Troy Long

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.

A DVH-guided IMRT optimization algorithm for automatic treatment planning and adaptive radiotherapy replanning

PURPOSE To develop a novel algorithm that incorporates prior treatment knowledge into intensity modulated radiation therapy optimization to facilitate automatic treatment planning and adaptive radiotherapy (ART) replanning. METHODS The algorithm automatically creates 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 among different portions of a DVH curve for an organ. In ART, the reference plan is the initial plan for the same patient, while for automatic treatment planning the reference plan is selected from a library of clinically approved and delivered plans of previously treated patients with similar medical conditions and geometry. The proposed algorithm employs a voxel-based optimization model and navigates the large voxel-based Pareto surface. 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 with the DVHs better than the reference ones. If the reference plan is too restricting for the new geometry, the algorithm generates a Pareto plan with DVHs close to the reference ones. In both cases, the new plans have similar DVH trade-offs as the reference plans. RESULTS The algorithm was tested using three patient cases and found to be able to automatically adjust the voxel-weighting factors in order to generate a Pareto plan with similar DVH trade-offs as the reference plan. The algorithm has also been implemented on a GPU for high efficiency. CONCLUSIONS A novel prior-knowledge-based optimization algorithm has been developed that 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 replanning and automatic treatment planning.

Shared data for intensity modulated radiation therapy (IMRT) optimization research: the CORT dataset

BackgroundWe provide common datasets (which we call the CORT dataset: common optimization for radiation therapy) that researchers can use when developing and contrasting radiation treatment planning optimization algorithms. The datasets allow researchers to make one-to-one comparisons of algorithms in order to solve various instances of the radiation therapy treatment planning problem in intensity modulated radiation therapy (IMRT), including beam angle optimization, volumetric modulated arc therapy and direct aperture optimization.ResultsWe provide datasets for a prostate case, a liver case, a head and neck case, and a standard IMRT phantom. We provide the dose-influence matrix from a variety of beam/couch angle pairs for each dataset. The dose-influence matrix is the main entity needed to perform optimizations: it contains the dose to each patient voxel from each pencil beam. In addition, the original Digital Imaging and Communications in Medicine (DICOM) computed tomography (CT) scan, as well as the DICOM structure file, are provided for each case.ConclusionsHere we present an open dataset – the first of its kind – to the radiation oncology community, which will allow researchers to compare methods for optimizing radiation dose delivery.

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.

FusionArc optimization: A hybrid volumetric modulated arc therapy (VMAT) and intensity modulated radiation therapy (IMRT) planning strategy

PURPOSE To introduce a hybrid volumetric modulated arc therapy/intensity modulated radiation therapy (VMAT/IMRT) optimization strategy called FusionArc that combines the delivery efficiency of single-arc VMAT with the potentially desirable intensity modulation possible with IMRT. METHODS A beamlet-based inverse planning system was enhanced to combine the advantages of VMAT and IMRT into one comprehensive technique. In the hybrid strategy, baseline single-arc VMAT plans are optimized and then the current cost function gradients with respect to the beamlets are used to define a metric for predicting which beam angles would benefit from further intensity modulation. Beams with the highest metric values (called the gradient factor) are converted from VMAT apertures to IMRT fluence, and the optimization proceeds with the mixed variable set until convergence or until additional beams are selected for conversion. One phantom and two clinical cases were used to validate the gradient factor and characterize the FusionArc strategy. Comparisons were made between standard IMRT, single-arc VMAT, and FusionArc plans with one to five IMRT∕hybrid beams. RESULTS The gradient factor was found to be highly predictive of the VMAT angles that would benefit plan quality the most from beam modulation. Over the three cases studied, a FusionArc plan with three converted beams achieved superior dosimetric quality with reductions in final cost ranging from 26.4% to 48.1% compared to single-arc VMAT. Additionally, the three beam FusionArc plans required 22.4%-43.7% fewer MU∕Gy than a seven beam IMRT plan. While the FusionArc plans with five converted beams offer larger reductions in final cost--32.9%-55.2% compared to single-arc VMAT--the decrease in MU∕Gy compared to IMRT was noticeably smaller at 12.2%-18.5%, when compared to IMRT. CONCLUSIONS A hybrid VMAT∕IMRT strategy was implemented to find a high quality compromise between gantry-angle and intensity-based degrees of freedom. This optimization method will allow patients to be simultaneously planned for dosimetric quality and delivery efficiency without switching between delivery techniques. Example phantom and clinical cases suggest that the conversion of only three VMAT segments to modulated beams may result in a good combination of quality and efficiency.

