Intraoperative Margin Assessment in Oral and Oropharyngeal Cancer Using Label-Free Fluorescence Lifetime Imaging and Machine Learning

Mark Marsden, Brent W. Weyers, Julien Bec, Tianchen Sun, Regina F Gandour-Edwards, Andrew Birkeland, Marianne Abouyared, Arnaud F. Bewley, D. Gregory Farwell, Laura Marcu

Research output: Contribution to journalArticlepeer-review

Abstract

Objective: To demonstrate the diagnostic ability of label-free, point-scanning, fiber-based Fluorescence Lifetime Imaging (FLIm) as a means of intraoperative guidance during oral and oropharyngeal cancer removal surgery. Methods: FLIm point-measurements acquired from 53 patients (n = 67893 pre-resection in vivo, n = 89695 post-resection ex vivo) undergoing oral or oropharyngeal cancer removal surgery were used for analysis. Discrimination of healthy tissue and cancer was investigated using various FLIm-derived parameter sets and classifiers (Support Vector Machine, Random Forests, CNN). Classifier output for the acquired set of point-measurements was visualized through an interpolation-based approach to generate a probabilistic heatmap of cancer within the surgical field. Classifier output for dysplasia at the resection margins was also investigated. Results: Statistically significant change (P $< $ 0.01) between healthy and cancer was observed in vivo for the acquired FLIm signal parameters (e.g., average lifetime) linked with metabolic activity. Superior classification was achieved at the tissue region level using the Random Forests method (ROC-AUC: 0.88). Classifier output for dysplasia (% probability of cancer) was observed to lie between that of cancer and healthy tissue, highlighting FLIm's ability to distinguish various conditions. Conclusion: The developed approach demonstrates the potential of FLIm for fast, reliable intraoperative margin assessment without the need for contrast agents. Significance: Fiber-based FLIm has the potential to be used as a diagnostic tool during cancer resection surgery, including Transoral Robotic Surgery (TORS), helping ensure complete resections and improve the survival rate of oral and oropharyngeal cancer patients.

Original languageEnglish (US)
Article number9144381
Pages (from-to)857-868
Number of pages12
JournalIEEE Transactions on Biomedical Engineering
Volume68
Issue number3
DOIs
StatePublished - Mar 2021

Keywords

  • Machine learning
  • medical robotics
  • surgical guidance/navigation

ASJC Scopus subject areas

  • Biomedical Engineering

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