M Zarepisheh

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.

A multicriteria framework with voxel‐dependent parameters for radiotherapy treatment plan optimization

PURPOSE To establish a new mathematical framework for radiotherapy treatment optimization with voxel-dependent optimization parameters. METHODS In the treatment plan optimization problem for radiotherapy, a clinically acceptable plan is usually generated by an optimization process with weighting factors or reference doses adjusted for a set of the objective functions associated to the organs. Recent discoveries indicate that adjusting parameters associated with each voxel may lead to better plan quality. However, it is still unclear regarding the mathematical reasons behind it. Furthermore, questions about the objective function selection and parameter adjustment to assure Pareto optimality as well as the relationship between the optimal solutions obtained from the organ-based and voxel-based models remain unanswered. To answer these questions, the authors establish in this work a new mathematical framework equipped with two theorems. RESULTS The new framework clarifies the different consequences of adjusting organ-dependent and voxel-dependent parameters for the treatment plan optimization of radiation therapy, as well as the impact of using different objective functions on plan qualities and Pareto surfaces. The main discoveries are threefold: (1) While in the organ-based model the selection of the objective function has an impact on the quality of the optimized plans, this is no longer an issue for the voxel-based model since the Pareto surface is independent of the objective function selection and the entire Pareto surface could be generated as long as the objective function satisfies certain mathematical conditions; (2) All Pareto solutions generated by the organ-based model with different objective functions are parts of a unique Pareto surface generated by the voxel-based model with any appropriate objective function; (3) A much larger Pareto surface is explored by adjusting voxel-dependent parameters than by adjusting organ-dependent parameters, possibly allowing for the generation of plans with better trade-offs among different clinical objectives. CONCLUSIONS The authors have developed a mathematical framework for radiotherapy treatment optimization using voxel-based parameters. The authors can improve the plan quality by adjusting voxel-based weighting factors and exploring the unique and large Pareto surface which include all the Pareto surfaces that can be generated by organ-based model using different objective functions.

A moment-based approach for DVH-guided radiotherapy treatment plan optimization

The dose-volume histogram (DVH) is a clinically relevant criterion to evaluate the quality of a treatment plan. It is hence desirable to incorporate DVH constraints into treatment plan optimization for intensity modulated radiation therapy. Yet, the direct inclusion of the DVH constraints into a treatment plan optimization model typically leads to great computational difficulties due to the non-convex nature of these constraints. To overcome this critical limitation, we propose a new convex-moment-based optimization approach. Our main idea is to replace the non-convex DVH constraints by a set of convex moment constraints. In turn, the proposed approach is able to generate a Pareto-optimal plan whose DVHs are close to, or if possible even outperform, the desired DVHs. In particular, our experiment on a prostate cancer patient case demonstrates the effectiveness of this approach by employing two and three moment formulations to approximate the desired DVHs.

Dosimetric benefit of adaptive re-planning in pancreatic cancer stereotactic body radiotherapy

Stereotactic body radiotherapy (SBRT) shows promise in unresectable pancreatic cancer, though this treatment modality has high rates of normal tissue toxicity. This study explores the dosimetric utility of daily adaptive re-planning with pancreas SBRT. We used a previously developed supercomputing online re-planning environment (SCORE) to re-plan 10 patients with pancreas SBRT. Tumor and normal tissue contours were deformed from treatment planning computed tomographies (CTs) and transferred to daily cone-beam CT (CBCT) scans before re-optimizing each daily treatment plan. We compared the intended radiation dose, the actual radiation dose, and the optimized radiation dose for the pancreas tumor planning target volume (PTV) and the duodenum. Treatment re-optimization improved coverage of the PTV and reduced dose to the duodenum. Within the PTV, the actual hot spot (volume receiving 110% of the prescription dose) decreased from 4.5% to 0.5% after daily adaptive re-planning. Within the duodenum, the volume receiving the prescription dose decreased from 0.9% to 0.3% after re-planning. It is noteworthy that variation in the amount of air within a patient׳s stomach substantially changed dose to the PTV. Adaptive re-planning with pancreas SBRT has the ability to improve dose to the tumor and decrease dose to the nearby duodenum, thereby reducing the risk of toxicity.