Sensitivity analysis for lexicographic ordering in radiation therapy treatment planning

PURPOSE To introduce a method to efficiently identify and calculate meaningful tradeoffs between criteria in an interactive IMRT treatment planning procedure. The method provides a systematic approach to developing high-quality radiation therapy treatment plans. METHODS Treatment planners consider numerous dosimetric criteria of varying importance that, when optimized simultaneously through multicriteria optimization, yield a Pareto frontier which represents the set of Pareto-optimal treatment plans. However, generating and navigating this frontier is a time-consuming, nontrivial process. A lexicographic ordering (LO) approach to IMRT uses a physicians criteria preferences to partition the treatment planning decisions into a multistage treatment planning model. Because the relative importance of criteria optimized in the different stages may not necessarily constitute a strict prioritization, the authors introduce an interactive process, sensitivity analysis in lexicographic ordering (SALO), to allow the treatment planner control over the relative sequential-stage tradeoffs. By allowing this flexibility within a structured process, SALO implicitly restricts attention to and allows exploration of a subset of the Pareto efficient frontier that the physicians have deemed most important. RESULTS Improvements to treatment plans over a LO approach were found by implementing the SALO procedure on a brain case and a prostate case. In each stage, a physician assessed the tradeoff between previous stage and current stage criteria. The SALO method provided critical tradeoff information through curves approximating the relationship between criteria, which allowed the physician to determine the most desirable treatment plan. CONCLUSIONS The SALO procedure provides treatment planners with a directed, systematic process to treatment plan selection. By following a physicians prioritization, the treatment planner can avoid wasting effort considering clinically inferior treatment plans. The planner is guided by criteria importance, but given the information necessary to accurately adjust the relative importance at each stage. Through these attributes, the SALO procedure delivers an approach well balanced between efficiency and flexibility.

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.

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.

Threshold-driven optimization for reference-based auto-planning

We study threshold-driven optimization methodology for automatically generating a treatment plan that is motivated by a reference DVH for IMRT treatment planning. We present a framework for threshold-driven optimization for reference-based auto-planning (TORA). Commonly used voxel-based quadratic penalties have two components for penalizing under- and over-dosing of voxels: a reference dose threshold and associated penalty weight. Conventional manual- and auto-planning using such a function involves iteratively updating the preference weights while keeping the thresholds constant, an unintuitive and often inconsistent method for planning toward some reference DVH. However, driving a dose distribution by threshold values instead of preference weights can achieve similar plans with less computational effort. The proposed methodology spatially assigns reference DVH information to threshold values, and iteratively improves the quality of that assignment. The methodology effectively handles both sub-optimal and infeasible DVHs. TORA was applied to a prostate case and a liver case as a proof-of-concept. Reference DVHs were generated using a conventional voxel-based objective, then altered to be either infeasible or easy-to-achieve. TORA was able to closely recreate reference DVHs in 5-15 iterations of solving a simple convex sub-problem. TORA has the potential to be effective for auto-planning based on reference DVHs. As dose prediction and knowledge-based planning becomes more prevalent in the clinical setting, incorporating such data into the treatment planning model in a clear, efficient way will be crucial for automated planning. A threshold-focused objective tuning should be explored over conventional methods of updating preference weights for DVH-guided treatment planning.