Early diagnosis and bioimaging of lung adenocarcinoma cells/organs based on spectroscopy machine learning

Abstract

Early diagnosis and fast detection with a high accuracy rate of lung cancer are important to improve the treatment effect. In this research, an early fast diagnosis and in vivo imaging method for lung adenocarcinoma are proposed by collecting the spectral data from normal and patients’ cells/tissues, such as Fourier infrared spectroscopy (FTIR), UV-vis absorbance, and fluorescence spectra using anthocyanin. The FTIR spectra of human normal lung epithelial cells (BEAS-2B cells) and human lung adenocarcinoma cells (A549 cells) were collected. After the data is cleaned, a feature selection algorithm is used to select important wavelengths, and then, the classification models of support vector machine (SVM) and the grid search method are used to select the optimal model parameters (accuracy: 96.89% on the training set and 88.57% on the test set). The optimal model is used to classify all samples, and the accuracy is 94.37%. Moreover, the anthocyanin was prepared and used for the intracellular absorbance and fluorescence, and the optimized algorithm was used for classification (accuracy: 91.38% on the training set and 80.77% on the test set). Most importantly, the in vivo cancer imaging can be performed using anthocyanin. The results show that there are differences between lung adenocarcinoma and normal lung tissues at the molecular level, reflecting the accuracy, intuitiveness, and feasibility of this algorithm-assistant anthocyanin imaging in lung cancer diagnosis, thus showing the potential to become an accurate and effective technical means for basic research and clinical diagnosis.