Central Nervous System (CNS) tumors encompass over 150 different types of tumors affecting the brain and spinal cord and can be categorized as primary or metastatic. Precise and rapid classification of these tumors is crucial to guide surgical decisions and plan personalized treatment strategies.
Although modern imaging techniques can determine whether a CNS tumor is primary or metastatic, surgical resection followed by intraoperative histological analysis is necessary to identify the specific tumor type. Traditional histology, relying on the use of dyes, is time-consuming, lacks chemical specificity, and fails to provide a label-free, rapid, and automated tool for CNS tumor classification.
CHIMERA addresses these limitations, providing CNS tumor research with an imaging platform for intraoperative tumor diagnosis and classification that rapidly extracts spectral, morphological, and biochemical features of tumors in a label-free way. CHIMERA will merge the sensitivity of nonlinear multiphoton techniques to endogenous biomarkers with the chemical specificity of vibrational imaging approaches, using sum-frequency generation, two-photon excited fluorescence, and hyperspectral coherent anti-Stokes Raman scattering. I will adopt a wide-field random illumination microscopy scheme that provides super-resolution in the transverse directions, z-sectioning capabilities, reduced sample damage, and unprecedented imaging speed over a large field of view. Through data processing and deep-learning classification methods, CHIMERA will offer a highly specific morpho-chemical contrast palette to simplify and accelerate the classification of various CNS tumor types.
CHIMERA will advance current nonlinear microscopy technologies, unveil new insights for studying CNS tumors, and serve as a powerful diagnostic tool in biomedical research and clinical settings. Finally, it will provide me with new knowledge in photonics, biochemistry, and data analysis to attain scientific independence.
