314 research outputs found
TROTS - The Radiotherapy Optimisation Test Set
No full textThe Radiotherapy Optimisation Test Set (TROTS) is an extensive set of problems originating from radiotherapy (radiation therapy) treatment planning. This dataset is created for 2 purposes: (1) to supply a large-scale dense dataset to measure performance and quality of mathematical solvers, and (2) to supply a dataset to investigate the multi-criteria optimisation and decision-making nature of the radiotherapy problem. The dataset contains 120 problems (patients), divided over 6 different treatment protocols/tumour types. Each problem contains numerical data, a configuration for the optimisation problem, and data required to visualise and interpret the results. The data is stored as HDF5 compatible Matlab files, and includes scripts to work with the dataset. The set as present in this version is of date 13 May 2019. Updated versions of the Scripts and other extensions can be found at the following pages: Persistent page with links: Erasmus University Rotterdam Library Mirror project page: TROTS Mirror Main publication: S. Breedveld & B. Heijmen, Data for TROTS - The Radiotherapy Optimisation Test Set, Data in Brief 12 (2017) 143-14
Evaluating VHEE radiotherapy treatment plans for prostate and lung cancer
No full textOver 50% of cancer patients will receive radiotherapy treatment at least once. Most patients are receiving photon radiotherapy. In this work very high energy electron (VHEE) radiotherapy is being studied as a potential replacement for photon radiotherapy. VHEE beams have a favorable depth dependence and the penumbra of VHEE pencil beams stays small deep inside the patient. Therefore, using VHEE radiotherapy can potentially result in a lower dose being delivered to the organs at risk (OAR) in comparison with clinically used volumetric modulated arc therapy (VMAT). New accelerator techniques allow VHEE beam generators to fit in standard radiotherapy treatment bunkers. Therefore, VHEE therapy can reduce the equipment costs in comparison with proton therapy and it can increase the treatment quality in comparison with photon therapy. In this work the VHEE treatment plans are compared with clinically used VMAT treatment plans for prostate and lung cancer. VHEE treatment plans were generated for 6 patients with prostate cancer and 3 patients with lung cancer. First, the pencil beam dose distributions were calculated for each patients using a Monte Carlo particle simulation tool called TOPAS MC. Thereafter, the optimal intensities of the pencil beams were calculated using iCycle, an automated optimization tool, which calculates the optimal treatment plan. The VHEE treatment plans are generated with 9, 18 and 36 beams and the energies that are used are: 100, 200, 300 and 400 MeV. The treatment plans were normalized to a 99% PTV coverage of 95% of the prescribed dose.For the prostate case, the 100 MeV VHEE treatment plans deliver more dose to the organs at risk (OARs) than the VMAT plan. The 18 beam 300 and 400 MeV VHEE treatment plans showed a dose reduction in the mean dose of the patient, the rectum, the anus and the bladder, but a dose increase to the left and right femoral heads. The 18 beam 400 MeV treatment plan reduced the dose to all OARs in comparison with the VMAT plan, for the lung cases. Increasing the number of beams of the VHEE treatment plan reduces the dose to the OARs, for both the prostate and the lung cases. Increasing the energy of the electron beams also reduces the OAR dose for both cases.VHEE plans reduce the dose to the healthy tissue, while keeping the PTV dose constant and can therefore be considered as a possible replacement for photon radiotherapy.Applied Science
Modeling ray angles in deep learning based dose calculation algorithms
No full textA fundamental tool in radiotherapy treatment planning is the dose calculation algorithm, which models the dose that will be distributed for given beam parameters and patient geometry. Various available algorithms include Monte Carlo simulations (MC) and pencil beam algorithms (PBA), with the former being computationally expensive but offering high precision and the latter sacrificing precision for speed. A recent study presents the deep-learning based Dose Transformer Algorithm (DoTA) which provides MC accuracy at speeds 33 times faster than PBA. However, as currently implemented, DoTA dose computations assume that each ray enters the patient geometry perpendicularly, while clinical treatment plans consist of many diverging rays with angles of entry up to 5°. In this project, we extend the current model to include angular dependency. The resulting models DoTA-A and DoTA-S improve on DoTA by including angle of entry as an additional input on top of the beam energy and patient geometry. DoTA-A includes the actual angle