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Related Concept Videos

Classification of Skeletal Muscle Fibers01:48

Classification of Skeletal Muscle Fibers

Skeletal muscles continuously produce ATP to provide the energy that enables muscle contractions. Skeletal muscle fibers can be categorized into three types based on differences in their contraction speed and how they produce ATP, as well as physical differences related to these factors. Most human muscles contain all three muscle fiber types, albeit in varying proportions.
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Slow oxidative, muscle fibers appear red due to large numbers of capillaries and high levels of...
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Raman Spectroscopy: Overview01:20

Raman Spectroscopy: Overview

The underlying principle of Raman spectroscopy is based on the interaction between light and matter, specifically molecules' inelastic scattering of photons. When a monochromatic beam of light, typically from a laser source, interacts with a sample, most scattered light has the same frequency as the incident light. This is known as Rayleigh scattering.
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Raman Spectroscopy Instrumentation: Overview01:26

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A conventional Raman spectrophotometer includes a laser source, a sample holding system, a wavelength selector, and a detector.
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Updated: May 12, 2026

Real-Time, Two-Color Stimulated Raman Scattering Imaging of Mouse Brain for Tissue Diagnosis
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Ex-vivo Raman spectroscopy and AI-based classification of soft tissue sarcomas.

Maede Boroji1, Vahid Danesh1, David Barrera2

  • 1Department of Mechanical Engineering, Stony Brook University, Stony Brook, New York, United States of America.

Plos One
|September 2, 2025
PubMed
Summary
This summary is machine-generated.

Microscopic Raman spectroscopy shows promise for distinguishing soft tissue sarcomas (STS) from normal tissues. This technique could offer a rapid, non-invasive tool to guide surgeons in identifying abnormal tissues and margins during operations.

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Area of Science:

  • Oncology
  • Biomedical Engineering
  • Spectroscopy

Background:

  • Soft tissue sarcomas (STS) are rare, heterogeneous malignancies requiring precise diagnosis and treatment.
  • Current intraoperative margin assessment methods for STS lack efficiency and accuracy.
  • Improved tools are needed to enhance surgical guidance for soft tissue sarcomas.

Purpose of the Study:

  • To investigate microscopic Raman spectroscopy for differentiating STS subtypes, benign tumors, and normal tissues.
  • To assess the feasibility of using Raman spectra as a rapid diagnostic tool in surgical settings.
  • To develop and validate a computational model for classifying tissue types based on Raman data.

Main Methods:

  • Ex-vivo Raman spectroscopy was performed on 286,672 tissue samples from seven patients.
  • A 633 nm excitation wavelength was utilized for spectral acquisition.
  • A custom ResNet architecture was employed for data pre-processing and classification.

Main Results:

  • The ResNet model achieved a high overall weighted accuracy of 97.1% in classifying tissue types.
  • A low clinical alert rate of 1.46% was recorded, indicating minimal misclassification of malignant tissues.
  • Raman spectroscopy demonstrated significant potential in distinguishing between various soft tissue types.

Conclusions:

  • Microscopic Raman spectroscopy can accurately differentiate between soft tissue sarcomas, benign tumors, and normal tissues.
  • Single Raman spectra show potential as a rapid, non-invasive tool for intraoperative surgical guidance.
  • This technique could improve the precision of identifying abnormal tissues and surgical margins in sarcoma resections.