Introduction: High Dose Rate (HDR) Brachytherapy is a radiotherapy modality that involves temporarily introducing a highly radioactive source into the target volume with the use of an applicator. With respect to HDR brachytherapy for prostate cancer, an 192Iridium source is driven into the target volume through catheters implanted into the prostate. The dose delivered to a point in the prostate depends on the time the source dwells at a given position. Treatment planning for brachytherapy involve the optimization of dwell times and dwell positions. The aim of the treatment plan is to deliver the prescribed dose to the target volume, the prostate, while minimizing the dose to the organs at risk (OAR), namely the urethra, bladder and rectum. In current clinical practice, the process of treatment planning involves the manual manipulation of the parameters of an optimizer until the desired dose distribution is achieved. This implies that the plan quality depends on the experience of the planner, and there is variation in plan quality between planners. The aim of this project was to develop an automated treatment planning system that would able to generate clinically acceptable plans with minimal human intervention. The brachytherapy treatment planning module is named B-iCycle and may be integrated in the future with the treatment planning software suite, called Erasmus-iCycle, developed at the Erasmus MC. Materials and methods: At the core of the treatment planning system (TPS) is a precise and fast dose engine that is able to simulate the dose to be delivered. In this project, we employ the TG-43 dose calculation formalism as it is the most widely implemented method in dose engines for brachytherapy treatment planning systems. The dose engine is then verified against the dose engine of the clinical treatment planning system. B-iCycle uses the 2-phase ϵ-constraint (2pϵc) algorithm to optimize the dwell times and positions. The 2pϵc algorithm requires a ‘wish-list’, which encapsulates the treatment protocol as goals and constraints for each critical structure. For this project three treatment protocols were chosen, four fractions of 9.5 Gy, single fraction of 19 Gy and single fraction of 20 Gy, and wish-lists were generated for each protocol. Three patient groups with different catheter geometries were selected. Treatment plans were generated for each patient and compared against the plans that were generated, for the same patients, in the clinic. The treatment plans that were generated in B-iCycle were then exported to the clinical treatment planning system (Oncentra from Elekta) to obtain the dose characteristics. The plans were compared based on the dose characteristics and the Conformity Index (COIN). The plans were also verified by a radiation oncologist. Results: The TG-43 dose engine was successfully verified against the clinical dose engine. The Gamma analysis showed that only 0.68% of the voxels failed the gamma analysis and these voxels were located within the catheters therefore they can be ignored as no tissue lies at these positions. With regard to plans that were generated, the physician confirmed that the clinically acceptable B-iCycle plans are very comparable to the clinical plans. The B-iCycle plans are better at minimizing the dose to the urethra. When comparing B-iCycle plans to the clinical plans using COIN, B-iCycle was found to be better than the clinical procedure. B-iCycle can generate a treatment plan in approximately 10 seconds, which is much faster than the clinical procedure, which averages at 10 minutes. It is also able to avoid the issue of treatment planner variability and is able to generate consistent, high quality treatment plans.

In the history of medicine, different treatments for cancer have been developed, such as radiotherapy, surgery and chemotherapy. However, nowadays more than 50% of the people diagnosed with cancer, undergo a radiotherapy treatment. One of the devices used for the treatment is the CyberKnife: a robotic radiodurgery system that can move around the patient using 102 virtually placed nodes. For the treatment of a patient, usually around 25 nodes are used, which result in a certain plan quality for the patient. The aim of this project is to find shorter paths than the existing ones, without significantly degrading the plan quality. This is done by using Dijkstra’s Algorithm, as well as incorporating Hamiltonian paths and the Traveling Salesman Problem. With these techniques, we developed the OPA (Optimal Path Algorithm): an algorithm that finds Hamiltonian paths through the nodes, combined with the iterated process of interchanging nodes with adjacent ones. With OPA, the traveltimes for the CyberKnife have been brought down by 38.6% on average without degrading the plan quality.