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Related Experiment Video

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A machine learning-enhanced gastric cancer diagnostic method based on shell-isolated nanoparticle-enhanced Raman

Mengya Li1,2, Liyi Li3,4, Pan Yang3,4

  • 1Department of Laboratory Medicine, First Affiliated Hospital, Third Military Medical University (Army Medical University), Chongqing 400038, China.

Nanoscale
|October 13, 2025
PubMed
Summary

This study introduces a new serum test using shell-isolated nanoparticle-enhanced Raman spectroscopy (SHINERS) for early gastric cancer (GC) detection. The SHINERS method combined with machine learning shows promise for rapid, non-invasive gastric cancer screening.

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Area of Science:

  • Nanotechnology
  • Biomedical Diagnostics
  • Spectroscopy

Background:

  • Gastric cancer (GC) is a leading cause of cancer death globally.
  • Early detection is crucial for improving patient outcomes.
  • Current diagnostic methods have limitations in efficiency and invasiveness.

Purpose of the Study:

  • To develop a non-invasive serum diagnostic approach for early gastric cancer detection.
  • To utilize shell-isolated nanoparticle-enhanced Raman spectroscopy (SHINERS) for improved signal enhancement and specificity.
  • To evaluate the performance of machine learning models for classifying gastric cancer based on serum spectra.

Main Methods:

  • Synthesis of silver-coated silica (Ag@SiO2) core-shell nanoparticles as SHINERS substrates.
  • Collection of serum samples from 100 GC patients and 100 healthy controls.
  • Analysis of serum samples using SHINERS and development of four machine learning classification models (1D-CNN, RF, SVM, kNN).

Main Results:

  • SHINERS provided distinct molecular fingerprint spectra with high signal-to-noise ratios from small serum volumes within a short detection window.
  • The Support Vector Machine (SVM) model achieved the highest classification performance, with an area under the ROC curve of 0.9000.
  • The SVM model significantly outperformed other tested machine learning algorithms (p < 0.01).

Conclusions:

  • The combination of SHINERS and machine learning offers a feasible strategy for reliable, rapid, and minimally invasive gastric cancer screening.
  • This approach demonstrates significant potential for clinical diagnostic applications in early gastric cancer detection.
  • SHINERS technology effectively overcomes limitations of conventional Raman spectroscopy for biological sample analysis.