[en] Radiotherapy planning involves inherent tradeoffs: the primary mission, to treat the tumor with a high, uniform dose, is in conflict with normal tissue sparing. We seek to understand these tradeoffs on a case-to-case basis, by computing for each patient a database of Pareto optimal plans. A treatment plan is Pareto optimal if there does not exist another plan which is better in every measurable dimension. The set of all such plans is called the Pareto optimal surface. This article presents an algorithm for computing well distributed points on the (convex) Pareto optimal surface of a multiobjective programming problem. The algorithm is applied to intensity-modulated radiation therapy inverse planning problems, and results of a prostate case and a skull base case are presented, in three and four dimensions, investigating tradeoffs between tumor coverage and critical organ sparing

[en] To quantify radiation exposure of radiation therapy technologists (RTTs) in a proton treatment facility in comparison with a photon therapy facility, to inform and establish these specialized occupational safety guidelines.

[en] Purpose: To introduce and demonstrate a practical multiobjective treatment planning procedure for intensity-modulated radiation therapy (IMRT) planning. Methods and Materials: The creation of a database of Pareto optimal treatment plans proceeds in two steps. The first step solves an optimization problem that finds a single treatment plan which is close to a set of clinical aspirations. This plan provides an example of what is feasible, and is then used to determine mutually satisfiable hard constraints for the subsequent generation of the plan database. All optimizations are done using linear programming. Results: The two-step procedure is applied to a brain, a prostate, and a lung case. The plan databases created allow for the selection of a final treatment plan based on the observed tradeoffs between the various organs involved. Conclusions: The proposed method reduces the human iteration time common in IMRT treatment planning. Additionally, the database of plans, when properly viewed, allows the decision maker to make an informed final plan selection

[en] The purpose of the work was to evaluate the dosimetric uncertainties of an analytical dose calculation engine and the impact on treatment plans using small fields in intracranial proton stereotactic radiosurgery (PSRS) for a gantry based double scattering system. 50 patients were evaluated including 10 patients for each of 5 diagnostic indications of: arteriovenous malformation (AVM), acoustic neuroma (AN), meningioma (MGM), metastasis (METS), and pituitary adenoma (PIT). Treatment plans followed standard prescription and optimization procedures for PSRS. We performed comparisons between delivered dose distributions, determined by Monte Carlo (MC) simulations, and those calculated with the analytical dose calculation algorithm (ADC) used in our current treatment planning system in terms of dose volume histogram parameters and beam range distributions. Results show that the difference in the dose to 95% of the target (D95) is within 6% when applying measured field size output corrections for AN, MGM, and PIT. However, for AVM and METS, the differences can be as great as 10% and 12%, respectively. Normalizing the MC dose to the ADC dose based on the dose of voxels in a central area of the target reduces the difference of the D95 to within 6% for all sites. The generally applied margin to cover uncertainties in range (3.5% of the prescribed range  +  1 mm) is not sufficient to cover the range uncertainty for ADC in all cases, especially for patients with high tissue heterogeneity. The root mean square of the R 90 difference, the difference in the position of distal falloff to 90% of the prescribed dose, is affected by several factors, especially the patient geometry heterogeneity, modulation and field diameter. In conclusion, implementation of Monte Carlo dose calculation techniques into the clinic can reduce the uncertainty of the target dose for proton stereotactic radiosurgery. If MC is not available for treatment planning, using MC dose distributions to adjust the delivered doses level can also reduce uncertainties below 3% for mean target dose and 6% for the D95. (paper)

