Explicit optimization of plan quality measures in intensity-modulated radiation therapy treatment planning

Conventional planning objectives in optimization of intensity-modulated radiotherapy treatment (IMRT) plans are designed to minimize the violation of dose-volume histogram (DVH) thresholds using penalty functions. Although successful in guiding the DVH curve towards these thresholds, conventional planning objectives offer limited control of the individual points on the DVH curve (doses-at-volume) used to evaluate plan quality. In this study, we abandon the usual penalty-function framework and propose planning objectives that more explicitly relate to DVH statistics. The proposed planning objectives are based on mean-tail-dose, resulting in convex optimization. We also demonstrate how to adapt a standard optimization method to the proposed formulation in order to obtain a substantial reduction in computational cost. We investigate the potential of the proposed planning objectives as tools for optimizing DVH statistics through juxtaposition with the conventional planning objectives on two patient cases. Sets of treatment plans with differently balanced planning objectives are generated using either the proposed or the conventional approach. Dominance in the sense of better distributed doses-at-volume is observed in plans optimized within the proposed framework, indicating that the DVH statistics are better optimized and more efficiently balanced using the proposed planning objectives

When is Better Best? A multiobjective perspective

Purpose: To identify the most informative methods for reporting results of treatment planning comparisons. Methods: Seven papers from the past year of International Journal of Radiation Oncology Biology Physics reported on comparisons of treatment plans for IMRT and IMAT. The papers were reviewed to identify methods of comparisons. Decision theoretical concepts were used to evaluate the study methods and highlight those that provide the most information. Results: None of the studies examined the correlation between objectives. Statistical comparisons provided some information but not enough to make provide support for a robust decision analysis. Conclusion: The increased use of treatment planning studies to evaluate different methods in radiation therapy requires improved standards for designing the studies and reporting the results

A Hierachical Evolutionary Algorithm for Multiobjective Optimization in IMRT

Purpose: Current inverse planning methods for IMRT are limited because they are not designed to explore the trade-offs between the competing objectives between the tumor and normal tissues. Our goal was to develop an efficient multiobjective optimization algorithm that was flexible enough to handle any form of objective function and that resulted in a set of Pareto optimal plans. Methods: We developed a hierarchical evolutionary multiobjective algorithm designed to quickly generate a diverse Pareto optimal set of IMRT plans that meet all clinical constraints and reflect the trade-offs in the plans. The top level of the hierarchical algorithm is a multiobjective evolutionary algorithm (MOEA). The genes of the individuals generated in the MOEA are the parameters that define the penalty function minimized during an accelerated deterministic IMRT optimization that represents the bottom level of the hierarchy. The MOEA incorporates clinical criteria to restrict the search space through protocol objectives and then uses Pareto optimality among the fitness objectives to select individuals. Results: Acceleration techniques implemented on both levels of the hierarchical algorithm resulted in short, practical runtimes for optimizations. The MOEA improvements were evaluated for example prostate cases with one target and two OARs. The modified MOEA dominated 11.3% of plans using a standard genetic algorithm package. By implementing domination advantage and protocol objectives, small diverse populations of clinically acceptable plans that were only dominated 0.2% by the Pareto front could be generated in a fraction of an hour. Conclusions: Our MOEA produces a diverse Pareto optimal set of plans that meet all dosimetric protocol criteria in a feasible amount of time. It optimizes not only beamlet intensities but also objective function parameters on a patient-specific basis

Lexicographic ordering: intuitive multicriteria optimization for IMRT

Optimization problems in IMRT inverse planning are inherently multicriterial since they involve multiple planning goals for targets and their neighbouring critical tissue structures. Clinical decisions are generally required, based on tradeoffs among these goals. Since the tradeoffs cannot be quantitatively determined prior to optimization, the decision-making process is usually indirect and iterative, requiring many repetitive optimizations. This situation becomes even more challenging for cases with a large number of planning goals. To address this challenge, a multicriteria optimization strategy called lexicographic ordering (LO) has been implemented and evaluated for IMRT planning. The LO approach is a hierarchical method in which the planning goals are categorized into different priority levels and a sequence of sub-optimization problems is solved in order of priority. This prioritization concept is demonstrated using two clinical cases (a simple prostate case and a relatively complex head and neck case). In addition, a unique feature of LO in a decision support role is discussed. We demonstrate that a comprehensive list of planning goals (e.g., ∼23 for the head and neck case) can be optimized using only a few priority levels. Tradeoffs between different levels have been successfully prohibited using the LO method, making the large size problem representations simpler and more manageable. Optimization time needed for each level was practical, ranging from ∼26 s to ∼217 s. Using prioritization, the LO approach mimics the mental process often used by physicians as they make decisions handling the various conflicting planning goals. This method produces encouraging results for difficult IMRT planning cases in a highly intuitive manner.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/58100/2/pmb7_7_006.pd

What is plan quality in radiotherapy? The importance of evaluating dose metrics, complexity, and robustness of treatment plans

