We present a study investigating fluorescence lifetime signatures of normal tissues adjacent to tumors (NATs) in head and neck squamous cell carcinoma (HNSCC) using fluorescence lifetime imaging (FLIm). Label-free FLIm offers insight into the metabolic activity and extracellular matrix composition. Understanding the metabolic activity, tissue heterogeneity and tumor-associated alterations in these transition areas can enhance the accuracy of margin delineation. Initial results show that the fluorescence lifetime is gradually increasing from shorter to longer lifetimes with increasing distance from the cancer and with varying magnitudes of change being observed in the individual emission bands.
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.
Herein, we present an anatomy-specific classification model using FLIm to differentiate between benign tissue, dysplasia, and cancer within the oral cavity and oropharynx. A total of 54 features, comprising both time-resolved and spectral intensity features, were used to train and test the classification model. This anatomy-specific classifier improves on our previous classification approach, now yielding an overall ROC-AUC of 0.94 during binary benign vs. cancer classification, and 0.92 while discriminating between healthy, cancer, and dysplasia. The proposed classification model demonstrates that FLIm has the potential to be used as an adjunctive diagnostic tool to facilitate head and neck cancer surgical guidance.
The primary standard of care for Head and Neck (H&N) cancer patients is the complete surgical removal of cancer. Tissue classifiers based of autofluorescence lifetime imaging (FLIm) parameters have shown potential to differentiate healthy from cancer tissue in H&N patients and thus enhance the accuracy of this procedure. Here we report how collective autofluorescence trends (100-patient cohort, oral/oropharyngeal cancer) driving healthy vs. tumor contrast depend on anatomical location, patient medical history (e.g. tobacco use) and surgical context (in vivo vs. ex vivo). Accounting for such biological variables may further improve the accuracy of FLIm-guided H&N cancer surgery.
Accurate cancer margin assessment prior to surgical resection is a key factor influencing the long-term survival of oral and oropharyngeal cancer patients. This leads to the need for additional guidance tools for real-time delineation of cancer margins. In this work, fiber-based fluorescence lifetime Imaging (FLIm) was combined with machine learning to perform intraoperative tumor identification. The developed classifier achieved a measurement-level ROC-AUC of 0.89±0.03 on an N=62 patient dataset. A transparent overlay of classifier output was augmented onto the surgical field and updated through tissue motion correction, ensuring co-registration between tissue and spectroscopic data/classifier output was maintained during imaging..
Oral cavity and oropharyngeal cancers are leading pathologies, representing 3% of all new cancer cases in the United States. Adequate intraoperative marginal clearance of these malignancies is essential for long-term survival; however, presently available techniques limit precise instantaneous tumor margin characterization. Herein, we report the clinical validation of a fiber-based fluorescence lifetime imaging device for real-time intraoperative tumor delineation. Results from 72 human patients are reported (autofluorescence trends, ROC-AUC), including diverse cancer histologies, anatomic sites (e.g. tongue, tonsil, etc.), and patient medical histories. Emphasis is placed on results governing the detection of unknown primary tumors from 4 patients, as well as data from 5 patients presenting with residual carcinoma.
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