FLIm-Based in Vivo Classification of Residual Cancer in the Surgical Cavity During Transoral Robotic Surgery.

Abstract

Incomplete surgical resection with residual cancer left in the surgical cavity is a potential sequelae of Transoral Robotic Surgery (TORS). To minimize such risk, surgeons rely on intraoperative frozen sections analysis (IFSA) to locate and remove the remaining tumor. This process, may lead to false negatives and is time-consuming. Mesoscopic fluorescence lifetime imaging (FLIm) of tissue fluorophores (i.e., collagen and metabolic co-factors NADH and FAD) emission has demonstrated the potential to demarcate the extent of head and neck cancer in patients undergoing surgical procedures of the oral cavity and the oropharynx. Here, we demonstrate the first label-free FLIm-based classification using a novelty detection model to identify residual cancer in the surgical cavity of the oropharynx. Due to highly imbalanced label representation in the surgical cavity, the model employed solely FLIm data from healthy surgical cavity tissue for training and classified the residual tumors as an anomaly. FLIm data from N = 22 patients undergoing upper aerodigestive oncologic surgery were used to train and validate the classification model using leave-one-patient-out cross-validation. Our approach identified all patients with positive surgical margins (N = 3) confirmed by pathology. Furthermore, the proposed method reported a point-level sensitivity of 0.75 and a specificity of 0.78 across optically interrogated tissue surface for all N = 22 patients. The results indicate that the FLIm-based classification model can identify residual cancer by directly imaging the surgical cavity, potentially enabling intraoperative surgical guidance for TORS.