[en] Purpose: To test whether multicriteria optimization (MCO) can reduce treatment planning time and improve plan quality in intensity-modulated radiotherapy (IMRT). Methods and Materials: Ten IMRT patients (5 with glioblastoma and 5 with locally advanced pancreatic cancers) were logged during the standard treatment planning procedure currently in use at Massachusetts General Hospital (MGH). Planning durations and other relevant planning information were recorded. In parallel, the patients were planned using an MCO planning system, and similar planning time data were collected. The patients were treated with the standard plan, but each MCO plan was also approved by the physicians. Plans were then blindly reviewed 3 weeks after planning by the treating physician. Results: In all cases, the treatment planning time was vastly shorter for the MCO planning (average MCO treatment planning time was 12 min; average standard planning time was 135 min). The physician involvement time in the planning process increased from an average of 4.8 min for the standard process to 8.6 min for the MCO process. In all cases, the MCO plan was blindly identified as the superior plan. Conclusions: This provides the first concrete evidence that MCO-based planning is superior in terms of both planning efficiency and dose distribution quality compared with the current trial and error–based IMRT planning approach.

[en] Definition of the clinical target volume (CTV) is one of the weakest links in the radiation therapy chain. In particular, inability to account for uncertainties is a severe limitation in the traditional CTV delineation approach. Here, we introduce and test a new concept for tumor target definition, the clinical target distribution (CTD). The CTD is a continuous distribution of the probability of voxels to be tumorous. We describe an approach to incorporate the CTD in treatment plan optimization algorithms, and implement it in a commercial treatment planning system. We test the approach in two synthetic and two clinical cases, a sarcoma and a glioblastoma case. The CTD is straightforward to implement in treatment planning and comes with several advantages. It allows one to find the most suitable tradeoff between target coverage and sparing of surrounding healthy organs at the treatment planning stage, without having to modify or redraw a CTV. Owing to the variable probabilities afforded by the CTD, a more flexible and more clinically meaningful sparing of critical structure becomes possible. Finally, the CTD is expected to reduce the inter-user variability of defining the traditional CTV. (paper)

[en] Gliomas differ from many other tumors as they grow infiltratively into the brain parenchyma rather than forming a solid tumor mass with a well-defined boundary. Tumor cells can be found several centimeters away from the central tumor mass that is visible using current imaging techniques. The infiltrative growth characteristics of gliomas question the concept of a radiotherapy target volume that is irradiated to a homogeneous dose—the standard in current clinical practice. We discuss the use of the Fisher–Kolmogorov glioma growth model in radiotherapy treatment planning. The phenomenological tumor growth model assumes that tumor cells proliferate locally and migrate into neighboring brain tissue, which is mathematically described via a partial differential equation for the spatio-temporal evolution of the tumor cell density. In this model, the tumor cell density drops approximately exponentially with distance from the visible gross tumor volume, which is quantified by the infiltration length, a parameter describing the distance at which the tumor cell density drops by a factor of e. This paper discusses the implications for the prescribed dose distribution in the periphery of the tumor. In the context of the exponential cell kill model, an exponential fall-off of the cell density suggests a linear fall-off of the prescription dose with distance. We introduce the dose fall-off rate, which quantifies the steepness of the prescription dose fall-off in units of Gy mm"−"1. It is shown that the dose fall-off rate is given by the inverse of the product of radiosensitivity and infiltration length. For an infiltration length of 3 mm and a surviving fraction of 50% at 2 Gy, this suggests a dose fall-off of approximately 1 Gy mm"−"1. The concept is illustrated for two glioblastoma patients by optimizing intensity-modulated radiotherapy plans. The dose fall-off rate concept reflects the idea that infiltrating gliomas lack a defined boundary and are characterized by a continuous fall-off of the density of infiltrating tumor cells. The approach can potentially be used to individualize the prescribed dose distribution if better methods to estimate radiosensitivity and infiltration length on a patient by patient basis become available. (paper)

