| Project Detail |
Brain tumour biomarker analysis for efficient classification
Precise cancer classification is paramount for effective therapy. With respect to brain tumours, modern imaging can differentiate primary from metastatic tumors, but histological analysis is necessary to determine the type of cancer. Funded by the Marie Sklodowska-Curie Actions programme, the CHIMERA project aims to develop an imaging platform for rapid, label-free intraoperative tumour diagnosis. The platform will combine multiphoton and coherent Raman techniques to extract tumour features and biomarkers, assisting clinicians in tumour classification. This innovation will improve diagnostics in biomedical research and enhance the treatment of the different types of tumours affecting the brain and spinal cord.
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. |
