Marius Staring

LUMC

In short, Marius Staring's research entails the following: 

Machine Learning to support Clinical Decision Making in Vestibular Schwannoma (MLSCHWAN)

Vestibular schwannomas (VS) are rare intracranial tumors that are (typically) benign, but may cause invalidating symptoms such as hearing loss, balance disturbance or even intracranial hypertension and brainstem compression in advanced cases. Typically, patients are monitored closely with periodic MR imaging, and only in case of tumor progression, treatment is opted for. Computational tools based on Machine Learning may allow prediction of tumor progression in an early stage, when less invasive treatment strategies are still an option. In the MLSCHWAN project our team of engineers, radiologists and ear-nose-throat surgeons, will develop tumor growth prediction tools, implement these in the clinic, and evaluate their added value.

MRI scan of a Vestibular Schwannoma: is it stable or does it grow?

Short summary of progress/results 2024

In this first period we have created and curated a patient inclusion list, and downloaded and pseudoanonimyzed longitudinal MR imaging data of over 2000 patients. This will be further cleaned and uploaded to an XNAT facility. We have started development of a machine learning approach based on MR imaging that predicts a binary classification of growth in the coming y