[en] Purpose: Patients with brain metastases are often treated with whole brain radiation therapy (WBRT) for purposes of palliation. The treatment of those who experience subsequent intracranial disease progression can include a second course of WBRT, although there is controversy surrounding its safety and efficacy. This study examines the outcomes in patients at Massachusetts General Hospital who underwent reirradiation. Patients and Methods: We examined the medical records of 17 patients at Massachusetts General Hospital with brain metastases who were initially treated with WBRT between 2002 and 2008 and were subsequently retreated with a second course of WBRT. The median dose for the first course of WBRT was 35 Gy (range, 28–40 Gy), with a fraction size of 2 to 3 Gy (median, 2.5 Gy). The median dose at reirradiation was 21.6 Gy (range, 14–30 Gy), with a fraction size of 1.5 to 2 Gy (median, 1.8 Gy). Results: The second course of WBRT was administered upon radiographic disease progression in all patients. Of 10 patients with complete follow-up data, 8 patients experienced complete or partial symptom resolution, and 2 did not show clinical improvement. The time to radiographic progression was 5.2 months. The median overall survival for all patients after diagnosis of metastases was 24.7 months. The median survival time after initiation of reirradiation was 5.2 months (95% CI, 1.3–8.7). In 6 patients with stable extracranial disease, the median survival time after retreatment was 19.8 months (95% CI, 2.7–∞), compared with 2.5 months (95% CI, 0.8–5.5) for those with extracranial disease progression (p = 0.05). Acute adverse reactions occurred in 70.5% of patients but were mild to moderate in severity. Conclusion: In select patients and especially those with stable extracranial disease, reirradiation may be an appropriate and effective intervention to provide symptomatic relief and slow intracranial disease progression. Side effects were minimal and did not cause substantial changes in quality of life.

[en] Glioblastoma differ from many other tumors in the sense that they grow infiltratively into the brain tissue instead of forming a solid tumor mass with a defined boundary. Only the part of the tumor with high tumor cell density can be localized through imaging directly. In contrast, brain tissue infiltrated by tumor cells at low density appears normal on current imaging modalities. In current clinical practice, a uniform margin, typically two centimeters, is applied to account for microscopic spread of disease that is not directly assessable through imaging. The current treatment planning procedure can potentially be improved by accounting for the anisotropy of tumor growth, which arises from different factors: anatomical barriers such as the falx cerebri represent boundaries for migrating tumor cells. In addition, tumor cells primarily spread in white matter and infiltrate gray matter at lower rate. We investigate the use of a phenomenological tumor growth model for treatment planning. The model is based on the Fisher–Kolmogorov equation, which formalizes these growth characteristics and estimates the spatial distribution of tumor cells in normal appearing regions of the brain. The target volume for radiotherapy planning can be defined as an isoline of the simulated tumor cell density. This paper analyzes the model with respect to implications for target volume definition and identifies its most critical components. A retrospective study involving ten glioblastoma patients treated at our institution has been performed. To illustrate the main findings of the study, a detailed case study is presented for a glioblastoma located close to the falx. In this situation, the falx represents a boundary for migrating tumor cells, whereas the corpus callosum provides a route for the tumor to spread to the contralateral hemisphere. We further discuss the sensitivity of the model with respect to the input parameters. Correct segmentation of the brain appears to be the most crucial model input. We conclude that the tumor growth model provides a method to account for anisotropic growth patterns of glioma, and may therefore provide a tool to make target delineation more objective and automated. (paper)

[en] The definition of the clinical target volume (CTV) is becoming the weakest link in the radiotherapy chain. CTV definition consensus guidelines include the geometric expansion beyond the visible gross tumor volume, while avoiding anatomical barriers. In a previous publication we described how to implement these consensus guidelines using deep learning and graph search techniques in a computerized CTV auto-delineation process. In this paper we address the remaining problem of how to deal with uncertainties in positions of the anatomical barriers. The objective was to develop an algorithm that implements the consensus guidelines on considering barrier uncertainties. Our approach is to perform multiple expansions using the fast marching method with barriers in place or removed at different stages of the expansion. We validate the algorithm in a computational phantom and compare manually generated with automated CTV contours, both taking barrier uncertainties into account. (note)