Raman spectroscopy analysis combined with computed tomography imaging to identify microsatellite instability in gastric cancers.

Accurate determination of microsatellite instability (MSI) status is critical for tailoring treatment approaches for gastric cancer patients. Existing clinical techniques for MSI diagnosis are plagued by problems of suboptimal time efficiency, high cost, and burdensome experimental requirements. Here, we for the first time establish the classification model of gastric cancer MSI status based on Raman spectroscopy. To begin with, we reveal that tumor heterogeneity-induced signal variations pose a prominent impact on MSI classification. To eliminate this issue, we develop Euclidean distance-based Raman Spectroscopy (EDRS) algorithm, which establishes a standard spectrum to represent the "most microsatellite stable" status. The similarity between each spectrum of tissues with the standard spectrum is calculated to provide a direct assessment on the MSI status. Compared to machine learning-algorithms including k-Nearest Neighbors, Random Forest, and Extreme Learning Machine, the EDRS method shows the highest accuracy of 94.6 %. Finally, we integrate the EDRS method with the clinical diagnostic modality, computed tomography, to construct an innovative joint classification model with good classification performance (AUC = 0.914, Accuracy = 94.6 %). Our work demonstrates a robust, rapid, non-invasive, and convenient tool in identifying the MSI status, and opens new avenues for Raman techniques to fit into existing clinical workflow.