Avanços no planeamento do tratamento de radioterapia impulsionados por abordagens computacionais inovadoras e pela aprendizagem

This interdisciplinary project unites experts from Computer Science, Mathematics, Operations Research, Radiation Oncology, Medical Physics, to develop versatile models and algorithms, aiming for significant advancements in optimisation in complex environments (non-linearity, non-convexity, multiobjective, uncertainty) with important applications in radiotherapy treatment planning. It is rooted in previous successful research results of the team. All approaches will be designed as general frameworks that can be applied to different treatment techniques (e.g. Intensity-Modulated Radiation Therapy and Intensity-Modulated Proton Therapy), showcasing adaptability and effectiveness for diverse patient needs and clinical scenarios, as well as numerous other practical applications across diverse domains. Main scientific objectives: 1. Develop new computational optimization approaches, based on nature-inspired randomized algorithms, capable of calculating Pareto-fronts for large-scale optimization problems characterized by non-linearities and non-convexities. 2. Develop new approaches for robust optimization, considering different concepts of robustness and interpreting robustness as a multiobjective concept on its own. 3. Study dynamic optimization problems where uncertainty can only be revealed at a cost. Main applied objectives: 1. Develop radiotherapy treatment planning approaches that explicitly consider lymphopenia mitigation constraints. There is currently no commercial or academic treatment planning system that allows treatment planning optimization aiming at maximizing the sparing of the immune system. 2. Integrate biological optimization into planning, addressing existing drawbacks, and maximizing its potential. 3. Explore the possibility of reaching a priori tailored daily treatment plans. Other objectives: 1. Drive knowledge sharing and shape a research agenda on large-scale complex real-world optimization problems, focusing on algorithms principles and exploring new research directions. 2. Promote knowledge transfer by producing freeware software and making it publicly available under appropriate licensing. 3. Disseminate scientific findings through conferences, publications, and scientific/applied/educational initiatives targeting various stakeholders. 4. Create educational and informative resources to equip users with the necessary skills to effectively leverage radiotherapy treatment robust optimization. These materials will include tutorials and software documentation to enhance users' understanding and utilization of the developed approaches. 5. Contribute to capacity building by enhancing competencies among researchers and clinical professionals, promoting scientific education by welcoming students in the team, fostering gender balance, encouraging collaboration, interdisciplinary exchange, innovation, creativity, and knowledge transfer among researchers. 6. Develop strategies and plans for sustaining the capacity building efforts beyond the duration of the research project, ensuring continued growth and development for the research community. This project showcases scientific creativity and application potential, leveraging interdisciplinary expertise to address healthcare challenges and advance computational automated solutions. It targets Technology Readiness Levels 2 and 3, aiming for scalable, reliable, fast, cost-effective optimization algorithmic solutions with significant social and economic impacts.