An Automated Treatment Plan Quality Control Tool for Intensity-Modulated Radiation Therapy Using a Voxel-Weighting Factor-Based Re-Optimization Algorithm

Intensity-modulated radiation therapy (IMRT) currently plays an important role in radiotherapy, but its treatment plan quality can vary significantly among institutions and planners. Treatment plan quality control (QC) is a necessary component for individual clinics to ensure that patients receive treatments with high therapeutic gain ratios. The voxel-weighting factor-based plan re-optimization mechanism has been proved able to explore a larger Pareto surface (solution domain) and therefore increase the possibility of finding an optimal treatment plan. In this study, we incorporated additional modules into an in-house developed voxel weighting factor-based re-optimization algorithm, which was enhanced as a highly automated and accurate IMRT plan QC tool (TPS-QC tool). After importing an under-assessment plan, the TPS-QC tool was able to generate a QC report within 2 minutes. This QC report contains the plan quality determination as well as information supporting the determination. Finally, the IMRT plan quality can be controlled by approving quality-passed plans and replacing quality-failed plans using the TPS-QC tool. The feasibility and accuracy of the proposed TPS-QC tool were evaluated using 25 clinically approved cervical cancer patient IMRT plans and 5 manually created poor-quality IMRT plans. The results showed high consistency between the QC report quality determinations and the actual plan quality. In the 25 clinically approved cases that the TPS-QC tool identified as passed, a greater difference could be observed for dosimetric endpoints for organs at risk (OAR) than for planning target volume (PTV), implying that better dose sparing could be achieved in OAR than in PTV. In addition, the dose-volume histogram (DVH) curves of the TPS-QC tool re-optimized plans satisfied the dosimetric criteria more frequently than did the under-assessment plans. In addition, the criteria for unsatisfied dosimetric endpoints in the 5 poor-quality plans could typically be satisfied when the TPS-QC tool generated re-optimized plans without sacrificing other dosimetric endpoints. In addition to its feasibility and accuracy, the proposed TPS-QC tool is also user-friendly and easy to operate, both of which are necessary characteristics for clinical use.

TH‐C‐137‐10: Development of a GPU Research Platform for Automatic Treatment Planning and Adaptive Radiotherapy Re‐Planning

PURPOSE To develop a research platform called SCORE (Super Computing Online Re-planning Environment) for automatic treatment planning and adaptive radiotherapy re-planning based on GPU. METHODS Our software is a Graphical User Interface (GUI) based on the Qt framework that allows users to easily and quickly create a new treatment plan based on a reference plan. It consists of several modules, including loading plan and patient geometry from DICOM RT files, automatic and manual rigid registration, deformable registration for contour propagation, previous plan based automatic plan optimization, physician-driven plan tuning, final dose calculation, and plan exporting in DICOM RT format. For automatic planning, a reference plan is identified from a library of previously delivered plans and it is used to guide the optimization process. For adaptive radiotherapy re-planning, the original plan of the same patient is used as the reference plan to guide the optimization process to generate a new plan on the new patient geometry defined by either a new CT or Cone-Beam CT image. All the computation modules have been implemented in CUDA to achieve high efficiency. The results for each step of the workflow can be visualized for review, revision, and approval. RESULTS SCORE has been well tested and validated for prostate and head/neck cancer cases. The validation was done by comparing SCORE plans against the same plans with re-calculated dose distributions and DVHs using a commercial planning system. We found that SCORE can generate clinically optimal treatment plans that are realistic and deliverable. The plans can be automatically created in only a few minutes, followed by another few minutes of physician fine-tuning using an interactive GUI. CONCLUSION We have developed a very efficient and user-friendly GPU-based research platform that can be used for clinical research on automatic treatment planning and adaptive radiotherapy re-planning.

