Myrte Wennen

Erasmus MC

In short, Myrte Wennen's fellowship entails the following:

The integration of deep learning for quantitative MRI in clinical and research workflows to personalize treatment of cervical and head and neck cancer

Cancer patients can greatly benefit from precision medicine, where quantitative magnetic resonance imaging (qMRI) biomarkers are used to select optimal personalized treatment. We can for example measure perfusion and diffusion in tumours and surrounding tissues. With these measurements, we aim to predict the response of patients to treatment and monitor disease progression. Unfortunately, the application of qMRI faces challenges in accuracy, precision and reproducibility. Consequently, despite many promising results, qMRI is not integrated in clinical care. Our research has however shown that deep learning can drastically improve the accuracy and consistency of qMRI techniques.

At this moment, these deep learning techniques are not utilized efficiently, which greatly hinders implementation in clinical and research workflows. With this Fellowship, I aim to enhance precision medicine for cancer patients by integrating deep learning techniques for qMRI directly on MR scanners and into clinical and research workflows. I will work in an interdisciplinary team to streamline adoption in clinical and research practice, thereby accelerating research and improvingclinical implementation. We will apply the techniques in two clinical trials investigating treatment response in patients with cervical and head and neck cancer. Additionally, I will translate my skills to another center in Sydney, Australia, where I will focus on the application of deep learning for qMRI in a radiotherapy setting. The Fellowship will allow me to use my skills to drive forward the integration of deep learning in oncological imaging, ultimately improving patient outcomes.