Rapid identification of benign and malignant pancreatic tumors using serum Raman spectroscopy combined with classification algorithms

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The survival time of patients with pancreatic tumors is closely related to the type of tumor. To diagnose patients with malignant pancreatic tumors as early as possible in order to improve patient survival, we proposed a new method for the assisted diagnosis of pancreatic tumors. First, we used partial least squares (PLS) to extract the Raman spectrum information of patients with pancreatic tumors. The study extracted ten eigenvalues experimentally. The cumulative variance of the first six PLS components reached 97.045 %, and then the feature information extracted by PLS was classified. This experiment used three classification algorithms: linear discriminant analysis (LDA), support vector machine (SVM), and k-nearest neighbor (KNN). Among them, the cubic kernel of SVM achieved the best classification effect, and its classification accuracy reached 96.4 %. This is the first time that serum Raman spectroscopy has been used to distinguish patients with benign and malignant tumors of the pancreas. The experimental results show that serum Raman spectroscopy may become a new auxiliary method for the clinical diagnosis of pancreatic cancer.