Quantification of Indocyanine Green Fluorescence Imaging in General, Visceral and Transplant Surgery

Abstract

Near-infrared (NIR) imaging with indocyanine green (ICG) has proven to be useful in general, visceral, and transplant surgery. However, most studies have performed only qualitative assessments. Therefore, a systematic overview of all studies performing quantitative indocyanine green evaluation in general, visceral, and transplant surgeries should be conducted. Free term and medical subject heading (MeSH) term searches were performed in the Medline and Cochrane databases until October 2022. The main categories of ICG quantification were esophageal surgery (24.6%), reconstructive surgery (24.6%), and colorectal surgery (21.3%). Concordantly, anastomotic leak (41%) was the main endpoint, followed by the assessment of flap perfusion (23%) and the identification of structures and organs (14.8%). Most studies examined open surgery (67.6%) or laparoscopic surgery (23.1%). The analysis was mainly carried out using manufacturer software (44.3%) and open-source software (15.6%). The most frequently analyzed parameter was intensity over time for blood flow assessment, followed by intensity alone or intensity-to-background ratios for structure and organ identification. Intraoperative ICG quantification could become more important with the increasing impact of robotic surgery and machine learning algorithms for image and video analysis.

Biotin‐new indocyanine green conjugate: Synthesis, in vitro photocytotoxicity and in vivo biodistribution

“…In addition, ICG has been proposed for breast cancer imaging (Toh et al, 2015). ICG could generate fluorescence, hyperthermia and singlet oxygen upon 808 nm laser irradiation, which allows ICG to be used for molecular imaging and phototherapy in cancer diagnosis and therapeutics (Chen et al, 2016;Hong et al, 2022;Pollmann et al, 2023). Besides, ICG has been widely investigated to combine with other therapeutics such as immunotherapy, chemotherapy and radiotherapy to better suppress cancer (Lian et al, 2023;Lu et al, 2023;Sun et al, 2022;Tu et al, 2020).…”

Optical Insights into Fibrotic Livers: Applications of Near-Infrared Spectroscopy and Machine Learning

“…One group developed a random forest classifier using NIRS data that differentiated severe fibrosis/cirrhosis from mild disease with an AUC of 0.82, compared to 0.77 for APRI and 0.73 for FIB-4. Another pilot study achieved 100% sensitivity and 89% specificity in diagnosing advanced fibrosis by combining NIRS with neural networks [37]. Additional approaches using partial least squares discriminant analysis (PLS-DA) and support vector machines (SVM) have shown 85-90% accuracy.…”