Multi-objective optimization framework to plan laser ablation procedure for prostate tumors through a genetic algorithm

Background and objectives: Prostate cancer is the most common form of cancer in the male population. While the survival rate is high, many patients undergo surgical procedures for prostate cancer that might never progress to clinical significance. As a result, minimally invasive therapies are increasingly preferred over chemotherapy, radiotherapy, or surgical interventions. Laser-induced hyperthermia is emerging as a promising minimally invasive technique that targets tumoral tissue without damaging the surrounding healthy prostate. However, the lack of a standardized protocol makes the procedure highly dependent on the surgeon's expertise. Indeed, besides the cancerous tissue, also the healthy one could be heated and undergo a necrosis. Consequently, two contrasting objectives have to be considered during the treatment design: to treat cancer without damaging healthy tissue. Therefore, in this work, a thorough multi-objective optimization is carried out with reference to the laser-induced thermal ablation framework for prostate tumors. This is achieved by coupling finite element simulations with a genetic algorithm-based optimization to identify the best settings for the procedure. Methods: A multi-objective optimization was conducted to determine the optimal settings for decision variables to achieve the best outcomes. The procedure was executed by the direct coupling between the genetic algorithm which continuously updated the decision variables, for the optimization, and a finite element commercial code to predict variables. The decision variables employed as input for the model were: procedure time, number of laser probes, their position, dimensions, delivered power, and the number of ON/OFF cycles. Pennes’ bioheat equation was employed to obtain the desired objective functions, say thermal damage in the tumor tissue and healthy prostate, to be maximized and minimized, respectively. Additionally, linear regression and Bayesian artificial neural networks were developed to correlate the design variables with the objective functions, providing a tool for optimizing treatment planning. Results: Results demonstrate that the multi-objective genetic algorithm is a powerful tool for selecting the optimal settings for treatment. By applying the utopian criterion, the best trade off is achieved, since the optimal solution is the one allowing for a complete tumor necrosis with an acceptable damage rate to the healthy prostate (188 mm3). Linear regressions proved ineffective for predicting the objective functions, while artificial neural networks yielded better results. Conclusions: This study introduces an effective methodology for optimizing laser-induced thermal ablation for prostate tumors. By coupling genetic algorithms with finite element simulations, a set of optimal protocols (in terms of time and laser settings) can be selected, ensuring maximal tumor necrosis with minimal damage to the healthy prostate. This approach assists surgeons in protocol planning and reduces uncertainty in outcomes.