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Radiotherapy planning is a critical aspect of cancer treatment, where theoptimal selection of beam directions and dose distributions significantlyimpacts treatment efficacy and patient outcomes. Traditionally, this processinvolves time-consuming manual trial-and-error methods, leading to suboptimaltreatment plans. To address this challenge, optimization strategies based onadvanced artificial intelligence (AI) techniques have been explored. This paperpresents an investigation into the application of AI-driven optimizationmethods for beam direction and dose distribution selection in radiotherapyplanning. The study proposes an approach utilizing Convolutional NeuralNetworks (CNN) to learn the relationship between patient anatomy and optimalbeam orientations. The CNN model is trained on a dataset comprising anatomicalfeatures and corresponding beam orientations, derived from a column generation(CG) algorithm. Additionally, Particle Swarm Optimization (PSO) and Grey WolfOptimization (GWO) algorithms are employed to optimize the CNN's weights andbiases to attain the Fluence Map Optimization (FMO) objective function.Experiments are conducted using data from 70 clinical prostate cancer patients.The results demonstrate the effectiveness of the CNN-PSO and CNN-GWO approachesin generating beam orientations that yield treatment plans with dosedistributions comparable to those obtained through traditional CG. DVH analysisof the resulting plans for different anatomical structures validates theaccuracy and feasibility of the CNN-GWO model in radiotherapy planning. Thefindings of this study highlight the potential of AI-driven optimizationstrategies to revolutionize radiotherapy planning by significantly reducingplanning time and enhancing treatment plan quality.

Optimization Strategies for Beam Direction and Dose Distribution Selection in Radiotherapy Planning