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.
Stereotactic needle biopsy is a time-consuming and invasive procedure that often cannot accurately distinguish recurrent tumors from treatment effect in gliomas. We report an intraoperative multispectral fluorescence lifetime imaging (FLIm) system coupled with a custom-made fiber optic probe integrated with the stealth biopsy needle as an optical biopsy tool. FLIm parameters collected from 3 suspected recurrent glioma patients changed over the biopsy trajectory as the needle passed different brain areas. An SVM classifier validated using a leave-one-patient-out validation scheme could identify the lesions from the normal surrounding tissue with sensitivity=0.99, specificity=0.91, and accuracy=0.95.
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.
Accurate detection of brain tumor boundaries is crucial for successful tumor removal and better patient outcomes. A novel method using label-free Fluorescence Lifetime Imaging (iFLIm) is presented in this study. The approach involved developing an optimized classification model based on tumor enhancement status, utilizing multispectral FLIm. The method was evaluated on 52 patients with adult-type diffuse glioma, demonstrating promising results with 87% sensitivity, 92% specificity, and an AUC of 0.90. This FLIm-based model has the potential to offer a non-invasive and real-time technique to assist neurosurgeons in accurately identifying tumor infiltrates, potentially improving tumor resection and patient 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.
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