Immune therapy has shown unprecedent results in cancer treatment and fostered a strong focus on immune checkpoint inhibitors (ICI) both in research and in the clinic. Initially adopted in metastatic setting, soon ICI will be available in a neoadjuvant setting to treat early stage non-small cell lung cancer (NSCLC) patients. Given the high costs of treatment and associated adverse events, there is an unmet need for predictive and prognostic biomarkers for NSCLC patients undergoing ICI.
Research question: Is it possible to predict immune therapy treatment response in NSCLC patients?
To develop a framework based on the synergy of digital pathology and AI for accurate classification of the tumor immune microenvironment of non-small cell lung cancer to allow personalization of (neo-adjuvant) immune therapy. Multiple student projects are available in the context of this research. Contact the supervisor of this project for more information about availability of projects.
Students with a major in computer science, biomedical engineering, artificial intelligence, physics, or a related area in the final stage of master level studies are invited to apply.
Interest in image analysis and machine learning
Affinity with programming in Python is required.
Project duration: 6 months
Location: Radboud University Nijmegen Medical Centre
For more information please contact Francesco Ciompi