Michael Schmitt1, Tobias Meyer-Zedler2, Orlando Guntinas-Lichius3, Juergen Popp4,5. 1. Institut für Physikalische Chemie und Abbe Center of Photonics, Friedrich-Schiller-Universität Jena, Jena, Deutschland. 2. Leibniz Institut für Photonische Technologien, Mitglied Leibniz Gesundheitstechnologien, Albert-Einstein-Str. 9, 07745, Jena, Deutschland. 3. Klinik und Poliklinik für Hals-. Nasen- und Ohrenheilkunde, Universitätsklinikum Jena, Jena, Deutschland. 4. Institut für Physikalische Chemie und Abbe Center of Photonics, Friedrich-Schiller-Universität Jena, Jena, Deutschland. juergen.popp@ipht-jena.de. 5. Leibniz Institut für Photonische Technologien, Mitglied Leibniz Gesundheitstechnologien, Albert-Einstein-Str. 9, 07745, Jena, Deutschland. juergen.popp@ipht-jena.de.
Abstract
BACKGROUND: The increasing number of cancer cases requires new imaging approaches for intraoperative tumor characterization. OBJECTIVE: Utilization of new optical/photonic methods in combination with artificial intelligence (AI) approaches to address urgent challenges in clinical pathology in terms of intraoperative computational spectral histopathology. METHODS: Multimodal nonlinear imaging by combining the spectroscopic methods coherent anti-Stokes Raman scattering (CARS), two-photon excited autofluorescence (TPEF), fluorescence lifetime imaging microscopy (FLIM), and second harmonic generation (SHG). RESULTS: By using multimodal spectroscopic imaging, tissue morphochemistry, i.e., its morphology and molecular structure can be visualized in a label-free manner. The multimodal images can be automatically analyzed using AI-based image analysis approaches. For clinical application in terms of frozen section diagnostics or in vivo use, the presented multimodal imaging approach can be translated into a compact microscope or endoscopic probe concepts. CONCLUSIONS: The synergistic combination of spectroscopic imaging modalities in combination with automated data analysis has great potential for fast and precise tumor diagnostics e.g., in terms of precise surgical guidance in laser or robotic surgery. Overall, intraoperative multimodal spectroscopic imaging may represent an innovative advancement for tumor diagnostics in the future, directly leading to improved patient care and significant cost savings.
BACKGROUND: The increasing number of cancer cases requires new imaging approaches for intraoperative tumor characterization. OBJECTIVE: Utilization of new optical/photonic methods in combination with artificial intelligence (AI) approaches to address urgent challenges in clinical pathology in terms of intraoperative computational spectral histopathology. METHODS: Multimodal nonlinear imaging by combining the spectroscopic methods coherent anti-Stokes Raman scattering (CARS), two-photon excited autofluorescence (TPEF), fluorescence lifetime imaging microscopy (FLIM), and second harmonic generation (SHG). RESULTS: By using multimodal spectroscopic imaging, tissue morphochemistry, i.e., its morphology and molecular structure can be visualized in a label-free manner. The multimodal images can be automatically analyzed using AI-based image analysis approaches. For clinical application in terms of frozen section diagnostics or in vivo use, the presented multimodal imaging approach can be translated into a compact microscope or endoscopic probe concepts. CONCLUSIONS: The synergistic combination of spectroscopic imaging modalities in combination with automated data analysis has great potential for fast and precise tumor diagnostics e.g., in terms of precise surgical guidance in laser or robotic surgery. Overall, intraoperative multimodal spectroscopic imaging may represent an innovative advancement for tumor diagnostics in the future, directly leading to improved patient care and significant cost savings.
Authors: Erik Rodner; Thomas Bocklitz; Ferdinand von Eggeling; Günther Ernst; Olga Chernavskaia; Jürgen Popp; Joachim Denzler; Orlando Guntinas-Lichius Journal: Head Neck Date: 2018-12-12 Impact factor: 3.147