Predicting Carbon Spectrum in Heteronuclear Single Quantum Coherence Spectroscopy for Online Feedback During Surgery.
Fiche publication
Date publication
juin 2019
Journal
IEEE/ACM transactions on computational biology and bioinformatics
Auteurs
Membres identifiés du Cancéropôle Est :
Pr NAMER Izzie-Jacques
Tous les auteurs :
Karakaslar EO, Coskun B, Outilaft H, Namer IJ, Cicek E
Lien Pubmed
Résumé
1H High-Resolution Magic Angle Spinning (HRMAS) Nuclear Magnetic Resonance (NMR) is a reliable technology used for detecting metabolites in solid tissues. Fast response time enables guiding surgeons in real time, for detecting tumor cells that are left over in the excision cavity. However, overlap of spectral resonances in 1D signal often render distinguishing metabolites impossible. In that case, Heteronuclear Single Quantum Coherence Spectroscopy (HSQC)-NMR is applied to distinguish metabolites in 2D spectra. Unfortunately, this requires much longer time and prohibits real time analysis. In this study, we show that using multiple multivariate regression and statistical total correlation spectroscopy, we can learn the relation between the 1H and 13C dimensions. Learning is possible with small sample sizes and without the need for performing the HSQC analysis, we can predict the 13C dimension by just performing HRMAS-NMR experiment. We show on a rat model of central nervous system that our methods achieve 0.971 and 0.957 mean R2 values, respectively. Our tests on human brain tumor samples show that we can predict 104 peaks of 39 metabolites with 97.1% accuracy. Finally, we show that we can predict the presence of a drug resistant tumor biomarker (creatine) despite obstructed signal in 1H dimension.
Référence
IEEE/ACM Trans Comput Biol Bioinform. 2019 Jun 4;: