PortPy: A Benchmark for AI and Optimization in Cancer Radiotherapy Planning

External beam radiotherapy is a cornerstone of cancer treatment, used in more than half of all cancer patients. It delivers high-energy radiation beams to destroy tumors while minimizing radiation dose to surrounding healthy tissues. Treatment planning requires optimizing machine parameters, including the shapes and intensities of radiation beams, based on each patient’s unique anatomy. This gives rise to large-scale, multi-criteria, and often combinatorial optimization problems that must be solved separately for each patient under strict clinical time constraints. Although recent advances in AI/ML show promise for improving many planning and decision-making tasks, progress in this area has been limited by the lack of standardized, publicly available benchmark datasets and baseline algorithms. To address this gap, we introduce PortPy (Planning and Optimization for Radiation Therapy in Python), an open-source benchmark dataset derived from real patient data, together with baseline algorithms and a Python-based framework for data processing, simulation, and standardized evaluation. Several of the provided baseline methods have been clinically validated and deployed at our institution to support automated radiotherapy planning for cancer patients. By providing a common benchmark and open research platform, PortPy aims to accelerate progress in radiotherapy treatment planning and promote collaboration across the AI/ML, mathematical optimization, medical physics, and radiation oncology communities. Toolkit: https://github.com/PortPy-Project/PortPy Dataset: https://huggingface.co/datasets/PortPy-Project/PortPy_Dataset