Fiber-probe optical spectroscopy discriminates normal brain from focal cortical dysplasia in pediatric subjects
Authors: Anand S., Cicchi R., Giordano F., Conti V., Buccoliero A.M., Guerrini R., Pavone F.S.
Autors Affiliation: National Institute of Optics-National Research Council (INO-CNR), Via Nello Carrara, 1, Sesto Fiorentino, 50019, Italy; European Laboratory for Non-Linear Spectroscopy (LENS), University of Florence, Via Nello Carrara, 1, Sesto Fiorentino, 50019, Italy; Division of Neurosurgery, Department of Neuroscience i, Anna Meyer Pediatric Hospital, Viale Gaetano Pieraccini 24, Florence, 50141, Italy; Pediatric Neurology, Neurogenetics and Neurobiology Unit and Laboratories, Neuroscience Department, A Meyer Children’s Hospital, University of Florence Florence, Viale Pieraccini 24, Florence, 50139, Italy; Division of Pathology, Department of Critical Care Medicine and Surgery, University of Florence, Viale Giovanni Battista Morgagni 85, Florence, 50134, Italy; Department of Physics, University of Florence, Via Giovanni Sansone 1, Sesto Fiorentino, 50019, Italy
Abstract: Focal cortical dysplasia (FCD) is an abnormality in the cerebral cortex that is caused by malformations during cortical development. Currently, magnetic resonance imaging (MRI) and electro-corticography (ECoG) are used for detecting FCD. On the downside, MRI is very much insensitive to small malformations in the brain, while ECoG is an invasive and time consuming procedure. Recently, optical techniques were widely exploited as a minimally invasive and quantitative approaches for disease diagnosis. These techniques include fluorescence and Raman spectroscopy. The aim of this investigation is to study the diagnostic performances of optical spectroscopy incorporating fluorescence (at 378 nm and 445 nm excitation wavelengths) and Raman spectroscopy (at 785 nm excitation) for the discrimination of FCD from normal brain in pediatric subjects. The study included 10 normal and 17 FCD tissue sites from 3 normal and 7 FCD samples. The emission spectra of FCD at 378 nm excitation wavelength presented a blue-shifted peak with respect to normal tissue. Prominent spectral differences between normal and FCD tissue were observed at 1298 cm-1, 1302 cm-1, 1445 cm-1 and 1660 cm-1 using Raman spectroscopy. Tissue classification models were developed using a multivariate statistical method, principal component analysis. This study demonstrates that a combined spectroscopic approach can provide a better diagnostic capability for classifying normal and FCD tissues. Further, the implementation of the technology within a fiber probe could open the way for in vivo diagnostics and intra-operative surgical guidance.
More Information: The research leading to these results has received funding from Fondazione Pisa in the framework of the project “Diagnostic technology for the post-operative monitoring of pediatric brain tumors”, from the Italian Ministry for Education, University and Research in the framework of the Flagship Project NANOMAX, from the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement number 284464, from the Italian Ministry of Health (GR-2011-02349626), from Tuscany Region and EU FP7 BiophotonicsPlus projects “LighTPatcH” (Led Technology in Photo Haemostasis) and “LITE” (Laser Imaging of The Eye), and from Ente Cassa di Risparmio di Firenze, from the European Union Seventh Framework Programme FP7/2007-2013 under the project ‘DESIRE’ (grant agreement 602531). The authors would like to thank the operating room staff from Anna Meyer Children’s Hospital, Florence, Italy for their assistance in tissue sample collection.KeyWords: Brain; Diagnosis; Electrophysiology; Emission spectroscopy; Fluorescence; Magnetic resonance imaging; Multivariant analysis; Pediatrics; Probes; Raman spectroscopy; Spectrum analysis; Statistical methods; Tissue, Diagnostic capabilities; Diagnostic performance; Excitation wavelength; Focal cortical dysplasias; Multivariate statistical method; Optical spectroscopy; Quantitative approach; Time-consuming procedure, Principal component analysis