Sentinel lymph node mapping using ICG fluorescence and cone beam CT – a feasibility study in a rabbit model of oral cancer

Abstract

Background Current sentinel lymph node biopsy (SLNB) techniques, including use of radioisotopes, have disadvantages including the use of a radioactive tracer. Indocyanine green (ICG) based near-infrared (NIR) fluorescence imaging and cone beam CT (CBCT) have advantages for intraoperative use. However, limited literature exists regarding their use in head and neck cancer SLNB. Methods This was a prospective, non-randomized study using a rabbit oral cavity VX2 squamous cell carcinoma model (n = 10) which develops lymph node metastasis. Pre-operatively, images were acquired by MicroCT. During surgery, CBCT and NIR fluorescence imaging of ICG was used to map and guide the SLNB resection. Results Intraoperative use of ICG to guide fluorescence resection resulted in identification of all lymph nodes identified by pre-operative CT. CBCT was useful for near real time intraoperative imaging and 3D reconstruction. Conclusions This pre-clinical study further demonstrates the technical feasibility, limitations and advantages of intraoperative NIR-guided ICG imaging for SLN identification as a complementary method during head and neck surgery.

Tumor Accumulation and Off-Target Biodistribution of an Indocyanine-Green Fluorescent Nanotracer: An Ex Vivo Study on an Orthotopic Murine Model of Breast Cancer

“…ICG is a tricarbocyanine, water-soluble fluorescent dye with substantial fluorescence emission in the near-infrared (NIR) wavelength region (700–900 nm) [ 8 , 12 , 13 , 14 ]. Due to its fluorescent characteristics and safety, ICG is currently used for several cancer-related surgical applications, including sentinel lymph node (SLN) mapping, the identification of solid tumors, lymphography, angiography, and anatomical imaging during surgery [ 14 , 15 , 16 , 17 , 18 ]. However, since it lacks specific tumor targeting and suffers from rapid degradation and blood-stream elimination [ 19 , 20 ], the potential for its use in oncological FGS still has certain limitations [ 3 , 21 , 22 ].…”

DeepVinci: A Semantic Segmentation Model with Edge Super-vision and Densely Multi-scale Pyramid Module for DaVinci Gynecological Surgery

“…Since the advent of deep learning in computer vision, the convolutional neural network (CNN) has become the cornerstone of image recognition and segmentation. Network accuracy has improved as the network grows deeper [18][19][20][21][22]. However, this improvement comes at the cost of increased complexity, which poses a challenge for limited computing power.…”

Integrative lymph node-mimicking models created with biomaterials and computational tools to study the immune system

“…Designing LN-mimicking models will allow reconstruction of the lymphatic architecture to gain a better understanding of LN biology and assist the development of targeted immunotherapy. Currently, animal models such as mice and pigs are widely adopted in LN experiments [ 11 , 12 ]. To better simulate the LN microenvironment, decellularized animal-derived LNs have been developed to engineer LN-like scaffolds that preserve intrinsic biological cues and architectural structure, and maintain immunological function [ 48 ].…”

“…However, these tools allow observation of the histological appearances without enabling further verification of the biological hypotheses, which LN-mimicking models can provide. Traditionally, the study of LN function and associated diseases is performed using animal models [ [11] , [12] , [13] ], but this approach has several limitations including a lack of relevant immune system to humans, physiological differences, high costs, intensive labor, complex operations, low reproducibility, and ethical considerations preventing eligibility for selected studies [ 14 ]. For instance, in cancer research, less than 8% of treatments are successfully translated to clinical trials, even though they exhibit significant therapeutic effects on animal models [ 15 ].…”