The advent of Knowledge-Based Planning (KBP) models has introduced a transformative approach to Intensity-Modulated Radiation Therapy (IMRT) treatment planning in breast cancer and lung cancer cases. This paper explores the application of KBP models in these specific cancer types, highlighting their potential to enhance treatment accuracy, efficiency, and patient outcomes. By leveraging historical treatment data and machine learning techniques, KBP-IMRT offers a data-driven framework for optimizing dose distributions, minimizing radiation exposure to healthy tissues, and improving overall treatment plan quality. Through a comprehensive review of the literature and clinical case studies, this paper underscores the advantages of KBP-IMRT, such as streamlined planning processes and improved plan consistency, while acknowledging the challenges associated with model development and implementation. As the field of radiotherapy continues to evolve, KBP models hold the promise of shaping the future of personalized and precise cancer treatment strategies.

 In large-scale, non-convex problems, adaptive stochastic global optimization methodologies are frequently used. But improving these methodsâ\euro™ search effectiveness and re- peatability frequently calls for well-customized methodologies. Intensity-modulated radiation treatment (IMRT) planning faces the critical but difficult problem of automatically selecting the beam angle. Despite numerous efforts, the clinical IMRT practice continues to be not particularly satisfying for the reason of the excessive processing of the inverse circumstance. The objective problem that we would discuss in this research is the 4-Dimensional Radiation Therapy (4DRT) Inverse Planning Problem. Previous research in this domain makes use of Particle Swarm Optimization (PSO) and Genetic Algorithms (GA). This article aims in proposing a hybrid model that combines both the Genetic Algorithm as well as the Particle Swarm Optimization and chooses the best resulting parameters. The proposition makes use of the concept of threading that allows both the GA, and the PSO to run in parallel at the same time.Â