Brachytherapy (BT) is an essential component in the curative treatment of cervical cancer. With commercial BT implant devices, called applicators, the radioactive sources can only be positioned in fixed intracavitary channels or in a fixed array of interstitial needles. Patient-tailored BT applicators containing individualised needle channels have the potential to enhance the delivered dose to the tumour while minimising tissue damage of the organs-at-risk (OARs). During the optimisation process of the needle channels, several constraints must be considered. The needle paths in the applicator are curvature-constrained, and the needles should not collide with each other or potentially perforate OARs. Furthermore, clinicians can impose additional constraints based on their preferences.

Existing approaches in literature for optimising needle placement in patient-tailored BT applicators for cervical cancer treatment have employed various algorithms. However, these approaches often rely on significant approximations. For instance, some assume that all needles are inserted in parallel or focus solely on optimising geometric coverage as opposed to optimising the dose distribution. Moreover, the few methods in literature incorporating needle path planning in the optimisation process require manually pre-specified dwell positions. To improve dose conformity and minimise the dependence on the clinician’s expertise and time, there is a need for the development of software that optimises the needle placement and paths without resorting to these severe assumptions.
This work proposes and validates a novel three-stage approach to generate a patient-tailored applicator configuration without resorting to fully geometric optimisation. First, a high-potential set of dwell positions is obtained by running a modified version of the dwell time optimisation software BiCycle (Erasmus Medical Centre, Rotterdam, the Netherlands) on a grid of possible dwell positions. This results in a resolution optimal treatment plan when not considering geometry and applicator constraints. The positions with the highest dwell times according to the resolution optimal treatment plan are selected in the high-potential set. Next, a weighted set cover problem is used iteratively to find a combination of feasible needle segments to cover all dwell points in the high-potential set at a minimum distance. Lastly, needle channels to steer the needle to these segments are simultaneously optimised to be of minimum curvature and mutually collision-free.

To evaluate our approach, a virtual configuration and planning study was performed in a cohort of 22 locally advanced cervical cancer patients previously treated with the Venezia (Elekta AB, Stockholm, Sweden). The resulting treatment plans of the clinically used configuration were compared with the resulting treatment plans of the proposed patient-tailored and grid configurations on clinically relevant dose-volume histogram parameters, dwell times, conformity index and number of interstitial needles. Statistical significance is assessed with Wilcoxon signed-rank tests.

The proposed workflow was demonstrated to be feasible, and for every patient, a configuration could be generated in clinically acceptable time. All treatment plans generated for the grid configuration, the patient-tailored configuration and the clinical configuration were acceptable following the EMBRACE ll aims. Planning aims, however, were met more frequently with both the grid configurations (145/151 instances) and the patient-tailored configurations (137/151 instances) in comparison with the clinically used configurations (119/151 instances). The treatment plans generated with the grid configurations obtained significantly better (p < 0.01) median normalised CTVIR D98 dose with respect to the clinical configurations. Moreover, with the grid configurations, there was a significant improvement in median normalised D2cm3 dose for the bladder (p < 0.001), rectum (p < 0.001), sigmoid (p < 0.01) and bowel (p < 0.001) compared to treatment plans obtained with the clinical configurations. The treatment plans generated with the patient-tailored configurations obtained comparable target doses, but median normalised D2cm3 doses for the bladder (p < 0.001), rectum (p < 0.01) and bowel (p < 0.01) were significantly better for the patient-tailored configurations compared to the clinical configurations.

The proposed automated patient-tailored BT source channel configuration planning method was demonstrated to be clinically feasible. The resulting treatment plans have dosimetric advantages over the treatment plans generated with the clinical applicator configuration. Improvements to the intracavitary dwell position placement are expected to further increase dose conformity.

The dose computation algorithm, or dose engine, is one of the fundamental parts of radiotherapy treatment planning. These algorithms predict how the dose will be distributed inside the patient.
Current dose engines are mainly based on either Monte Carlo simulations (MC) or pencil beam algorithms (PBA). MC being very precise, but relatively slow. PBA being quicker, but generally lacking accuracy. Since treatment planning requires both high speed and accuracy, one would prefer MC accuracy with even higher speeds than PBA. A recent study showed a possible solution based on deep-learning called the Dose Transformation Algorithm (DoTA). This deep-learning algorithm is capable of doing MC accurate dose calculations and is faster than PBA (Pastor-Serrano and Perk ́o 2022).
In this project, a feasibility study has been performed on the integration of DoTA as a dose engine in actual treatment planning. The treatment planning system (TPS) in this study is Erasmus- iCycle (Breedveld, Storchi, et al. 2012). This study included the creation of an algorithm to do dose computations with DoTA for any given set of parameters given by the TPS. Subsequently, the dose computations by DoTA have been compared to those computed by Erasmus-iCycle’s current dose engine, ASTROID (Kooy et al. 2010). Analyses on these dose computations included comparisons in a homogeneous water box, alternative homogeneous matter and patient geometries. Two main sources of discrepancy between DoTA and ASTROID where the beam’s range and the beam model used by ASTROID, compared to what DoTA was trained on. Both dose engines likely use a different interpretation of the proton stopping power, leading to range discrepancies up to 14.9% for 200 MeV beamlets when projected in a homogeneous matter of 1000 Hounsfield Units (HUs). Comparing the dose distributions in water, the maximum dose discrepancy around the Bragg peak (BP) for a 80 MeV beam was about 60.0%, due to the width of the beam being larger for DoTA. The mean dose discrepancy in water reached a maximum of 18.9%. In a patient geometry, the range differences made the mean discrepancies go up to a maximum of 22.9%, as expected from the range discrepancies found earlier. Implementation of different gantry and beamlet angles increased the discrepancies, likely caused by the interpolation required to perform calculations under these angles. In terms of distributed energy, the models were closer, with the mean discrepancy decreasing to maximum of 7.1%. Computations of two treatment plan dose distributions showed that the discrepancies arising from this beam model and range difference were to large to achieve viable dose volume histograms. A two lateral beam plan showed the better results of the two plans with an under dosing of 20.8%, likely due to robustness occasionally compensating for the range discrepancy.

Over 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.

A 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.