SU‐E‐CAMPUS‐T‐02: Selecting Reference Patients for Automatic Treatment Planning Using Multiple Geometrical Features

Purpose: To discover the geometric features that best identify an anatomically‐similar reference patient from a library of previous treatments to predict achievable dose distributions for automatic IMRT treatment planning of a new patient. Methods: Using a library of previously‐treated patients, two similarity functions are defined in the domain of the dose distributions and geometrical features that match a patient with their most similar member from the cohort. The goal is to attain the same patient matches from these similarity functions, so that when a new patient without dosimetric information is input into the geometric similarity function a similar patient from the library can be identified with good dosimetric predictive power for the new patient. The dosimetric similarity function is defined by a weighted sum of the difference area between two DVHs for all organs and PTV, with the weights tuned to reflect clinical priorities. The generalized geometric similarity function was defined as a nonlinear combination of organ/PTV volume, organ/PTV overlapping volume, and Dice index between a pair of two organs/PTVs/intersections. A genetic‐algorithm‐based learning technique was then employed to find the optimal combination of geometrical features to predict patient similarity. Results: For a database of 10 prostate cancer treated patients, a combination of bladder volume and (PTV, rectum) overlapping Dice index achieved 80% accuracy compared to the DVH similarity function. Validation on a library of 15 patients reduced the accuracy to 60%, implying that other geometric features could be needed. Conclusion: We have developed a method using machine learning to identify the best match of a new patient to the prior patients to guide automatic IMRT planning. Initial results from this feasibility study have modest predictive power but future work will focus in the incorporation of other geometric features, e.g. mutual information and overlap‐volume‐histograms, to increase selection accuracy.

SU‐E‐T‐588: A Novel IMRT Plan Optimization Algorithm for Physician‐Driven Plan Tuning

PURPOSE To greatly speed up the time-consuming treatment planning procedure by developing a new optimization algorithm allowing the physicians to interactively fine-tune the DVHs and iso-dose lines of an IMRT plan. METHODS The conventional treatment planning procedure is a time-consuming and resource-demanding task that may need multiple iterations between the dosimetrists and clinicians after an initial plan is developed. We develop a new optimization algorithm to speed up this procedure by allowing the physician to interactively fine-tune the DVH and iso-dose lines on top of the initially optimized plan. After the physician modifies the DVH and iso-dose lines of the current plan towards a more desired one through an interactive graphical interface, the algorithm will adjust the voxel-dependent optimization parameters to guide the plan towards the modified one. To ease the fine-tuning procedure, the algorithm enables the physician to lock some DVH curves as well as to change the priorities of the organs. The algorithm explores a large Pareto surface by adjusting voxel-dependent parameters to find out a plan that is the closest to the physicians desired plan. RESULTS The algorithm was tested using a series of clinically realistic patient data and found to have desirable performance. It can adjust the voxel-dependent parameters and guide a plan towards the physician-driven fine-tuned plan. This algorithm has been implemented on GPU for high efficiency, and the updating procedure is near real time. CONCLUSION The conventional treatment planning procedure can be significantly improved in terms of efficiency and physician satisfactory by utilizing the new GPU-based algorithm allowing the physician to interactively fine-tune the plan in near real time.

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.

TU‐G‐BRB‐07: Improve PTV Dose Distribution by Using Spatial Information in IMRT Optimization

Purpose: The spatial distribution of radiation dose within the PTV does have clinical significance. For instance, if hot/cold spots in the PTV cannot be avoided, high doses are preferred to be located in the center, and low doses at the peripheries. However, traditional plan optimization models for IMRT usually treat equally voxels inside the same structure, failing to incorporate those location preferences. We present a re‐optimization model that, while preserving the quality of an initial treatment plan represented by DVH curves, incorporates spatial information for voxels inside the PTV into the optimization to generate a more desirable dose distribution. Methods: Our re‐optimization model incorporates a convex function that penalizes the deviation of the dose received by each voxel from an individual reference value. For PTV, the reference values per voxel match the ideal redistribution of the initial PTV dose, where voxels close to the boundary receive the low doses, while voxels in the center receive the high doses. For OAR, the reference value per voxel corresponds to its dose from the initial plan. In addition to the structure‐based weighting factors in traditional planning approaches, we incorporated individual penalty weights for PTV voxels. Structure‐based factors are calibrated according to the difference from the reference DVH curves, while voxel‐based, according to the difference to reference value. Results: We tested our model in four gynecologic cancer cases. For each case, we compare the resulting dose distribution within the PTV to that from the initial plan. It is observed that without sacrificing the plan quality represented by DVH curves, our re‐optimization model generates more desirable PTV dose distributions. Conclusions: We have presented a re‐optimization model that, by incorporating spatial location information for PTV voxels, yields to more clinically favorable dose distributions with similar DVH curves. This work is supported by Varian Medical Systems through a Master Research Agreement.