Direct detection and identification of neuro-oncology markers in brain tumours
From 01-10-2019 to 30-09-2023
Description
The outcome for brain cancer patients is highly dependent on the maximal removal of tumour tissue, but the quality of life post-surgery is linked to the precision by which non-tumoural, essential parts of the brain can be preserved. Today, the decision between removing and partly leaving tissue during surgery is based on a combination of MRI-neuro-navigation guidance, the use Gliolan®, a fluorescent dye that highlights tumour cells, intra-operative ultrasound, functional mapping and finally, the experience of the surgeon, integrating all these tools. Even though these are of major help for the surgeon to recognize tumour tissue during surgery, the delineation between normal and tumoural tissue is often still cumbersome. Also, histopathological confirmation of tumour type becomes available several days after completing surgery. This means very few samples are directly classified during surgery. However, direct tumour identification could significantly guide and affect surgical decisions. When a surgeon can make a solid, real-time distinction between healthy and tumour tissue during surgery, the outcome benefit for the patient will be significant. Moreover, direct identification of the tumour subtype during surgery might steer surgery outcome. This project will use multimodal imaging mass spectrometry to collect chemical information of brain tumour types and healthy brain tissue. On the same tissue sample, multiple analysis will result in the chemical profile. Moreover, tumour heterogeneity can be evaluated thanks to the spatial information. This information will be compared with extraction and pathological results in order to collect the full tissue profile and identity. Ultimately, all collected information will be used to build a database and pattern recognition system to directly identify tissue and tumour type during electrosurgical dissection within a few seconds possible. The final developed method will be offline validated in the final stage of this project.
Team
- Bart De Moor, Co-promoter
- Steven De Vleeschouwer, Coordinator (External)
- Melanie Nijs, Team member
Financing
Funding: FWO - Research Foundation - Flanders
Program/Grant Type: FWO TBM - FWO Projects Applied Biomedical Research with a Primary Social finality
Events
2/09/2024:
PhD defense - Martijn Oldenhof
Machine Learning for Advanced Chemical Analysis and Structure Recognition in Drug Discovery
3/09/2024:
Meet the Jury Igor Tetko on Advanced Machine Learning in Drug Discovery
12/09/2024:
Multimodal analysis of cell-free DNA for sensitive cancer detection in low-coverage and low-sample settings
Seminar by Antoine Passemiers
News
STADIUS Alumni Herman Verrelst – new CEO of Biocartis
08 June 2017
Herman Verrelst, the founder of KU Leuven spin-off Cartagenia, who has been working in Silicon Valley, US for the last few years will be returning to Belgium to follow the steps of Rudi Pauwels as CEO of the Belgian diagnostic company, Biocartis.
Supporting healthcare policymaking via machine learning – batteries included!
29 May 2017
STADIUS takes the lead in the data analytics efforts in an ambitious European Project MIDAS.
Marc Claesen gives an interview about his PhD for the magazine of the Faculty of Engineering Sciences "Geniaal"
10 February 2017
Did you know that in Belgium approximately one third of type 2 diabetes patients are unaware of their condition?
Joos Vandewalle is nieuwe voorzitter KVAB
09 October 2016
Op 5 oktober 2016 heeft de Algemene Vergadering van de Academie KVAB Joos Vandewalle verkozen tot voorzitter van de KVAB.