Identification of lymphoma types using 2D light scattering microscopy and machine learning

Lymphomas encompass Hodgkin lymphoma and non-Hodgkin lymphoma. In this study, we have developed a static cytometry leveraging laser and microscope technology to capture 2D light scattering patterns of individual cells. Within this method, a single lymphoma cell is positioned in a liquid-based chip and vertically stimulated by a 532 nm green laser. The resulting light scattering pattern of the cell is observed and recorded by a COMS detector through a microscope optical system, covering a polar angle range of 75 to 105 degrees. By extracting and analyzing the characteristic values from these scattering patterns, we can achieve lymphoma cell identification. In this study, we successfully differentiated between HDLM-2 and Daudi cells using the SVM algorithm, achieving a classification accuracy of 88%. This outcome underscores the potential of our 2D light scattering static cytometry for lymphoma cell classification, offering a marker-free, cost-effective approach for early cancer screening at the single-cell level.