Mesoscopic FLIm for detection of residual cancer in transoral robotic surgery

Mohamed Hassan; Brent W. Weyers; Julien Bec; Jinyi Qi; Dorina Gui; Arnaud F. Bewley; Marianne Abouyared; Andrew C. Birkeland; Laura Marcu

doi:10.1117/12.3003627

13 March 2024 Mesoscopic FLIm for detection of residual cancer in transoral robotic surgery

Mohamed Hassan, Brent W. Weyers, Julien Bec, Jinyi Qi, Dorina Gui, Arnaud F. Bewley, Marianne Abouyared, Andrew C. Birkeland, Laura Marcu

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Proceedings Volume PC12818, Imaging, Therapeutics, and Advanced Technology in Head and Neck Surgery and Otolaryngology 2024; (2024) https://doi.org/10.1117/12.3003627
Event: SPIE BiOS, 2024, San Francisco, California, United States

Abstract

This study introduces mesoscopic FLIm as a potential solution to address the challenge of residual cancer in Transoral Robotic Surgery. Current methods rely on intraoperative frozen sections analysis (IFSA), which can yield false negatives. FLIm utilizes tissue fluorophores to delineate head and neck cancer in the surgical cavity accurately. A FLIm-based semi-supervised classification model was developed using data from 22 patients, achieving a sensitivity of 0.75 for residual tumors and an overall tissue specificity of 0.78. The proposed approach also outperformed IFSA in detecting positive surgical margins. FLIm shows promise in guiding TORS and improving surgical outcomes.

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Mohamed Hassan, Brent W. Weyers, Julien Bec, Jinyi Qi, Dorina Gui, Arnaud F. Bewley, Marianne Abouyared, Andrew C. Birkeland, and Laura Marcu "Mesoscopic FLIm for detection of residual cancer in transoral robotic surgery", Proc. SPIE PC12818, Imaging, Therapeutics, and Advanced Technology in Head and Neck Surgery and Otolaryngology 2024, (13 March 2024); https://doi.org/10.1117/12.3003627

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KEYWORDS

Cancer

Cancer detection

Data modeling

Robotic surgery

Tumor growth modeling

Tumors

Tissues

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