E Romeijn

4π Non-Coplanar Liver SBRT: A Novel Delivery Technique

PURPOSE To improve the quality of liver stereotactic body radiation therapy (SBRT) treatments, a novel 4π framework was developed with accompanying algorithms to optimize non-coplanar beam orientations and fluences. The dose optimization is performed on a patient-specific deliverable beam geometry solution space, parameterized with patient and linear accelerator gantry orientations. METHODS AND MATERIALS Beams causing collision between the gantry and the couch or patient were eliminated by simulating all beam orientations using a precise computer assisted design model of the linear accelerator and a human subject. Integrated beam orientation and fluence map optimizations were performed on remaining beams using a greedy column generation method. Testing of the new method was performed on 10 liver SBRT cases previously treated with 50 to 60 Gy in 5 fractions using volumetric modulated arc therapy (VMAT). For each patient, both 14 and 22 non-coplanar fields were selected and optimized to meet the objective of ≥95% of the planning target volume (PTV) covered by 100% of the prescription dose. Doses to organs at risk, normal liver volumes receiving <15 Gy, integral dose, and 50% dose spillage volumes were compared against the delivered clinical VMAT plans. RESULTS Compared with the VMAT plans, the 4π plans yielded reduced 50% dose spillage volume and integral dose by 22% (range 10%-40%) and 19% (range 13%-26%), respectively. The mean normal liver volume receiving <15 Gy was increased by 51 cc (range 21-107 cc) with a 31% reduction of the mean normal liver dose. Mean doses to the left kidney and right kidney and maximum doses to the stomach and spinal cord were on average reduced by 70%, 51%, 67%, and 64% (P≤.05). CONCLUSIONS This novel 4π non-coplanar radiation delivery technique significantly improved dose gradient, reduced high dose spillage, and improved organ at risk sparing compared with state of the art VMAT plans.

4π noncoplanar stereotactic body radiation therapy for centrally located or larger lung tumors.

PURPOSE To investigate the dosimetric improvements in stereotactic body radiation therapy for patients with larger or central lung tumors using a highly noncoplanar 4π planning system. METHODS AND MATERIALS This study involved 12 patients with centrally located or larger lung tumors previously treated with 7- to 9-field static beam intensity modulated radiation therapy to 50 Gy. They were replanned using volumetric modulated arc therapy and 4π plans, in which a column generation method was used to optimize the beam orientation and the fluence map. Maximum doses to the heart, esophagus, trachea/bronchus, and spinal cord, as well as the 50% isodose volume, the lung volumes receiving 20, 10, and 5 Gy were minimized and compared against the clinical plans. A dose escalation study was performed to determine whether a higher prescription dose to the tumor would be achievable using 4π without violating dose limits set by the clinical plans. The deliverability of 4π plans was preliminarily tested. RESULTS Using 4π plans, the maximum heart, esophagus, trachea, bronchus and spinal cord doses were reduced by 32%, 72%, 37%, 44%, and 53% (P≤.001), respectively, and R50 was reduced by more than 50%. Lung V20, V10, and V5 were reduced by 64%, 53%, and 32% (P≤.001), respectively. The improved sparing of organs at risk was achieved while also improving planning target volume (PTV) coverage. The minimal PTV doses were increased by the 4π plans by 12% (P=.002). Consequently, escalated PTV doses of 68 to 70 Gy were achieved in all patients. CONCLUSIONS We have shown that there is a large potential for plan quality improvement and dose escalation for patients with larger or centrally located lung tumors using noncoplanar beams with sufficient quality and quantity. Compared against the clinical volumetric modulated arc therapy and static intensity modulated radiation therapy plans, the 4π plans yielded significantly and consistently improved tumor coverage and critical organ sparing. Given the known challenges in central structure dose constraints in stereotactic body radiation therapy to the lung, 4π planning may increase efficacy and reduce toxicity.

Feasibility of prostate robotic radiation therapy on conventional C-arm linacs

PURPOSE Significant dosimetric improvement for radiation therapy using optimized noncoplanar fields has been previously demonstrated. The purpose here is to study the feasibility of optimized robotic noncoplanar radiation therapy, termed 4π therapy, for prostate cancer treatments on a conventional C-arm linac. METHODS AND MATERIALS Twelve low-risk prostate cancer patients previously treated by 2-arc volumetric modulated arc therapy (VMAT) were selected. Forty gray in 5 fractions were prescribed to cover 95% of the prostate planning target volume (PTV). To replan by 4π therapy, a column generation method was used to optimize beam orientations and fluence. A total of 30 beams were selected for each patient. RESULTS Both planning methods provided adequate PTV coverage. Compared against VMAT plans, the 4π plan reduced the rectum V50%, V80%, V90%, D1cc, and the penile bulb maximum doses by 50%, 28%, 19% 11%, and 9% (P < .005), respectively, and the mean body dose was reduced from 2.07 Gy to 1.75 Gy (P = .0001). The bladder dose was only slightly reduced. CONCLUSIONS By optimizing beam angles and fluences in the noncoplanar solution space, superior prostate treatment plan quality was achieved compared against state of the art VMAT plans. The dosimetric potential for 4π therapy is established on an existing C-arm linac platform.

