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Related Concept Videos

Raman Spectroscopy: Overview01:20

Raman Spectroscopy: Overview

484
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.
However, a small fraction of the scattered light exhibits a frequency shift due to the exchange of energy between the incident photons and...
484
Raman Spectroscopy Instrumentation: Overview01:26

Raman Spectroscopy Instrumentation: Overview

475
A conventional Raman spectrophotometer includes a laser source, a sample holding system, a wavelength selector, and a detector.
The monochromatic laser source, typically using visible or near-infrared radiation, generates a highly focused beam of light. This light interacts with the molecules of the sample, scattering some of the light. Liquid and gaseous samples are usually tested in ordinary glass capillaries, while solids can be analyzed as powders packed in capillaries or as potassium...
475

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Detection and Isolation of Cancer in Prostate Biopsies Using Stimulated Raman Histology and Artificial Intelligence
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Raman spectroscopy and topological machine learning for cancer grading.

Francesco Conti1,2, Mario D'Acunto3, Claudia Caudai4

  • 1Institute of Information Science and Technologies, National Research Council of Italy, Via G. Moruzzi 1, Pisa, 56124, Italy. francesco.conti@phd.unipi.it.

Scientific Reports
|May 4, 2023
PubMed
Summary
This summary is machine-generated.

Raman spectroscopy combined with persistent homology and machine learning accurately classifies tumor tissues. This approach shows promise for improving chondrosarcoma grading in clinical practice.

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Area of Science:

  • Biomedical Engineering
  • Computational Biology
  • Spectroscopy

Background:

  • Raman spectroscopy offers biochemical mapping for tumor tissue classification.
  • Tumor grading requires accurate tissue differentiation based on molecular composition.

Purpose of the Study:

  • To evaluate the efficacy of combining persistent homology and machine learning for Raman spectra classification in tumor grading.
  • To develop an automated pipeline for selecting optimal topological features and machine learning classifiers for chondrosarcoma grading.

Main Methods:

  • Extraction of topological features from Raman spectra using persistent homology.
  • Training machine learning classifiers (specifically, a support vector classifier) with Betti Curve representations.
  • Utilizing cross and leave-one-patient-out cross-validation for accuracy assessment.

Main Results:

  • The combined approach achieved 81% validation accuracy and 90% test accuracy for binary classification of chondrosarcoma.
  • High accuracy was maintained even with data acquired at different times and using different equipment.
  • The Betti Curve representation combined with a support vector classifier demonstrated superior performance compared to existing literature.

Conclusions:

  • Persistent homology and machine learning integration provides a robust method for Raman spectra-based tumor grading.
  • The developed model shows potential for seamless integration into clinical settings for improved chondrosarcoma diagnosis.
  • This technique offers a promising advancement in objective and automated tumor classification systems.