Treatment of early stage breast cancer is generally invasive to a patient's daily life while being treated. Therefore, to diminish the physical and psychological impact during and after recovery, a newly proposed minimally invasive therapy for early-stage breast cancer treatment is proposed. Within this study, this proposed treatment is modelled to gain more knowledge of the behavior of the treatment material in tissue. The treatment includes magnetic thermal ablation, which is combined with permanent Low Dose Rate (LDR) brachytherapy, both performed simultaneously.
The goal of the treatment is to diminish the physical and psychological impact during treatment and after recovery, since it requires only a single medical intervention. The treatment material consists of radioactive palladium-103 superparamagnetic iron-oxide nanoparticles (Pd-103 SPIONs) incorporated in a solid gel, forming a seed that is implanted into the tumor. To investigate the effectiveness and limitations of the combined therapy, computational simulations were performed in Matlab using the Finite Element Method (FEM).
These simulations allowed for the prediction of the treatment results, by calculating the temperature distribution based on Pennes' bioheat equation, the nanoparticle concentration distribution and the dose distribution over time. The sensitivity of the results to the relevant physical properties and optimization parameters was analyzed. The latter resulted in a recommended optimization approach that ultimately could be used for treatment planning. First, an initial simulation was performed using property values from literature. Then, the temperature and dose results were tested on their sensitivity to model parameter changes. The temperature model was found to be most sensitive to changes in the nanoparticle heat source value Qnp, to an increased heat conduction coefficient k and to a decreased blood perfusion rate wb. The cumulative dose results are sensitive to both the initial concentration ci and to a decreased diffusion coefficient D. It is concluded that accurate values for these temperature and concentration model parameters are necessary to perform relevant simulations.
Furthermore, the possible optimization parameters were identified. For dose optimization, these parameters are the activity of the nanoparticles A, which is not easily modified, and the initial nanoparticle concentration in the seeds ci, which affects the temperature distribution as well. The temperature distribution specific variables that were found, are the strength of the magnetic field H and the time t of magnetic field application, which both can be adjusted during the treatment. The seed location and number of seeds are two additional adjustable variables used for optimization of both temperature and dose distribution. Lastly, it was concluded that the internal radiation part of the treatment is limiting in the reaching treatment goals and in number of optimization possibilities, compared to the thermal ablation part. Therefore, treatment optimization should be performed on the dose distribution first. Because most limitations of the models are a result of the 2D representation and because these limitations strongly affect the outcomes of the models, it is recommended to transform these models to 3D. These limitations make it impossible to do proper treatment planning with the 2D model, which requires a 3D view of the results. With all these findings, this study has contributed by providing basic knowledge of the state-of-the-art early stage breast cancer combined therapy, bringing it one step closer to clinical implementation.