Multimodal detection system for lymphoma integrating 2D light scattering and electrochemical techniques

Lymphoma has become one of the most prevalent malignant tumors in China, with a significant rise in incidence among young people in recent years. Early diagnosis and treatment are therefore crucial for improving patient outcomes, including efficacy, survival, and quality of life. In this study, we developed a multimodal detection system that combines twodimensional (2D) light scattering and electrochemical techniques to differentiate between normal and tumor cells at the single-cell and molecular level. Using a laser microscopy detection system, we capture 2D light scattering images of individual cells, where the lymphocytes display distinctive patch-like patterns. The texture of these patterns is influenced by the internal cellular structures, and the differentiation of normal and tumor cells is achieved by extracting and analyzing the eigenvalues from the light scattering images. Additionally, electrochemical sensors detect hydrogen peroxide levels in the cellular solution by measuring changes in current, with tumor cells producing a greater current variation than normal cells. A support vector machine (SVM) algorithm was employed to distinguish between normal and tumor cells, achieving an accuracy of 88%. The results demonstrate that the multimodal detection system effectively differentiates normal and tumor cells from both physical and chemical perspectives, enhancing detection accuracy. This system offers a nondestructive, efficient, and cost-effective method for early cancer screening.