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Related Concept Videos

Raman Spectroscopy: Overview01:20

Raman Spectroscopy: Overview

380
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...
380
  1. Home
  3. A Molecular Typing Method For Invasive Breast Cancer By Serum Raman Spectroscopy.
  1. Home
  3. A Molecular Typing Method For Invasive Breast Cancer By Serum Raman Spectroscopy.

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A Molecular Typing Method for Invasive Breast Cancer by Serum Raman Spectroscopy.

Jun Jiang1, Lintao Li2, Gang Yin2

  • 1School of Medicine, University of Electronic Science and Technology of China, Chengdu, China; Department of Breast Surgery, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China.

Clinical Breast Cancer
|March 16, 2024

View abstract on PubMed

Summary
This summary is machine-generated.

Raman spectroscopy and machine learning accurately identify breast cancer subtypes from blood samples. This novel approach offers a precise and rapid method for molecular typing, aiding in clinical decisions.

Keywords:
Breast carcinomLiquid biopsyMolecular subtypeRaman spectrum analysisSupport vector machine

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Area of Science:

  • Biomedical Spectroscopy
  • Machine Learning in Oncology
  • Cancer Diagnostics

Background:

  • Breast cancer is a leading and heterogeneous cancer.
  • Immunohistochemistry is standard for molecular subtyping, guiding treatment and prognosis.
  • Novel methods for accurate breast cancer subtyping are needed.

Purpose of the Study:

  • To investigate Raman spectroscopy combined with support vector machine (SVM) learning for a novel breast cancer molecular typing approach.
  • To assess the feasibility of analyzing blood samples for breast cancer subtyping.

Main Methods:

  • Collected blood and biopsy samples from 459 invasive breast cancer patients.
  • Classified tumor subtypes using immunohistochemistry and in situ hybridization.
  • Developed SVM models using Raman spectra from blood samples, with 80% for training and 20% for validation.

Main Results:

  • SVM classification models achieved Area Under the Curve (AUC) values exceeding 0.85.
  • Demonstrated outstanding performance and excellent discrimination of breast cancer molecular subtypes.
  • Validated the model's effectiveness on an independent dataset.

Conclusions:

  • Raman spectroscopy of serum samples can rapidly and accurately detect invasive breast cancer molecular subtypes.
  • This technique shows significant potential for clinical application in breast cancer diagnostics.
  • Offers a non-invasive method for precise molecular subtyping.