Integral dose investigation of non-coplanar treatment beam geometries in radiotherapy

PURPOSE Automated planning and delivery of non-coplanar plans such as 4π radiotherapy involving a large number of fields have been developed to take advantage of the newly available automated couch and gantry on C-arm gantry linacs. However, there is an increasing concern regarding the potential changes in the integral dose that needs to be investigated. METHODS A digital torso phantom and 22 lung and liver stereotactic body radiation therapy (SBRT) patients were included in the study. The digital phantom was constructed as a water equivalent elliptical cylinder with a major axis length of 35.4 cm and minor axis of 23.6 cm. A 4.5 cm diameter target was positioned at varying depths along the major axis. Integral doses from intensity modulated, non-coplanar beams forming a conical pattern were compared against the equally spaced coplanar beam plans. Integral dose dependence on the phantom geometry and the beam number was also quantified. For the patient plans, the non-coplanar and coplanar beams and fluences were optimized using a column generation and pricing approach and compared against clinical VMAT plans using two full (lung) or partial coplanar arcs (liver) entering at the side proximal to the tumor. Both the average dose to the normal tissue volume and the total volumes receiving greater than 2 Gy (V2) and 5 Gy (V5) were evaluated and compared. RESULTS The ratio of integral dose from the non-coplanar and coplanar plans depended on the tumor depth for the phantom; for tumors shallower than 10 cm, the non-coplanar integral doses were lower than coplanar integral doses for non-coplanar angles less than 60°. Similar patterns were observed in the patient plans. The smallest non-coplanar integral doses were observed for tumor 6-8 cm deep. For the phantom, the integral dose was independent of the number of beams, consistent with the liver SBRT patients but the lung SBRT patients showed slight increase in the integral dose when more beams were used. Larger tumor size and larger patient body size did not change the overall relationship of integral doses between non-coplanar and coplanar cases. However, the thin disk-shaped tumor received at least 40% greater integral doses with the non-coplanar plans. Overall, patient non-coplanar integral doses and V5 were comparable to those of coplanar doses from the same optimization engine and 15%-20% lower than state of the art VMAT plans. However, non-coplanar beams significantly increased V2 in both the phantom and patients. On average, the lung and liver SBRT patient normal tissue volumes receiving dose greater than 2 Gy were increased by 749 and 532 cm(3), respectively. CONCLUSIONS The authors used a digital phantom simulating a patient torso and 22 SBRT patients to show that the integral doses from the plans employing optimized non-coplanar beams are comparable to those of the coplanar plans using an equal number of discrete beams and are significantly lower than those of VMAT plans. The non-coplanar beams expose a larger normal tissue volume to non-zero doses, whose impact will need to be evaluated individually to determine the risk/benefit ratio of the non-coplanar plans.

A Degradation-Informed Battery-Swapping Policy for Fleets of Electric or Hybrid-Electric Vehicles

Motivated by high oil prices, several large fleet companies initiated future plans to hybridize their fleets to establish immunity of their optimized business models against severe oil price fluctuations, and adhere to increasing awareness of environmentally friendly solutions. The hybridization projects increased maintenance costs especially for costly and degradable components such as Li-ion batteries. This paper introduces a degradation-based resource allocation policy to optimally utilize batteries on fleet level. The policy, denoted as degradation-based swapping optimization, incorporates optimal implementation of swapping and substitution actions throughout a plan of finite-time horizon to minimize projected maintenance costs. The swapping action refers to the interchange in the placement of two batteries within a fleet. The substitution action refers to the replacement of degraded batteries with new ones. The policy takes advantage of the different degradation rates of the state of health of the batteries because of different loading conditions, achieving optimal placement at different time intervals throughout the plan horizon. A mathematical model for the policy is provided. The optimization of the generated model is studied through several algorithms. Numerical results for sample problems are obtained to illustrate the capability of the proposed policy in establishing substantial savings in the projected maintenance costs compared to other policies.

