From Single-Modal Signal Recognition to Multidimensional Precision Detection: Breakthroughs and Innovations of Multi-Modal Sensors in Tumor Biomarker Detection

Abstract

The sensitive detection of tumor markers is of great significance for clinical diagnosis. Due to the extremely low abundance of early tumor biomarkers and severe background interference from biological matrices, traditional single-modal atomolar biological testing generally has limitations such as high false positives and narrow linear ranges, making it difficult to meet the clinical needs of precise diagnosis and treatment. This has driven the iterative development of biosensing technology from a single signal readout mode to multi-modal collaborative analysis, significantly improving the sensitivity and accuracy of complex biological sample detection. Dual-modal sensors integrate two complementary sensing mechanisms, whereas multi-modal sensors produce three or more independent signal outputs. By combining such mechanisms, for example electrochemistry and optics, these platforms can establish a signal cross-validation and self-calibration detection system. This multidimensional analysis strategy can mitigate interference caused by environmental factors and human operational errors to a certain extent, lowering detection limits and broadening the linear range, thereby potentially providing higher-reliability data support for early tumor diagnosis. This work systematically reviews the technological evolution and performance differences in tumor biomarker sensors from single-mode to multi-mode in recent years, with a focus on summarizing the cutting-edge research progress in this field. It provides a comprehensive reference for researchers and facilitates further applications of multi-modal sensors in point-of-care detection, and artificial intelligence-assisted analysis.