In:
Neuro-Oncology, Oxford University Press (OUP), Vol. 23, No. Supplement_6 ( 2021-11-12), p. vi125-vi125
Abstract:
Raman Spectra have been shown to be sufficiently characteristic to their samples of origin that they can be used in a wide range of applications including distinction of intracranial tumors. While not replacing pathological analysis, the advantage of non-destructive sample analysis and extremely fast feedback make this technique an interesting tool for surgical use. METHODS We sampled intractanial tumors from more than 300 patients at the Centre Hospitalier Luxembourg over a period of three years and compared the spectra of different tumor entities, different tumor subregions and healthy surrounding tissue. We created machine-learning based classifiers that include tissue identification as well as diagnostics. RESULTS To this end, we solved several classes in the intracranial tumor classification, and developed classifiers to distinguish primary central nervous system lymphoma from glioblastoma, which is an important differential diagnosis, as well as meningioma from the surrounding healthy dura mater for identification of tumor tissue. Within glioblastoma, we resolve necrotic, vital tumor tissue and peritumoral infiltration zone.We are currently developing a multi-class classifier incorporating all tissue types measured. CONCLUSIONS Raman Spectroscopy has the potential to aid the surgeon in the surgery theater by providing a quick assessment of the tissue analyzed with regards to both tumor identity and tumor margin identification. Once a reliable classifier based on sufficient patient samples is developed, this may even be integrated into a surgical microscope or a neuronavigation system.
Type of Medium:
Online Resource
ISSN:
1522-8517
,
1523-5866
DOI:
10.1093/neuonc/noab196.495
Language:
English
Publisher:
Oxford University Press (OUP)
Publication Date:
2021
detail.hit.zdb_id:
2094060-9
