My research focuses is personalized radiotherapy through advanced machine and deep learning to predict treatment outcome and side effects based on therapy, oncologic and/or quantified multi-modality imaging information. My experience in medical image processing, complex modelling, and my clinical expertise as a technical physician make me uniquely position to translate technical innovations to clinic-usable applications.
My formal education in Technical Medicine allows me to operate as both a physician as a computer scientist, proving me with knowledge and expertise in both the medical and physics/informatics domain. This skillset grants me ability to deal with the increasing complexity of technical and medical innovations. With my PhD trajectory, I have obtained distinct expertise in radiomics and machine learning approaches to predict of radiation-induced toxicities and tumor outcome with multi-modality imaging in head and neck cancer patient. I have been the core developer of the dedicated image processing, machine learning software. My post-doctoral research at UMCG was a natural shift to deep learning approaches for organ segmentation.
Funded by Dutch Research Council for my post-doctoral fellowship at MD Anderson Cancer Center, I have developed a model-based treatment decision support systems to select high-risk head and neck cancer patients for the Phase I dose-escalation study for head and neck cancer patients that I initiated. Funded by a KWF and NWO grant, I now lead the UMCG effort to use Artificial Intelligence to predict toxicity trajectories, and develop model-based decision-support to guide physicians in finding optimal strategies to reduce these severe toxicities.
Disclosures:
- Employment: none
- Compensation: none
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