Karlijn Groenen started at the Radiology department of the Radboudumc on January 15th, 2018. She submitted her thesis manuscript early December 2017. Click her LinkedIn tag.
T: +31 (0) 24 36 13366 (secretary)
In August 2013 I obtained my master’s degree in Biomedical Sciences, with a major in Clinical Human Movement Sciences (summa cum laude). To broaden my knowledge in (bio)mechanics I did a minor in ‘Biomedical Engineering’ at the Eindhoven University of Technology. In the end of 2012, I participated in the Radboud University Nijmegen Medical Center (RUNMC) Ph.D. competition 2013, which provides the opportunity for students of the RUNMC to perform research as a PhD student on a self-written project. Under supervision of Nico Verdonschot, Esther Tanck, and Dennis Janssen I wrote a research proposal entitled ‘Preventing neurological damage in patients suffering from spinal metastases by using patient-specific finite element models of the metastatically involved spine’. I was awarded with one of the scholarships and in December 2013 I started as a Ph.D. student on this project here at the ORL.
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Approximately one third of all cancer patients develop spinal metastases. Spinal metastases weaken the vertebrae, which results in an increased fracture risk of the affected vertebrae. These fractures will not only cause pain, but can also force bone fragments into the spinal canal, leading to compression of neural structures (Metastatic Spinal Cord Compression or MSCC). Eventually, MSCC might result in severe and often irreversible neurological deficits or even total paraplegia. A proactive clinical approach is therefore needed.
Ideally, clinical guidelines should differentiate between stable metastatic vertebrae, with a low risk of fracture, and unstable vertebrae, with a high risk of fracture and subsequent risk of neurological damage. The former type can be treated with non-invasive low dose radiotherapy, while the latter benefits from more radical treatments, e.g. vertebroplasty or stabilization surgery.
Mechanical vertebral stability is thus an important factor in directing treatment. Currently, clinicians can only judge the severity of the metastatic spinal involvement by evaluating patient- and diagnostic imaging data, and have great difficulties predicting the mechanical stability of the pathological spine. A better method to distinguish stable from unstable vertebra is thus urgently required.
In our project, we aim to improve the prediction of spinal instability and the subsequent risk of neurological damage due to impending deformation in patients suffering from spinal metastases by creating patient-specific finite element models.
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