Job description
Advancements in radiotherapy are crucial given the global rise in cancer incidence, which poses significant societal and economic burdens. Effective radiotherapy can vastly improve patient outcomes, reducing mortality and enhancing quality of life. However, traditional treatment planning is complex and can lead to suboptimal results and side effects. Innovation in this field, particularly through robust auto-planning, is necessary to ensure consistently high-quality treatments, minimize toxicities, and increase the efficiency and accessibility of advanced cancer care, ultimately benefiting public health and reducing healthcare costs.
Automated machine learning planning will be explored for proton radiotherapy, specifically Intensity Modulated Proton Therapy (IMPT), Proton Arc Therapy and photon arc therapy and compared to manually optimized plans. The most frequently treated tumor sites in the head and neck (i.e., nasopharynx, oropharynx, hypopharynx, and larynx) will be included as well as tumor sites in the thorax (lung and esophageal cancer). Results will be compared to Quality of Life planning, and methods will be optimized to maximize performance.
We offer two PhD positions in a project with our treatment planning system manufacturer, RaySearch Laboratories.