CBCT-guided Adaptive Radiotherapy and Biological Monitoring in Lung Cancer

Project Overview

This project focuses on improving radiotherapy for non-small cell lung cancer by overcoming limitations of static treatment plans. It seeks to develop online adaptive radiotherapy workflows that adapt to daily anatomical and biological changes using cone-beam CT (CBCT) imaging enhanced with artificial intelligence. The goal is highly individualized treatments that enhance tumor control and patient safety.

What You Will Do

You will develop and clinically validate AI models to enhance CBCT image quality enabling reliable online treatment adaptations. The project includes designing in-silico studies, analyzing data from clinical trials testing a novel 'Primer Shot' radiation protocol, and training liquid biopsy-based hypoxia signatures to non-invasively monitor tumor oxygenation and response over time. Collaboration with medical physicists, oncologists, and AI researchers at NKI is a key part of your role.

Expected Outcomes

The research aims to establish a direct-to-LINAC workflow bypassing traditional CT planning, validate CBCT-based online adaptive radiotherapy clinically, provide new biological markers for treatment monitoring, and improve understanding of tumor reoxygenation schedules. The results could significantly improve accuracy, safety, and personalization of lung cancer radiotherapy.

Why This Matters

Current radiotherapy approaches do not account for patient anatomy changes or tumor biology during treatment, contributing to suboptimal outcomes. This project addresses these challenges by integrating state-of-the-art AI and biological monitoring, potentially setting new standards for adaptive therapies that better control tumors and minimize side effects, ultimately improving patient survival and quality of life globally.