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Glioblastomas (a highly malignant brain tumor) are notorious for their tumor infiltration, where tumor extends into adjacent normal-appearing brain tissue. As tumor infiltration is not visible on conventional MRI-scans, a safety margin of 1.5-cm is always added to the visible tumor when the target area for radiotherapy is defined. In this research, the aim is to eliminate this one-size-fits-all approach and assess the potential of artificial intelligence and state-of-the-art MRI techniques to more accurately define the target area for each individual patient. By only targeting what needs to be targeted, the development of side-effects caused by radiotherapy could be reduced.

Very high electron energy radiotherapy (VHEE) is an emerging treatment modality for cancer patients that can potentially result in a paradigm shift in radiotherapy, as it can result in substantial reductions in treatment-induced side-effects and options to enhance cure rates. In this project we will develop a flexible, versatile and computationally efficient VHEE optimisation platform to investigate and optimise the clinical benefit of VHEE for various cancer indications, and develop guidelines for an optimal treatment machine design.

De vraag naar aardappelen stijgt wereldwijd. Om ook in de toekomst aan de stijgende vraag naar voedsel en aardappelen te kunnen voldoen, is onderzoek en ontwikkeling nodig. Na de ontrafeling van het genoom van de aardappel, onder andere door Indiase onderzoekers, ligt nu de weg open voor verdere verbetering van de aardappel.