In Vitro Antibody Quantification with Hyperspectral Imaging in a Large Field of View for Clinical Applications.

Hyperspectral imaging (HSI) is a non-invasive, contrast-free optical-based tool that has recently been applied in medical and basic research fields. The opportunity to use HSI to identify exogenous tumor markers in a large field of view (LFOV) could increase precision in oncological diagnosis and surgical treatment. In this study, the anti-high mobility group B1 (HMGB1) labeled with Alexa fluorophore (647 nm) was used as the target molecule. This is the proof-of-concept of HSI's ability to quantify antibodies via an in vitro setting. A first test was performed to understand whether the relative absorbance provided by the HSI camera was dependent on volume at a 1:1 concentration. A serial dilution of 1:1, 10, 100, 1000, and 10,000 with phosphatase-buffered saline (PBS) was then used to test the sensitivity of the camera at the minimum and maximum volumes. For the analysis, images at 640 nm were extracted from the hypercubes according to peak signals matching the specificities of the antibody manufacturer. The results showed a positive correlation between relative absorbance and volume (r = 0.9709, p = 0.0013). The correlation between concentration and relative absorbance at min (1 µL) and max (20 µL) volume showed r = 0.9925, p < 0.0001, and r = 0.9992, p < 0.0001, respectively. These results demonstrate the HSI potential in quantifying HMGB1, hence deserving further studies in ex vivo and in vivo settings.

Automatic Liver Viability Scoring with Deep Learning and Hyperspectral Imaging.

Hyperspectral imaging (HSI) is a non-invasive imaging modality already applied to evaluate hepatic oxygenation and to discriminate different models of hepatic ischemia. Nevertheless, the ability of HSI to detect and predict the reperfusion damage intraoperatively was not yet assessed. Hypoxia caused by hepatic artery occlusion (HAO) in the liver brings about dreadful vascular complications known as ischemia-reperfusion injury (IRI). Here, we show the evaluation of liver viability in an HAO model with an artificial intelligence-based analysis of HSI. We have combined the potential of HSI to extract quantitative optical tissue properties with a deep learning-based model using convolutional neural networks. The artificial intelligence (AI) score of liver viability showed a significant correlation with capillary lactate from the liver surface (r = -0.78, p = 0.0320) and Suzuki's score (r = -0.96, p = 0.0012). CD31 immunostaining confirmed the microvascular damage accordingly with the AI score. Our results ultimately show the potential of an HSI-AI-based analysis to predict liver viability, thereby prompting for intraoperative tool development to explore its application in a clinical setting.