Precise Identification of Glioblastoma Micro-Infiltration at Cellular Resolution by Raman Spectroscopy.

in Advanced science (Weinheim, Baden-Wurttemberg, Germany) by Lijun Zhu, Jianrui Li, Jing Pan, Nan Wu, Qing Xu, Qing-Qing Zhou, Qiang Wang, Dong Han, Ziyang Wang, Qiang Xu, Xiaoxue Liu, Jingxing Guo, Jiandong Wang, Zhiqiang Zhang, Yiqing Wang, Huiming Cai, Yingjia Li, Hao Pan, Longjiang Zhang, Xiaoyuan Chen, Guangming Lu

TLDR

  • The study is about finding a way to see tiny cancer cells in the brain that are hard to see with other methods. They used a special tool called Raman spectroscopy to look at the tissue from the brain and found that there were differences in the way the cells looked. They also used a computer program to help them see the differences more clearly. The study found that Raman spectroscopy could find the tiny cancer cells with high accuracy and sensitivity. This could help doctors better treat patients with this type of cancer.

Abstract

Precise identification of glioblastoma (GBM) microinfiltration, which is essential for achieving complete resection, remains an enormous challenge in clinical practice. Here, the study demonstrates that Raman spectroscopy effectively identifies GBM microinfiltration with cellular resolution in clinical specimens. The spectral differences between infiltrative lesions and normal brain tissues are attributed to phospholipids, nucleic acids, amino acids, and unsaturated fatty acids. These biochemical metabolites identified by Raman spectroscopy are further confirmed by spatial metabolomics. Based on differential spectra, Raman imaging resolves important morphological information relevant to GBM lesions in a label-free manner. The area under the receiver operating characteristic curve (AUC) for Raman spectroscopy combined with machine learning in detecting infiltrative lesions exceeds 95%. Most importantly, the cancer cell threshold identified by Raman spectroscopy is as low as 3 human GBM cells per 0.01 mm. Raman spectroscopy enables the detection of previously undetectable diffusely infiltrative cancer cells, which holds potential value in guiding complete tumor resection in GBM patients.

Overview

  • The study aims to develop a non-invasive method for identifying glioblastoma (GBM) microinfiltration in clinical specimens using Raman spectroscopy. The hypothesis being tested is that Raman spectroscopy can effectively identify GBM microinfiltration with cellular resolution in clinical specimens. The study uses Raman spectroscopy to analyze tissue samples from GBM patients and healthy controls. The methodology involves acquiring Raman spectra from the tissue samples and using machine learning algorithms to analyze the data. The primary objective of the study is to develop a Raman-based method for detecting GBM microinfiltration in clinical specimens with high accuracy and sensitivity.

Comparative Analysis & Findings

  • The study compares the Raman spectra of GBM tissue samples with those of healthy brain tissue samples. The results show that there are significant differences in the Raman spectra between the two groups. The spectral differences are attributed to phospholipids, nucleic acids, amino acids, and unsaturated fatty acids. The study also uses spatial metabolomics to confirm the Raman spectra results. Based on the differential spectra, Raman imaging resolves important morphological information relevant to GBM lesions in a label-free manner. The area under the receiver operating characteristic curve (AUC) for Raman spectroscopy combined with machine learning in detecting infiltrative lesions exceeds 95%. The cancer cell threshold identified by Raman spectroscopy is as low as 3 human GBM cells per 0.01 mm. These findings suggest that Raman spectroscopy can effectively identify GBM microinfiltration with high accuracy and sensitivity.

Implications and Future Directions

  • The study's findings have significant implications for clinical practice, as they provide a non-invasive method for identifying GBM microinfiltration in clinical specimens. The method developed in this study can potentially guide complete tumor resection in GBM patients. The study identifies several limitations, including the need for further validation in larger patient cohorts and the potential for false positives. Future research directions could include the development of a Raman-based method for real-time monitoring of GBM microinfiltration during surgery and the integration of Raman spectroscopy with other imaging modalities, such as magnetic resonance imaging (MRI). The study also highlights the potential of Raman spectroscopy for identifying other types of cancer microinfiltration in clinical specimens.
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