Optimization of machine learning classification models for tumor cells based on cell elements heterogeneity with laser‐induced breakdown spectroscopy

Abstract

The rapid and accurate diagnosis of cancer is an important topic in clinical medicine. In the present work, an innovative method based on laser‐induced breakdown spectroscopy (LIBS) combined with machine learning was developed to distinguish and classify different tumor cell lines. The LIBS spectra of cells were first acquired. Then the spectral pre‐processing was performed as well as detailed optimization to improve the classification accuracy. After that, the convolutional neural network (CNN), support vector machine (SVM), and K‐nearest neighbors were further compared for the optimized classification ability of tumor cells. Both the CNN algorithm and SVM algorithm have achieved impressive discrimination performances for tumor cells distinguishing, with an accuracy of 97.72%. The results show that the heterogeneity of elements in tumor cells plays an important role in distinguishing the cells. It also means that the LIBS technique can be used as a fast classification method for classifying tumor cells.

Assessment of the Electrolyte Heterogeneity of Tissues in Mandibular Bone-Infiltrating Head and Neck Cancer Using Laser-Induced Breakdown Spectroscopy

“…This observation emphasizes the tumor-associated significance of K, which we have already described in the context of discrimination between healthy bone substances and bone-infiltrating tumor tissue [20]. While Wang et al [30] recently showed that the heterogeneity of elements within tumor cells can be used for LIBS-based discrimination of different tumor cell lines, our study proves that the distribution of elements is also significantly different between different areas within mandibular bone-infiltrating head and neck tumors. Further development of nano-LIBS systems is expected to have great potential for cellular research in the future [31], which could also contribute to addressing current challenges in the diagnosis and treatment of head and neck cancers.…”