Endomicroscopic AI-driven morphochemical imaging and fs-laser ablation for selective tumor identification and selective tissue removal

The rising incidence of head and neck cancer represents a serious global health challenge, requiring more accurate diagnosis and innovative surgical approaches. Multimodal nonlinear optical microscopy, combining coherent anti-Stokes Raman scattering (CARS), two-photon excited fluorescence (TPEF), and second-harmonic generation (SHG) with deep learning-based analysis routines, offers label-free assessment of the tissue's morphochemical composition and allows early-stage and automatic detection of disease. For clinical intraoperative application, compact devices are required. In this preclinical study, a cohort of 15 patients was examined with a newly developed rigid CARS/TPEF/SHG endomicroscope. To detect head and neck tumor from the multimodal data, deep learning-based semantic segmentation models were used. This preclinical study yields in a diagnostic sensitivity of 88% and a specificity of 96%. To combine diagnostics with therapy, machine learning-inspired image-guided selective tissue removal was used by integrating femtosecond laser ablation into the endomicroscope. This enables a powerful approach of intraoperative "seek and treat," paving the way to advanced surgical treatment.

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Publication Details

Journal
Science Advances
Published
2024-12-11
DOI
https://doi.org/10.1126/sciadv.ado9721
Citations
20
Primary Topic
Spectroscopy Techniques in Biomedical and Chemical Research
Type
article
Field-Weighted Citation Impact
13.25

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article

Endomicroscopic AI-driven morphochemical imaging and fs-laser ablation for selective tumor identification and selective tissue removal

Jürgen Popp, Matteo Calvarese, Karl Reichwald, Thomas Wilhelm Bocklitz et al.
20 citations
Science Advances
Spectroscopy Techniques in Biomedical and Chemical Research
13.25
article

Endomicroscopic AI-driven morphochemical imaging and fs-laser ablation for selective tumor identification and selective tissue removal

Jürgen Popp, Matteo Calvarese, Karl Reichwald, Thomas Wilhelm Bocklitz, Elena Corbetta, David Pertzborn, Chenting Lai, Franziska Hoffmann, Michael P. Schmitt, Anna Mühlig, Bernhard Messerschmidt, Orlando Guntinas‐Lichius, Jhonatan Contreras, Hyeonsoo Bae, Tobias Meyer‐Zedler
article en
20 citations

Abstract

The rising incidence of head and neck cancer represents a serious global health challenge, requiring more accurate diagnosis and innovative surgical approaches. Multimodal nonlinear optical microscopy, combining coherent anti-Stokes Raman scattering (CARS), two-photon excited fluorescence (TPEF), and second-harmonic generation (SHG) with deep learning-based analysis routines, offers label-free assessment of the tissue's morphochemical composition and allows early-stage and automatic detection of disease. For clinical intraoperative application, compact devices are required. In this preclinical study, a cohort of 15 patients was examined with a newly developed rigid CARS/TPEF/SHG endomicroscope. To detect head and neck tumor from the multimodal data, deep learning-based semantic segmentation models were used. This preclinical study yields in a diagnostic sensitivity of 88% and a specificity of 96%. To combine diagnostics with therapy, machine learning-inspired image-guided selective tissue removal was used by integrating femtosecond laser ablation into the endomicroscope. This enables a powerful approach of intraoperative "seek and treat," paving the way to advanced surgical treatment.

Science AdvancesVol. 10(50)
Leibniz Institute of Photonic Technology (DE), Jena University Hospital (DE), Grintech (Germany) (DE), University of Bayreuth (DE), Friedrich Schiller University Jena (DE)
Friedrich-Schiller-Universität Jena, European Commission, Deutsche Forschungsgemeinschaft, Bundesministerium für Bildung und Forschung, Leibniz-Gemeinschaft, European Regional Development Fund
Openalex Percentile: Top 1%
Spectroscopy Techniques in Biomedical and Chemical Research
13.25
DOIOpen Access PDFOpenAlex
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