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Machine-Learning-Enabled Raman Spectroscopy Refines Indocyanine Green Fluorescence Boundaries for Precise

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This study introduces a new method combining Raman spectroscopy and indocyanine green (ICG) for faster, more accurate intraoperative glioblastoma diagnosis. The technique overcomes signal interference, enabling precise tumor margin identification during surgery.

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Area of Science:

  • Neuro-oncology
  • Medical Spectroscopy
  • Surgical Technology

Background:

  • Intraoperative cancer diagnosis is crucial for precise tumor resection.
  • Raman spectroscopy offers distinct tissue signatures for glioblastoma detection.
  • Current methods like indocyanine green (ICG) have limitations in accuracy and concurrent use with Raman spectroscopy due to fluorescence interference.

Purpose of the Study:

  • To develop a method for concurrent intraoperative glioblastoma diagnosis using Raman spectroscopy and ICG.
  • To overcome the challenge of ICG fluorescence overwhelming the Raman signal.
  • To enable rapid, machine-learning-based diagnosis for improved surgical guidance.

Main Methods:

  • Developed a signal-processing algorithm to suppress ICG-derived fluorescence interference.
  • Integrated ICG for initial tumor region identification and Raman spectroscopy for detailed analysis.
  • Implemented a machine-learning model for rapid, in vivo diagnosis within seconds.

Main Results:

  • Achieved coregistered acquisition of Raman and ICG data by suppressing fluorescence interference.
  • Enabled machine-learning-based Raman diagnosis with 85% to 90% accuracy in vivo within 3 seconds.
  • Demonstrated that ICG overestimates tumor extent, while Raman spectroscopy accurately identifies histopathological margins.

Conclusions:

  • The developed signal-processing technique enables concurrent use of ICG and Raman spectroscopy for intraoperative glioblastoma diagnosis.
  • This integrated approach provides a real-time corrective filter for fluorescence guidance, improving precision in glioblastoma resection.
  • Offers a practical solution for enhancing surgical outcomes in neuro-oncology.