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.
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.
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..
In this work, we evaluate the potential for Fluorescence Lifetime Imaging (FLIm) to complement a surgeon's visual, endoscopic, and pathologic assessment of the adequacy of intraoperative tumor resection in clinical cancer applications of the oral cavity and oropharynx. Using a custom-built FLIm instrument during both non-robotic and robotic assisted surgical procedures, we show that intrapatient contrast between healthy and tumor tissue can be achieved intraoperatively in vivo prior to cancer resection with statistical significance (p<0.001) in 9/9 patients using at least 1/6 FLIm parameters, and ex vivo for surgically excised specimens (p<0.001) for 8/9 patients. We employ a multi-parameter linear discriminant analysis approach to demonstrate superior pathology discrimination ability through leveraging a weighted combination of all FLIm metrics. We also highlight interpatient comparisons to evaluate how FLIm signatures vary across different patients and disparate tissue anatomies.
The literature articulates the importance of advancing novel solutions which enable clinicians to intraoperatively resolve pathological tissue from healthy tissue in situ in order to guide the accuracy and efficiency of surgical tumor resection. A method which non-invasively provides real-time delineation of cancer margins has great potential to improve clinical outcomes by accelerating surgical procedural times, ensuring complete tumor resection, and by enabling more conservative resection approaches which preserves healthy tissue. Autofluorescence lifetime imaging is a powerful technique which holds great promise in addressing this clinically unmet need. Using a custom built, fiber-optic based, multi-spectral time-resolved fluorescence spectroscopy (ms-TRFS) instrument (excitation 355 nm) applied to cancer within head & neck anatomy, our preliminary results from 13 human patients indicate that tumor vs. healthy tissue regions (confirmed via histology) can be distinguished on the basis of lifetime and intensity ratio for both in vivo (pre-resection) and ex vivo (post-resection) applications. Each of the three major ms-TRFS spectral bands demonstrate highly conserved lifetime and intensity ratio trends within specific tissue types (palate, palatine tonsil, lingual tonsil, & base of tongue) for cancerous regions when juxtaposed to neighboring healthy peripheral tissue. Current results demonstrate distinct lifetime and intensity ratio results when comparing across tissue types. Collectively, our initial data suggests that time-resolved autofluorescence could serve as a valuable tool for providing real-time intraoperative diagnosis and surgical guidance during robot-assisted cancer removal in otolaryngologic applications.
Autofluorescence lifetime spectroscopy is a promising non-invasive label-free tool for characterization of biological tissues and shows potential to report structural and biochemical alterations in tissue owing to pathological transformations. In particular, when combined with fiber-optic based instruments, autofluorescence lifetime measurements can enhance intraoperative diagnosis and provide guidance in surgical procedures. We investigate the potential of a fiber-optic based multi-spectral time-resolved fluorescence spectroscopy instrument to characterize the autofluorescence fingerprint associated with histologic, morphologic and metabolic changes in tissue that can provide real-time contrast between healthy and tumor regions in vivo and guide clinicians during resection of diseased areas during transoral robotic surgery. To provide immediate feedback to the surgeons, we employ tracking of an aiming beam that co-registers our point measurements with the robot camera images and allows visualization of the surgical area augmented with autofluorescence lifetime data in the surgeon’s console in real-time. For each patient, autofluorescence lifetime measurements were acquired from normal, diseased and surgically altered tissue, both in vivo (pre- and post-resection) and ex vivo. Initial results indicate tumor and normal regions can be distinguished based on changes in lifetime parameters measured in vivo, when the tumor is located superficially. In particular, results show that autofluorescence lifetime of tumor is shorter than that of normal tissue (p < 0.05, n = 3). If clinical diagnostic efficacy is demonstrated throughout this on-going study, we believe that this method has the potential to become a valuable tool for real-time intraoperative diagnosis and guidance during transoral robot assisted cancer removal interventions.
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