Anatomy-Specific Classification Model Using Label-Free FLIm to Aid Intraoperative Surgical Guidance of Head and Neck Cancer.

Intraoperative identification of head and neck cancer tissue is essential to achieve complete tumor resection and mitigate tumor recurrence. Mesoscopic fluorescence lifetime imaging (FLIm) of intrinsic tissue fluorophores emission has demonstrated the potential to demarcate the extent of the tumor in patients undergoing surgical procedures of the oral cavity and the oropharynx. Here, we report FLIm-based classification methods using standard machine learning models that account for the diverse anatomical and biochemical composition across the head and neck anatomy to improve tumor region identification. Three anatomy-specific binary classification models were developed (i.e., "base of tongue," "palatine tonsil," and "oral tongue"). FLIm data from patients (N = 85) undergoing upper aerodigestive oncologic surgery were used to train and validate the classification models using a leave-one-patient-out cross-validation method. These models were evaluated for two classification tasks: (1) to discriminate between healthy and cancer tissue, and (2) to apply the binary classification model trained on healthy and cancer to discriminate dysplasia through transfer learning. This approach achieved superior classification performance compared to models that are anatomy-agnostic; specifically, a ROC-AUC of 0.94 was for the first task and 0.92 for the second. Furthermore, the model demonstrated detection of dysplasia, highlighting the generalization of the FLIm-based classifier. Current findings demonstrate that a classifier that accounts for tumor location can improve the ability to accurately identify surgical margins and underscore FLIm's potential as a tool for surgical guidance in head and neck cancer patients, including those subjects of robotic surgery.

Intraoperative delineation of p16+ oropharyngeal carcinoma of unknown primary origin with fluorescence lifetime imaging: Preliminary report.

BACKGROUND: This study evaluated whether fluorescence lifetime imaging (FLIm), coupled with standard diagnostic workups, could enhance primary lesion detection in patients with p16+ head and neck squamous cell carcinoma of the unknown primary (HNSCCUP). METHODS: FLIm was integrated into transoral robotic surgery to acquire optical data on six HNSCCUP patients' oropharyngeal tissues. An additional 55-patient FLIm dataset, comprising conventional primary tumors, trained a machine learning classifier; the output predicted the presence and location of HNSCCUP for the six patients. Validation was performed using histopathology. RESULTS: Among the six HNSCCUP patients, p16+ occult primary was surgically identified in three patients, whereas three patients ultimately had no identifiable primary site in the oropharynx. FLIm correctly detected HNSCCUP in all three patients (ROC-AUC: 0.90 ± 0.06), and correctly predicted benign oropharyngeal tissue for the remaining three patients. The mean sensitivity was 95% ± 3.5%, and specificity 89% ± 12.7%. CONCLUSIONS: FLIm may be a useful diagnostic adjunct for detecting HNSCCUP.

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

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.

FLImBrush: dynamic visualization of intraoperative free-hand fiber-based fluorescence lifetime imaging.

A free-hand scanning approach to medical imaging allows for flexible, lightweight probes to image intricate anatomies for modalities such as fluorescence lifetime imaging (FLIm), optical coherence tomography (OCT) and ultrasound. While very promising, this approach faces several key challenges including tissue motion during imaging, varying lighting conditions in the surgical field, and sparse sampling of the tissue surface. These challenges limit the coregistration accuracy and interpretability of the acquired imaging data. Here we report FLImBrush as a robust method for the localization and visualization of intraoperative free-hand fiber optic fluorescence lifetime imaging (FLIm). FLImBrush builds upon an existing method while employing deep learning-based image segmentation, block-matching based motion correction, and interpolation-based visualization to address the aforementioned challenges. Current results demonstrate that FLImBrush can provide accurate localization of FLIm point-measurements while producing interpretable and complete visualizations of FLIm data acquired from a tissue surface. Each of the main processing steps was shown to be capable of real-time processing (> 30 frames per second), highlighting the feasibility of FLImBrush for intraoperative imaging and surgical guidance. Current findings show the feasibility of integrating FLImBrush into a range of surgical applications including cancer margins assessment during head and neck surgery.

Fluorescence lifetime imaging for intraoperative cancer delineation in transoral robotic surgery.

This study evaluates the potential for fluorescence lifetime imaging (FLIm) to enhance intraoperative decisionmaking during robotic-assisted surgery of oropharyngeal cancer. Using a custom built FLIm instrument integrated with the da Vinci robotic surgical platform, we first demonstrate that cancer in epithelial tissue diagnosed by histopathology can be differentiated from surrounding healthy epithelial tissue imaged in vivo prior to cancer resection and ex vivo on the excised specimen. Second, we study the fluorescence properties of tissue imaged in vivo at surgical resection margins (tumor bed). Fluorescence lifetimes and spectral intensity ratios were calculated for three spectral channels, producing a set of six FLIm parameters. Current results from 10 patients undergoing TORS procedures demonstrate that healthy epithelium can be resolved from cancer (P < .001) for at least one FLIm parameter. We also showed that a multiparameter linear discriminant analysis approach provides superior discrimination to individual FLIm parameters for tissue imaged both in vivo and ex vivo. Overall, this study highlights the potential for FLIm to be developed into a diagnostic tool for clinical cancer applications of the oropharynx. This technique could help to circumvent the issues posed by the lack of tactile feedback associated with robotic surgical platforms to better enable cancer delineation.

Deletion of the cytoplasmic domain of N-cadherin reduces, but does not eliminate, traction force-transmission.

Collective migration of epithelial cells is an integral part of embryonic development, wound healing, tissue renewal and carcinoma invasion. While previous studies have focused on cell-extracellular matrix adhesion as a site of migration-driving, traction force-transmission, cadherin mediated cell-cell adhesion is also capable of force-transmission. Using a soft elastomer coated with purified N-cadherin as a substrate and a Hepatocyte Growth Factor-treated, transformed MDCK epithelial cell line as a model system, we quantified traction transmitted by N-cadherin-mediated contacts. On a substrate coated with purified extracellular domain of N-cadherin, cell surface N-cadherin proteins arranged into puncta. N-cadherin mutants (either the cytoplasmic deletion or actin-binding domain chimera), however, failed to assemble into puncta, suggesting the assembly of focal adhesion like puncta requires the cytoplasmic domain of N-cadherin. Furthermore, the cytoplasmic domain deleted N-cadherin expressing cells exerted lower traction stress than the full-length or the actin binding domain chimeric N-cadherin. Our data demonstrate that N-cadherin junctions exert significant traction stress that requires the cytoplasmic domain of N-cadherin, but the loss of the cytoplasmic domain does not completely eliminate traction force transmission.