TH‐A‐116‐09: A Novel Prior‐Knowledge‐Based Optimization Algorithm for Automatic Treatment Planning and Adaptive Radiotherapy Re‐Planning

PURPOSE To develop a novel algorithm that takes existing prior-knowledge information into account in optimization to facilitate automatic treatment planning and adaptive radiotherapy (ART) re-planning. METHODS We developed an algorithm to automatically create a treatment plan guided by the DVH curves of a reference plan that contains information on the clinician approved dose-volume trade-offs among different targets/organs and within the same organ. This method has applications in automatic treatment planning and ART re-planning. In ART, the reference plan is the initial plan for the same patient, while for automatic treatment planning the reference plan is selected (based on patient similarity) from a library of clinically approved and delivered plans of previously treated patients with similar medical conditions. The proposed algorithm employs a voxel-based optimization model and approximates the large voxel-based Pareto surface iteratively. The voxel weights are iteratively adjusted to approach a plan that is similar to the reference plan in terms of the DVHs. If the reference plan is feasible but not Pareto optimal, the algorithm generates a Pareto optimal plan consistent with the reference DVHs. If the reference plan is too restricting, the algorithm generates a Pareto plan with DVHs close to the reference ones. RESULTS The algorithm was tested using a series of patient cases and found to be able to automatically adjust the voxel-weighting factors automatically in order to generate a Pareto plan with DVHs similar to the reference plan. The algorithm has been implemented on GPU for high efficiency. CONCLUSION A novel prior-knowledge-based optimization algorithm has been developed that uses the prior knowledge in optimization process to automatically adjust the voxel weights and generate a clinical optimal plan at high efficiency. It is found that the new algorithm can significantly improve the plan quality and planning efficiency in ART re-planning and automatic treatment planning.

TH‐C‐137‐02: Robotic Radiotherapy Using Intermediate Beam Energies

PURPOSE Intermediate energy (1-2MV) x-rays have steeper depth dose drop-off and sharper penumbra than commonly used 6MV x-rays. Dosimetry benefits of these characteristics are studied on a robotic non-coplanar planning and delivery platform. METHODS Dose of 1MV and 6MV x-rays was calculated using the convolution/superposition algorithm with heterogeneity correction and Monte Carlo calculated dose kernels. The X-ray spectrum was adjusted to match depth dose curves of published data. Thirty noncoplanar beams were selected by a pricing approach from a candidate beam pool, which consisted of 1162 uniformly distributed non-coplanar beams minus beams leading to collision. The collision model was fit to individual treatment sites. Fluence optimization based on 5 mm MLC was performed after adding each beam. Identical objective functions for PTV and organs-at-risk (OARs) were employed in the 3 planning scenarios: 1 MV alone, 6 MV alone and the combination of 1 MV and 6 MV beams (1&6 MV) with the prescription dose covering 95% of the PTV. Four representative cases from the following anatomical sites were included in the study: head and neck, partial breast, lung and liver. RESULTS 1 MV and 1&6 MV plans provided superior OAR sparing for head, liver, partial breast and lung cases while maintaining the same PTV coverage. Compared with 6 MV plan, 1 MV plans reduced the integral dose by 25%, 23%, 19% and 9% for lung, breast, head and liver cases respectively. The plan quality of 1&6 MV plans, which primarily was slightly superior to that of the 1MV only plans. CONCLUSION The dosimetric drawbacks of intermediate energy x-rays are higher skin doses and shallower penetration when few of them are used on a coplanar platform but these drawbacks were effectively overcome on a highly non-coplanar treatment planning platform, where its advantages of normal tissue sparing and sharp penumbra are manifested.

