Back

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

Alhiyari, Y.; Liu, Y.; Mukdad, L.; Odion, R. A.; Evans, L. K.; Shori, R.; Vo-Dinh, T.; St. John, M.

2023-09-22 cancer biology
10.1101/2023.09.19.558473 bioRxiv
Show abstract

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.

Matching journals

The top 7 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.