Assessment of Primary Human Liver Cancer Cells by Artificial Intelligence-Assisted Raman Spectroscopy
Concetta Esposito, Mohammed Janneh, Sara Spaziani, Vincenzo Calcagno, Mario Luca Bernardi, Martina Iammarino, Chiara Verdone, Maria Tagliamonte, Luigi Buonaguro, Marco Pisco, Lerina Aversano, Andrea Cusano
Abstract
We investigated the possibility of using Raman spectroscopy assisted by artificial intelligence methods to identify liver cancer cells and distinguish them from their Non-Tumor counterpart. To this aim, primary liver cells (40 Tumor and 40 Non-Tumor cells) obtained from resected hepatocellular carcinoma (HCC) tumor tissue and the adjacent non-tumor area (negative control) were analyzed by Raman micro-spectroscopy. Preliminarily, the cells were analyzed morphologically and spectrally. Then, three machine learning approaches, including multivariate models and neural networks, were simultaneously investigated and successfully used to analyze the cells' Raman data. The results clearly demonstrate the effectiveness of artificial intelligence (AI)-assisted Raman spectroscopy for Tumor cell classification and prediction with an accuracy of nearly 90% of correct predictions on a single spectrum.