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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.