Convolutional neural network-assisted Raman spectroscopy for high-precision diagnosis of glioblastoma.

Journal: Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
PMID: 39721487

Abstract

Glioblastoma multiforme (GBM) is the most lethal intracranial tumor with a median survival of approximately 15 months. Due to its highly invasive properties, it is particularly difficult to accurately identify the tumor margins intraoperatively. The current gold standard for diagnosing GBM during surgery is pathology, but it is time-consuming. Under these circumstances, we developed a method combining Raman spectroscopy (RS) with convolutional neural networks (CNN) to distinguish GBM. Analysis of the spectra of normal brain samples (478 spectra) and GBM samples (462 spectra) from 29 in situ intracranial tumor-bearing mice showed that this method identified GBM tissue with 96.8 % accuracy. Subsequently, spectral analysis of 23 normal human brain tissues (223 spectra) versus 21 tissues from patients with pathologically diagnosed GBM (267 spectra) revealed that the accuracy of this method was 93.9 %. Most importantly, for the difference peaks in the spectra of GBM and normal brain tissue, the common difference peaks in the mouse and human spectra were at 750 cm, 1440 cm, and 1586 cm, which emphasized the differences in cytochrome C and lipids between GBM samples and normal brain samples in both mice and human. The preliminary results showed that CNN-assisted RS is simple to operate and can rapidly and accurately identify whether it is GBM tissue or normal brain tissue.

Authors

  • Jiawei He
    Department of Critical Care Medicine, Beijing Friendship Hospital, Capital Medical University, Beijing, China.
  • Hongmei Li
    Department of Radiology, Subei People's Hospital of Jiangsu Province, Yangzhou Jiangsu, China.
  • Bingchang Zhang
    Department of Neurosurgery and Department of Neuroscience, The First Affiliated Hospital of Xiamen University, School of Medicine, College of Chemistry and Chemical Engineering, College of Energy, Institute of Artificial Intelligence, State Key Laboratory for Physical Chemistry of Solid Surfaces, Xiamen University, Xiamen 361102, China.
  • Gehao Liang
    Department of Neurosurgery and Department of Neuroscience, The First Affiliated Hospital of Xiamen University, School of Medicine, College of Chemistry and Chemical Engineering, College of Energy, Institute of Artificial Intelligence, State Key Laboratory for Physical Chemistry of Solid Surfaces, Xiamen University, Xiamen 361102, China.
  • Liang Zhang
  • Wentao Zhao
    Shanghai New Tobacco Product Research Institute Limited Company, Shanghai 200082, China.
  • Wenpeng Zhao
    Department of Neurosurgery and Department of Neuroscience, The First Affiliated Hospital of Xiamen University, School of Medicine, College of Chemistry and Chemical Engineering, College of Energy, Institute of Artificial Intelligence, State Key Laboratory for Physical Chemistry of Solid Surfaces, Xiamen University, Xiamen 361102, China.
  • Yue-Jiao Zhang
    Department of Neurosurgery and Department of Neuroscience, The First Affiliated Hospital of Xiamen University, School of Medicine, College of Chemistry and Chemical Engineering, College of Energy, Institute of Artificial Intelligence, State Key Laboratory for Physical Chemistry of Solid Surfaces, Xiamen University, Xiamen 361102, China. Electronic address: zhangyuejiao@xmu.edu.cn.
  • Zhan-Xiang Wang
    Department of Neurosurgery and Department of Neuroscience, The First Affiliated Hospital of Xiamen University, School of Medicine, College of Chemistry and Chemical Engineering, College of Energy, Institute of Artificial Intelligence, State Key Laboratory for Physical Chemistry of Solid Surfaces, Xiamen University, Xiamen 361102, China. Electronic address: sjwkwzx@163.com.
  • Jian-Feng Li
    YIZHENG Hospital, Drum Tower Hospital Group of Nanjing, Jiangsu, China. Electronic address: lisword.good@163.com.

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