values as input, while for DoTA-S an expected beam shape is precalculated with a trajectory based on the angle of entry. A training dataset of more than 30.000 samples with MC baseline dose is generated from a public patient dataset, using a 2 mm resolution. The architecture of the models is similar to that of DoTA, with convolutional layers extracting important spatial features from the input geometry and a transformer layer using a self-attention mechanism to weigh token inter-dependence.The models DoTA-A and DoTA-S are evaluated and compared on different test sets with MC baseline doses. Both models are shown to be more accurate than PBA, with DoTA-S having the best performance by most metrics. We demonstrate the relevance of ray angles in dose calculations by comparing DoTA-A and DoTA-S to perpendicular MC predictions, which were considered ground-truth for DoTA. The models DoTA-A and DoTA-S compute dose distributions at an average speed of 10 ms to 15 ms per dose, with the predictions achieving an average relative error of 1% across various test sets. The average relative error of the perpendicular MC predictions lies around 3%, demonstrating the importance of angle of entry as an input variable in dose calculation algorithms. The gamma pass rates (for δ=1%, Δ=3mm) of a full treatment plan with dose distributions predicted by our models are 97.60% for DoTA-A and 95.74% for DoTA-S, indicating that there is no strictly better model between the two.Applied Mathematic
Fully automated treatment planning of spinal metastases – A comparison to manual planning of Volumetric Modulated Arc Therapy for conventionally fractionated irradiation
No full text_Background:_ Planning for Volumetric Modulated Arc Therapy (VMAT) may be time consuming and its use is limited by available staff resources. Automated multicriterial treatment planning can eliminate this bottleneck. We compared automatically created (auto) VMAT plans generated by Erasmus-iCycle to manually created VMAT plans for treatment of spinal metastases.
_Methods:_ Forty-two targets in 32 patients were analyzed. Lungs and kidneys were defined as organs at risk (OARs). Twenty-two patients received radiotherapy on kidney levels, 17 on lung levels, and 3 on both levels.
_Results:_ All Erasmus-iCycle plans were clinically acceptable. When compared to manual plans, planning target volume (PTV) coverage of auto plans was significantly better. The Homogeneity Index did not differ significantly between the groups. Mean dose to OARs was lower in auto plans concerning both kidneys and the left lung. One hotspot (>110% of D50%) occurred in the spinal cord of one auto plan (33.2 Gy, D50%: 30 Gy). Treatment time was 7% longer in auto plans.
_Conclusions:_ Erasmus-iCycle plans showed better target coverage and sparing of OARs at the expense of minimally longer treatment times (for which no constraint was set)
Data for TROTS – The Radiotherapy Optimisation Test Set
No full textThe Radiotherapy Optimisation Test Set (TROTS) is an extensive set of problems originating from radiotherapy (radiation therapy) treatment planning. This dataset is created for 2 purposes: (1) to supply a large-scale dense dataset to measure performance and quality of mathematical solvers, and (2) to supply a dataset to investigate the multi-criteria optimisation and decision-making nature of the radiotherapy problem. The dataset contains 120 problems (patients), divided over 6 different treatment protocols/tumour types. Each problem contains numerical data, a configuration for the optimisation problem, and data required to visualise and interpret the results. The data is stored as HDF5 compatible Matlab files, and includes scripts to work with the dataset
Individualized Selection of Beam Angles and Treatment Isocenter in Tangential Breast Intensity Modulated Radiation Therapy
No full textPurpose and Objective Propose a novel method for individualized selection of beam angles and treatment isocenter in tangential breast intensity modulated radiation therapy (IMRT). Methods and Materials For each patient, beam and isocenter selection starts with the fully automatic generation of a large database of IMRT plans (up to 847 in this study); each of these plans belongs to a unique combination of isocenter position, lateral beam angle, and medial beam angle. The imposed hard planning constraint on patient maximum dose may result in plans with unacceptable target dose delivery. Such plans are excluded from further analyses. Owing to differences in beam setup, database plans differ in mean doses to organs at risk (OARs). These mean doses are used to construct 2-dimensional graphs, showing relationships between: (1) contralateral breast dose and ipsilateral lung dose; and (2) contralateral breast dose and heart dose (analyzed only for left-sided). The graphs can be used for