Vo-Dinh, T. | bioRxiv Science

bioRxiv2023

ICG-Functionalized Gold Nanostars As An Effective Contrast Agent For Real-time Tumor Localization with Dynamic Optical Contrast Imaging (DOCI) and Enhanced Radiation Therapy

The primary management of head and neck squamous cell carcinoma relies on complete surgical resection of the tumor. However, the establishment of negative margin complete resection is often difficult given the devastating side effects of aggressive surgery and the anatomic proximity to vital structures. Positive margin status is associated with significantly decreased survival. Currently, surgeons determine where the tumor cuts are made, by palpating the edges of the tumor and using prior imaging. After a tumor is presumed to be removed in its entirety, the surrounding tissues are sampled by frozen section histologic pathology to ensure that no microscopic disease is left behind, the efficacy of which varies and is subject to sampling error. The methodology by which frozen sections are collected whether tumor bed driven or specimen driven can also alter margin outcome. Thus, improving intraoperative detection of tumor margins is key to optimizing treatment and outcomes. Our group has developed a possible solution for this unmet clinical need. We have previously designed Dynamic Optical Contrast Imaging (DOCI), a novel imaging modality that acquires temporally dependent measurements of tissue autofluorescence. Furthermore, we demonstrated that DOCI can distinguish HNSCC from adjacent healthy tissue with a high degree of accuracy. DOCI images are captured in real time and offer an operatively wide field of view. With the addition of ICG conjugated gold nanostars (GNS) we can improve DOCI image contrast between tumors vs normal tissues as well as use the GNS for CT imaging and radiotherapeutic treatment.

cancer biology↗

bioRxiv2022

AI-Enabled Classification of Head and Neck Tumors using Convolutional Neural Networks with Dynamic Optical Contrast Imaging

BackgroundRecent advances in artificial intelligence (AI) in the field of imaging have resulted in new opportunities for automated tumor detection and margin assessment. In particular, AI deep learning techniques such as the Convolutional Neural Network (CNN) have greatly advanced the field of computer vision. Here we introduce the application of a CNN model for use with Dynamic Optical Contrast Imaging (DOCI), an imaging technique developed by our group that creates a unique molecular signature on tissue targets by obtaining the autofluorescence decay of several spectral bands in the UV-Vis range. Methods21 patients undergoing surgical resection for tonsillar squamous cell carcinoma (SCC) were identified on a prospective basis. DOCI images were analyzed and compared to the pathology results as ground truth. A CNN model was used to segment sections of DOCI images and provide a percentage chance of tumor presence, allowing for automated tumor margin delineation without a-priori knowledge of the tumor tissue composition. ResultsCNN outputs yielded a 99.98% confidence in classifying non-tumor tissue and 76.02% confidence in classifying tumor tissue. ConclusionsOur results indicate that a CNN-based classification model for DOCI allows for real-time analysis of tissue, providing improved sensitivity and accuracy of determining true margins and thus enabling the head and neck cancer surgeon to save healthy tissue and improve patient outcomes.

bioinformatics↗