Endomicroscopic AI-driven morphochemical imaging and fs-laser ablation for selective tumor identification and selective tissue removal
Matteo Calvarese, Elena Corbetta, Jhonatan Contreras, Hyeonsoo Bae, Chenting Lai, Karl Reichwald, Tobias Meyer‐Zedler, David Pertzborn, Anna Mühlig, Franziska Hoffmann, Bernhard Messerschmidt, Orlando Guntinas‐Lichius, Michael Schmitt, Thomas Bocklitz, Jürgen Popp
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.