Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Raman Spectroscopy Instrumentation: Overview01:26

Raman Spectroscopy Instrumentation: Overview

530
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...
530
Raman Spectroscopy: Overview01:20

Raman Spectroscopy: Overview

593
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...
593

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Mechanistic and Therapeutic Approach of Plants as an Anti-inflammatory Agent: A Review.

Mini reviews in medicinal chemistry·2026
Same author

Effect of vacuum plasma treatment duration on physicochemical, mechanical, and biocompatibility properties of bacteriophage-incorporated PVA/duck egg white nanofibers.

RSC advances·2026
Same author

Structural basis for substrate recognition by the pain-associated neuronal polyamine transporter SLC45A4.

International journal of biological macromolecules·2026
Same author

Integrating stemness and epithelial-mesenchymal transition signatures with machine learning identifies RUNX1 as a therapeutic vulnerability in colorectal cancer.

Computers in biology and medicine·2026
Same author

Intermediate filament protein Vimentin propels intrahepatic lipid accumulation in insulin-resistant mice.

The Journal of biological chemistry·2026
Same author

Traditional antidiabetic medicinal plants of Himachal Pradesh: ethnomedicinal evidence and drug-discovery potential.

Frontiers in nutrition·2026

Related Experiment Video

Updated: Sep 9, 2025

A Multimodal Imaging Framework to Advance Phenotyping of Living Label-free Breast Cancer Cells
10:37

A Multimodal Imaging Framework to Advance Phenotyping of Living Label-free Breast Cancer Cells

Published on: August 22, 2025

154

Raman Spectroscopy and Machine Learning in the Diagnosis of Breast Cancer.

Sowndarya Rao1, Nikita Sharma1, Vyasraj G Bhat2

  • 1Department of Bioinformatics, Manipal School of Life Sciences, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India.

Lasers in Medical Science
|September 2, 2025
PubMed
Summary

Machine learning and Raman spectroscopy offer a non-invasive, accurate method for early breast cancer detection. This combination shows high diagnostic accuracy, improving patient prognoses and potentially transforming cancer diagnostics.

Keywords:
Breast cancerMachine learningRaman spectroscopySystematic review

More Related Videos

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.9K
Detection and Isolation of Cancer in Prostate Biopsies Using Stimulated Raman Histology and Artificial Intelligence
08:05

Detection and Isolation of Cancer in Prostate Biopsies Using Stimulated Raman Histology and Artificial Intelligence

Published on: June 10, 2025

659

Related Experiment Videos

Last Updated: Sep 9, 2025

A Multimodal Imaging Framework to Advance Phenotyping of Living Label-free Breast Cancer Cells
10:37

A Multimodal Imaging Framework to Advance Phenotyping of Living Label-free Breast Cancer Cells

Published on: August 22, 2025

154
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.9K
Detection and Isolation of Cancer in Prostate Biopsies Using Stimulated Raman Histology and Artificial Intelligence
08:05

Detection and Isolation of Cancer in Prostate Biopsies Using Stimulated Raman Histology and Artificial Intelligence

Published on: June 10, 2025

659

Area of Science:

  • Biomedical Engineering
  • Medical Diagnostics
  • Computational Biology

Background:

  • Breast cancer is a leading global cancer in women, necessitating improved diagnostic methods.
  • Traditional diagnostics like mammograms and biopsies can be invasive and lack precision.
  • Early detection significantly improves breast cancer patient outcomes.

Purpose of the Study:

  • To review the efficacy of combining machine learning (ML) and Raman spectroscopy (RS) for breast cancer diagnosis.
  • To assess the diagnostic accuracy, sensitivity, and specificity of ML-RS techniques.
  • To identify challenges and future directions for RS-ML applications in breast cancer detection.

Main Methods:

  • Systematic literature review using PRISMA methodology.
  • Inclusion criteria: studies from 2017-2024 with sensitivity/specificity >80%.
  • Analysis of ML algorithms (SVM, CNN, LDA) applied to RS data from biological samples.

Main Results:

  • Nine studies met the inclusion criteria, demonstrating high diagnostic performance.
  • Combined RS-ML methods frequently achieved sensitivity and specificity exceeding 90%.
  • RS-ML is adaptable for analyzing various biological samples (tissues, serum) and real-time applications.

Conclusions:

  • Raman spectroscopy combined with machine learning presents a highly accurate and non-invasive approach for breast cancer diagnosis.
  • The RS-ML technique shows promise for intraoperative and real-time cancer evaluations.
  • Further research is needed for standardized frameworks, multi-center validation, and cost-effective technology development.