TH‐AB‐BRB‐08: Optimizing Global Liver Function in Liver SBRT Treatment Planning

Purpose: Liver SBRT patients have variable pre-treatment liver function, which strongly influences liver toxicity risk. Previous studies include a “perfusion-weighted” mean dose model as a surrogate for liver function in plan optimization. This work investigates the benefit of using a more sophisticated dose-response model that incorporates two important dose thresholds: (i) based on pre-treatment function, a significant low-dose may need to be exceeded before noticeable damage is done, and (ii) a high-dose saturation point, beyond which no additional damage is done. Methods: Voxel-based liver perfusion from DCE-MRI was obtained. Two optimization models subject to the same linear dose constraints (e.g., minimum target EUD, maximum critical structure dose) were compared: the original linear model, which use pre-treatment perfusion-weighted mean liver dose only, and a new model designed to directly optimize the predicted post-treatment global liver function. The latter, a highly non-linear non-convex problem, was approximately solved by optimizing a piece-wise linear approximation of the objective function with a customized mixed-integer linear programming (MILP) based algorithm. 2D synthetic and 3D clinical cases were studied to assess the potential benefit of the new model. Results: Compared to those of the original model, dose distributions for the functional dose response model take advantage of voxels with saturated damage to deliver the required dose to the target instead of inflicting additional damage on other well-functioning voxels. This leads to an improvement in predicted post-treatment global liver function. The customized solution method also improves optimization efficiency compared to solving the MILP conventionally. Conclusion: Functional imaging such as DCE-MRI can be used during treatment planning to maximize potential post-treatment function. However, simple approximations such as minimization of perfusion-weighted mean dose may not be sufficient. Alternatively, a dose-response model that reflects response threshold and saturation effects was shown to improve the prediction of post-treatment function in liver SBRT. Supported by P01 CA 059827

TH-A-9A-04: Incorporating Liver Functionality in Radiation Therapy Treatment Planning

PURPOSE Liver SBRT patients have both variable pretreatment liver function (e.g., due to degree of cirrhosis and/or prior treatments) and sensitivity to radiation, leading to high variability in potential liver toxicity with similar doses. This work aims to explicitly incorporate liver perfusion into treatment planning to redistribute dose to preserve well-functioning areas without compromising target coverage. METHODS Voxel-based liver perfusion, a measure of functionality, was computed from dynamic contrast-enhanced MRI. Two optimization models with different cost functions subject to the same dose constraints (e.g., minimum target EUD and maximum critical structure EUDs) were compared. The cost functions minimized were EUD (standard model) and functionality-weighted EUD (functional model) to the liver. The resulting treatment plans delivering the same target EUD were compared with respect to their DVHs, their dose wash difference, the average dose delivered to voxels of a particular perfusion level, and change in number of high-/low-functioning voxels receiving a particular dose. Two-dimensional synthetic and three-dimensional clinical examples were studied. RESULTS The DVHs of all structures of plans from each model were comparable. In contrast, in plans obtained with the functional model, the average dose delivered to high-/low-functioning voxels was lower/higher than in plans obtained with its standard counterpart. The number of high-/low-functioning voxels receiving high/low dose was lower in the plans that considered perfusion in the cost function than in the plans that did not. Redistribution of dose can be observed in the dose wash differences. CONCLUSION Liver perfusion can be used during treatment planning potentially to minimize the risk of toxicity during liver SBRT, resulting in better global liver function. The functional model redistributes dose in the standard model from higher to lower functioning voxels, while achieving the same target EUD and satisfying dose limits to critical structures. This project is funded by MCubed and grant R01-CA132834.

MO‐A‐137‐06: A Stochastic Optimization Approach to Adaptive Lung Radiation Therapy Treatment Planning

PURPOSE We have demonstrated that the ratio between a lung cancer patients TGFβ1 level two weeks into treatment versus pre-treatment is a predictive biomarker for radiation induced lung toxicity (RILT). Instead of adapting to this new information only when the data is available, we present a stochastic optimization model that can explicitly incorporate this future knowledge into adaptive lung radiation therapy treatment planning. METHODS A two-stage stochastic treatment plan optimization model is developed that accommodates knowledge of a patients predisposition to RILT that would be obtained during treatment. The goal of the stochastic model is to maximize the population probability of local tumor control (LTC) after 2 years while controlling the probability of RILT. Two adaptive strategies are considered: (1) bounding the expected proportion of the entire population that will experience RILT, and (2) bounding the probability that each individual patient will experience RILT. These strategies were compared to a non-adaptive treatment planning strategy that uses a simple bound on the mean lung dose to control the probability of RILT. RESULTS This technique is applied to lung cancer cases that are treated in daily fractions over a six-week period. The tradeoffs between the probability of RILT and LTC are observed for each strategy. Both adaptive strategies dominate (are always better than) the non-adaptive treatment plans in the worst-case sense (i.e., highest probability among all treated patients), while adaptive strategy (1) dominates it in the population sense in terms of the tradeoff between probability of RILT and expected probability of LTC. CONCLUSION By incorporating future knowledge into a two-stage stochastic IMRT treatment planning model, superior adaptive treatment plans can be obtained that improve both population and worst-case outcomes for patients. In addition, this modeling paradigm provides treatment planners a tool to help personalize treatment plans for individual patients. Troy Long is on a NSF Graduate Research Fellowship. Data collection was assisted by R01CA142840 grant.