Challenges in medicine: frontrunners about their dilemmas and innovations

Abstract

Treatment for cancer patients has become more personalized, due to developments in targeted treatments. To make the treatment for patients with brain metastases more personalized is more challenging, since surgery is not always feasible. More important, pathological features (e.g. hormone receptors, BRAF-mutations, and PD-L1 expression) between the primary tumour and its brain metastases can be different. For example 14% of brain metastases of lung cancer lost their programmed cell death-ligand (PD-L1)-expression below the therapeutic threshold. As a result, systemic treatment, based on the pathology of the primary tumour, might not be effective on brain metastases, while the patient can experience side-effects. It would be most helpful to predict, which patients will most likely benefit from systemic treatment and which patients probably won't. Can we predict this on brain MRIs with artificial intelligence?

Biography

I studied medicine in Utrecht (2011) and became a neurologist in 2020 (Erasmus MC, Rotterdam). After obtaining my PhD (2021) about primary central nervous system lymphoma, a rare primary brain tumour, I continued my training in neuro-oncology in Princess Margaret Cancer Centre in Toronto, Canada by doing a one year clinical fellowship. In 2021, I started to work as a neurologist/ neuro-oncologist at Medisch Spectrum Twente in Enschede. In addition to my clinical work I am doing research on prognostication and personalized medicine in patients with brain metastases.

Abstract

Gastrointestinal stromal tumours (GISTs) are rare malignant mesenchymal tumours affecting the gastrointestinal tract. Besides surgery, chemotherapy and radiotherapy are the most frequently used therapies in oncology. In GISTs however, both these therapies are ineffective. Treatment with Tyrosine Kinase Inhibitors (TKIs), is typically used to restrict tumour growth in unresectable and metastatic GISTs. Unfortunately, treatment with TKIs is not always effective, expensive and associated with serious side-effects. Treatment personalization through early identification of nonresponding disease, would benefit the individual patient and eliminate unnecessary costs for society. Our research aims to develop computer algorithms that predict and monitor TKI treatment response, by combining clinical and quantitative imaging features. These algorithms are expected to support clinicians in personalized and evidence-based decision-making.

Abstract

The medical 3Dlab in MST is founded to implement current developments in 3D technologie. The core is 3D medical image processing with 3D printing as an important tool. We can aid with clinical (implementation) problems and often have challenges for which we're searching collaborations.

Biography

Technical Physician with a doctorate in the clinical implementation of 3D medical imaging technologies (2021). Currently working as a technical Physician in the medical 3Dlab of Medical Spectrum Twente and the innovationlab of Deventer Hospital. Guest lecturer at Robotics and Mechatronics reasearchgroup at University of Twente.