selection of the isocenter and beam angles with the optimal, patient-specific tradeoffs between the mean OAR doses. For 30 previously treated patients (15 left-sided and 15 right-sided tumors), graphs were generated considering only the clinically applied isocenter with 121 tangential beam angle pairs. For 20 of the 30 patients, 6 alternative isocenters were also investigated. Results Computation time for automatic generation of 121 IMRT plans took on average 30 minutes. The generated graphs demonstrated large variations in tradeoffs between conflicting OAR objectives, depending on beam angles and patient anatomy. For patients with isocenter optimization, 847 IMRT plans were considered. Adding isocenter position optimization next to beam angle optimization had a small impact on the final plan quality. Conclusion A method is proposed for individualized selection of beam angles in tangential breast IMRT. This may be especially important for patients with cardiac risk factors or an enhanced risk for the development of contralateral breast cancer
Identifying the most relevant cost-functions for radiotherapy treatment planning with multicriteria optimisation
No full textCancer is a disease that one of every three people will get in The Netherlands. One of the treatment methods for this disease is radiotherapy. Approximately half of all cancer patients will get radiotherapy at some point of their treatment. During radiotherapy cancer cells are destroyed with ionizing radiation, but healthy cells get destroyed too. When a patient gets treated with radiotherapy, the goal is to find a treatment plan which will destroy all of the cancer cells and as few healthy cells as possible. To reach this goal we want to make a unique treatment plan for every patient, because every patient is anatomical unique. We use a wish-list to generate this unique optimal treatment plan. This wish-list contains all of the demands of the physician. All of the demands can be written into cost-functions. We will use inverse multicriteria optimisation to find the most relevant cost-functions for every organ and the tumour (planning target volume (PTV)). The relevance of a cost-function can be obtained by determining the weight of a cost-function. We start with a non-linear problem and we use the Karush-Kuhn-Tucker conditions. We did not receive the desired solutions.Afterwards, we tried to find the optimal weights for a linear problem by writing it in the form of an absolute duality gap minimization problem. This gave the results we were hoping for.Applied Mathematic
Robust optimization in intensity modulated proton therapy treatment planning
No full textIntensity modulated proton therapy is an advanced radiotherapy technique that is used to treat cancer patients. In order to successfully treat a patient, sufficient dose to the tumor is required. However, during the fractionated treatment, multiple errors can cause a difference between the planned and actual dose delivery. To ensure adequate dose delivery in potential error scenarios, robust treatment plans are acquired as these are less sensitive to uncertainties inherent to proton therapy. However, robust optimization is challenging.First, as robust optimization accounts for multiple error scenarios, the time needed to generate optimal treatment plans increases significantly. Therefore, it is investigated in this thesis if the optimization time can be reduced while preserving treatment plan quality. This is investigated through two different methods. In the first method, the number of error scenarios accounted for during optimization is reduced. We found that this can significantly reduce optimization time, while improved target coverage and lower risk on side effects are obtained. However, the near-maximum dose to the tumor was found to be less favourable. The second method investigated is variance optimization. This method significantly reduces optimization time. However, for similar target coverage, the risk of side effects increases.Another challenge related to robust optimization is the increase in delivered dose to healthy tissues surrounding the tumor, which increases the risk of side effects. Therefore, it is investigated in this thesis if the risk of side effects can be lowered by allowing higher maximum dose to the tumor. It is found that this method indeed reduces the risk of side effects. However, the increased maximum dose to the tumor may not be clinically desired as the increase may lead to higher risks of other side effects: edema and fibrosis. The clinically desired trade-off between near-maximum dose and normal tissue sparing should be established.Applied MathematicsBiomedical Engineerin
Automatically configuring the reference point method for automated multi-objective treatment planning
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