Targeted Metabolomics Analyses for Brain Tumor Margin Assessment During Surgery.

Date publication

mai 2022

Journal

Bioinformatics (Oxford, England)

Auteurs

Membres identifiés du Cancéropôle Est :
Pr NAMER Izzie-Jacques , Pr PROUST François , Dr BUND Caroline

Tous les auteurs :
Cakmakci D, Kaynar G, Bund C, Piotto M, Proust F, Namer IJ, Cicek AE

Lien Pubmed

https://www.ncbi.nlm.nih.gov/pubmed/35512389

Résumé

Identification and removal of micro-scale residual tumor tissue during brain tumor surgery are key for survival in glioma patients. For this goal, High Resolution Magic Angle Spinning Nuclear Magnetic Resonance (HRMAS NMR) spectroscopy based assessment of tumor margins during surgery has been an effective method. However, the time required for metabolite quantification and the need for human experts such as a pathologist to be present during surgery are major bottlenecks of this technique. While machine learning techniques that analyze the NMR spectrum in an untargeted manner (i.e., using the full raw signal) have been shown to effectively automate this feedback mechanism, high dimensional and noisy structure of the NMR signal limits the attained performance.

Référence

Bioinformatics. 2022 May 5;: