the biological dose to the healthy brain in SRS-based treatments of multiple BMs. Although cSTF cannot achieve the full BED reduction of STF, it improves on uniform fractionation and is more robust against both setup errors and biological uncertainties related to partial tumor irradiation

A novel stochastic optimization method for handling misalignments of proton and photon doses in combined treatments

Objective.Combined proton-photon treatments, where most fractions are delivered with photons and only a few are delivered with protons, may represent a practical approach to optimally use limited proton resources. It has been shown that, when organs at risk (OARs) are located within or near the tumor, the optimal multi-modality treatment uses protons to hypofractionate parts of the target volume and photons to achieve near-uniform fractionation in dose-limiting healthy tissues, thus exploiting the fractionation effect. These plans may be sensitive to range and setup errors, especially misalignments between proton and photon doses. Thus, we developed a novel stochastic optimization method to directly incorporate these uncertainties into the biologically effective dose (BED)-based simultaneous optimization of proton and photon plans.Approach.The method considers the expected valueEband standard deviationσbof the cumulative BEDbin every voxel of a structure. For the target, a piecewise quadratic penalty function of the formbmin-Eb-2σb+2is minimized, aiming for plans in which the expected BED minus two times the standard deviation exceeds the prescribed BEDbmin.Analogously,Eb+2σb-bmax+2is considered for OARs.Main results.Using a spinal metastasis case and a liver cancer patient, it is demonstrated that the novel stochastic optimization method yields robust combined treatment plans. Tumor coverage and a good sparing of the main OARs are maintained despite range and setup errors, and especially misalignments between proton and photon doses. This is achieved without explicitly considering all combinations of proton and photon error scenarios.Significance.Concerns about range and setup errors for safe clinical implementation of optimized proton-photon radiotherapy can be addressed through an appropriate stochastic planning method

The convergence of radiation and immunogenic cell death signaling pathways.

Ionizing radiation (IR) triggers programmed cell death in tumor cells through a variety of highly regulated processes. Radiation-induced tumor cell death has been studied extensively in vitro and is widely attributed to multiple distinct mechanisms, including apoptosis, necrosis, mitotic catastrophe (MC), autophagy, and senescence, which may occur concurrently. When considering tumor cell death in the context of an organism, an emerging body of evidence suggests there is a reciprocal relationship in which radiation stimulates the immune system, which in turn contributes to tumor cell kill. As a result, traditional measurements of radiation-induced tumor cell death, in vitro, fail to represent the extent of clinically observed responses, including reductions in loco-regional failure rates and improvements in metastases free and overall survival. Hence, understanding the immunological responses to the type of radiation-induced cell death is critical. In this review, the mechanisms of radiation-induced tumor cell death are described, with particular focus on immunogenic cell death (ICD). Strategies combining radiotherapy with specific chemotherapies or immunotherapies capable of inducing a repertoire of cancer specific immunogens might potentiate tumor control not only by enhancing cell kill but also through the induction of a successful anti-tumor vaccination that improves patient survival

Outcomes of Stereotactic Ablative Radiotherapy for Centrally Located Early-Stage Lung Cancer

Introduction:The use of stereotactic ablative radiotherapy (SABR) in centrally located early-stage lung tumors has been associated with increased toxicity. We studied outcomes after delivery of risk-adapted SABR of central tumors.Methods:SABR was delivered in eight fractions of 7.5 Gy to 63 such patients between 2003 and 2009. Of these, 37 patients had a tumor at a central hilar location, whereas 26 patients had tumors abutting the pericardium or mediastinal structures. Survival outcomes were compared with patients with peripheral tumors treated during the same time period using fewer fractions of SABR.Results:Median follow-up was 35 months. Late grade III toxicity was limited to chest wall pain (n = 2) and increased dyspnoea (n = 2). No grade IV/V toxicity was observed, but grade V toxicity could not be excluded with certainty in nine patients who died of cardiopulmonary causes. Distant metastases were the predominant cause of death; cardiovascular deaths were not associated with a paracardial tumor location. No significant differences in outcomes were observed between these 63 patients and 445 other SABR patients treated for peripheral early-stage lung tumors. Three-year local control rates were 92.6% and 90.2% (p = 0.9). Three-year overall survival rates were 64.3% and 51.1% with median survival rates of 47 and 36 months, in favor of the group of patients with central tumors (p = 0.09).Conclusions:Use of risk-adapted SABR delivered in eight fractions of 7.5 Gy did not result in excess toxicity for centrally located early-stage lung tumors, and clinical outcomes were comparable with those seen for peripheral lesions

Biologically effective dose (BED) treatment planning for Gamma Knife Radiosurgery

Gamma Knife (GK) radiosurgery treats brain lesions through multiple targeted radiation exposures of varying duration and spatial distribution. Clinical radiosurgery treatment planning only considers the total amount of delivered radiation. A biologically effective dose (BED) model allows quantifying the damage induced in a tissue due to radiation exposure while accounting for cellular repair. With this thesis work, we explore the potential and feasibility of using the more complex BED formulation to generate biologically-aware treatment plans. To this end, we quantify the impact of changes in the temporal domain of treatment delivery (i.e. beam-off periods, order of delivery), which need to be considered at the treatment planning stage to avoid undesirable BED variations. The delivery sequence alone can incur variations in marginal BED by up to 14%. Consideration of treatment delivery timing and sequence creates a nonconvex nonlinear treatment planning problem that is too computationally expensive to solve in a time-sensitive clinical setting. We develop multiple optimisation techniques to identify the most suitable one for a clinical workflow. While a convex underestimator approach provides slightly improved solutions, it requires several orders of magnitude more computational resources than local optimisation approaches that reach similar performance in terms of plan quality. In consultation with our clinical collaborators, we devise a BED treatment planning workflow that further reduces the possible planning times by combining pre-computation of candidate solutions with interactive exploration and refinement of the final treatment plans. To evaluate this workflow, we develop a prototype treatment planning framework. We show that BED optimisation removes the time dependence and further increases plan quality. The results of the proof-of-concept workflow demonstrate the feasibility of a future clinical application of BED planning in GK radiosurgery