Plan evaluation is a key step in the radiotherapy treatment workflow. Central to this step is the assessment of treatment plan quality. Hence, it is important to agree on what we mean by plan quality and to be fully aware of which parameters it depends on. We understand plan quality in radiotherapy as the clinical suitability of the delivered dose distribution that can be realistically expected from a treatment plan. Plan quality is commonly assessed by evaluating the dose distribution calculated by the treatment planning system (TPS). Evaluating the 3D dose distribution is not easy, however; it is hard to fully evaluate its spatial characteristics and we still lack the knowledge for personalising the prediction of the clinical outcome based on individual patient characteristics. This advocates for standardisation and systematic collection of clinical data and outcomes after radiotherapy. Additionally, the calculated dose distribution is not exactly the dose delivered to the patient due to uncertainties in the dose calculation and the treatment delivery, including variations in the patient set-up and anatomy. Consequently, plan quality also depends on the robustness and complexity of the treatment plan. We believe that future work and consensus on the best metrics for quality indices are required. Better tools are needed in TPSs for the evaluation of dose distributions, for the robust evaluation and optimisation of treatment plans, and for controlling and reporting plan complexity. Implementation of such tools and a better understanding of these concepts will facilitate the handling of these characteristics in clinical practice and be helpful to increase the overall quality of treatment plans in radiotherapy

Implications of Radiosensitizer and Radioprotector Factors in Refining the Dose-Volume Constraints and Radiobiological Models

Radiotherapy is a cornerstone of the modern treatment of many types of cancer, having both curative and palliative roles. It is estimated that more than half of cancer patients will need radiation therapy in the course of evolution. The goal of radiotherapy is to maximize tumor control, reducing adverse effects on normal tissues in close proximity at the same time. Improving the therapeutic ratio is the main goal of the efforts made to improve the technique and accuracy of the radiotherapy by using the targeting of the tumor volume with the help of the imaging guide and the dose conformation around the target volume. The use of the multi-leaf collimator (MLC) allowed a better coverage of the target volume in the irradiation field, thus reducing the unnecessary irradiation of healthy tissues. The use of radioprotective agents and radiosensitizers is another strategy to maximize the effect of radiotherapy. Recently, interest has focused on the design of irradiation protocols that exploit the differences in biology in terms of the response to irradiation between tumor cells and normal tissues

Monte Carlo Modelling for Photon and Proton Therapy in Heterogenous Tissue and Prosthesis Material

Treatment outcomes in radiotherapy can be improved by reducing uncertainties in patient set-up, beam delivery and dose distribution. Clarification of arrangements can minimize the dose distributed to normal tissues, and facilitate dose escalation. However, heterogeneity can increase any ambiguities associated with dose distribution. The treatment planning system (TPS) cannot effectively calculate dose distribution in complex heterogeneous areas, which increases uncertainty. This research aims to study microscopic dose distribution in temporal bone, cochlea and pancreatic stents as applicable to modern radiotherapy treatments. To achieve this aim a multiscale approach will be used, as it provides essential information about differences in dose distribution between TPS/clinical CT and Monte Carlo (MC)/Micro CT for photons and protons. In the first part of this study, two DICOM series of pancreatic cancer patients were used with an inserted stent. A new model includes the atomic composition of the stent material, and new stent contouring was introduced to overcome a CT artefact. A PRIMO Monte Carlo model was tuned and compared with the TPS dose distribution and a one-beam volume-modulated arc therapy (VMAT) plan was created. A significant dose difference was observed when comparing the new model and TPS, suggesting increased uncertainty of the dose distribution in clinical practice. An open-access DICOM format of the data for the resected temporal bone and cochlea tissue was used with the FLUKA MC code to imitate potential high-dose scenarios associated with VMAT using the FLOOD option. Twenty-three photon and proton energy levels ranging from 0.055 to 5.5 MeV for photons and 37.59 to 124.83 MeV for protons were simulated separately to calculate dose distribution. Micro CT data shows three density levels in the temporal bone and cochlea. The photon distribution in the low energy range 0.055-0.09 MeV, the largest proportion of the dose (48.8%) was deposited within high-density bone, whereas above 0.125 MeV, the change on dose distribution started to occur where there was greater deposition in low-density tissue, reaching 53%. The dose distribution in the soft bone's intermediate density was 26.4% at 0.07 MeV and dropped to 19.7% at 2.5 MeV. There is a 29% percentage difference in dose distribution on the soft bone between the low and high energy. The dose distribution did not change significantly in proton between the low, intermediate and high-density areas. The dose distribution in 37.59 MeV shows 54.86% in low density, 19.75% in intermediate density and 25.39% in high density. A similar outcome was observed in high energy 124.83 MeV, a dose distribution was 54.21% in low density, 19.79% intermediate density and 26% in high density.An advanced model was created to connect the results to a clinical routine when treating brain tumours using the VMAT technique. Cases were selected from 280 data sets of patients diagnosis with gliomas. Eleven different scenarios were identified. The advanced model shows five cases with an enhanced mean dose. The TPS overestimated the mean dose in all cases. In some instances, A significant mean dose variance of 8.8% was noticed in two cases. Extra cases were selected with a distance between the target and cochlea less than 1 cm. The cases show a significant difference in the mean dose and normal tissue complication probability (NTCP) models. A model was created to connect the results with Gamma Knife treatment. Thirty-four cases of schwannoma were used, and four revealed a significant difference in the scattering dose to the cochlea. The maximum difference in mean dose achieved reached 8.3%.Uncertainty due to dose distribution can affect treatment outcomes. For example, hearing loss and tinnitus can be side effects of brain cancer radiotherapy treatment. It was found that increasing the dose led to a corresponding increased dose reaching the cochlea. Increasing the model accuracy using micro-CT data and MC computation helps to control the dose to the cochlea by controlling dose distribution. In addition, pancreatic cancer can help achieve higher dose escalation to provide better outcomes to patients. Using dose-to-medium calculation, manufactures data associated with stent materials, and models based on Micro CT of resected organs can reveal uncertainty in dose distribution in heterogeneous areas