Mutation Endmember Library Sparse Mixed Abundance Estimation Model for Glioma Margin Determination with Raman Spectroscopy

Abstract

The glioma margin is a region of brain tissue where glioblastoma tissue transitions to normal tissue with varying levels of cancer cell concentration. This article uses Raman spectroscopy to detect the glioma margin, which is a fuzzy and uncertain substance that cannot be accurately identified by conventional pattern recognition algorithms. This article applies abundance estimation to Raman spectral unmixing of glioma marginal tissues for the accurate and real-time determination of the tumor surgical boundary during an operation. This article introduces a novel method: the mutation endmember library sparse mixed abundance estimation model. This method adds different representative Raman spectra to each endmember library to account for its dynamic properties, thus reducing errors from such variations and fully capturing the diversity within the substance. Moreover, it uses group sparse endmember bundle decomposition, where each substance endmember library consists of multiple Raman spectra. Fractionally mixed norms are used to ensure intergroup and intragroup sparsity, eliminate redundant spectra, and enhance the generalization ability of the abundance estimation. This method was compared with conventional abundance estimation methods. The experimental results of 112 human glioma margin tissues demonstrate that this method outperforms other methods in terms of accuracy, stability, and generalization ability. This article demonstrates the potential of miniature Raman spectroscopy as a new approach to in vivo and noninvasively determining intraoperative margin assessment.