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Spatially Resolved Fibre-Optic Probe for Cervical Precancer Detection Using Fluorescence Spectroscopy and

Shivam Shukla1, Bhaswati Singha Deo1, Nemichand2

  • 1Center for Lasers and Photonics, IIT Kanpur, Kanpur, India.

Journal of Biophotonics
|October 8, 2024
PubMed
Summary

Spatially resolved fluorescence spectroscopy, combined with principal component analysis and artificial neural networks, accurately detects cervical precancer. This non-invasive method distinguishes between normal and precancerous tissues with high precision.

Keywords:
artificial neural networkcervical cancerepithelial cancerfibre‐optic probespatially resolved fluorescence spectroscopy

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Area of Science:

  • Biomedical Optics
  • Medical Spectroscopy
  • Cancer Diagnostics

Background:

  • Early detection of cervical cancer relies on identifying subtle biochemical and morphological changes in the epithelium.
  • Conventional methods for cervical cancer detection can be invasive and time-consuming.
  • Fluorescence spectroscopy offers a non-invasive, real-time approach to detect these changes with high accuracy.

Purpose of the Study:

  • To develop and validate a custom-designed, spatially resolved fiber-optic probe (SRFOP) for cervical cancer detection using fluorescence spectroscopy.
  • To classify different grades of cervical precancer based on their fluorescence spectra.
  • To evaluate the efficacy of a robust classification algorithm integrating principal component analysis (PCA) and artificial neural networks (ANN).

Main Methods:

  • Utilized a custom SRFOP with 77 fibers arranged in concentric rings for spectral acquisition.
  • Excited cervical tissue samples (n=28) with a 405 nm laser diode and collected fluorescence spectra (400-700 nm) using a USB 4000 spectrometer.
  • Applied PCA for dimensionality reduction, selecting the top 10 principal components as input features for an ANN classifier.

Main Results:

  • Proximal fibers (PFs) of the SRFOP demonstrated superior performance over distal fibers (DFs) in capturing discriminatory spectral features from the epithelium.
  • The combined PCA-ANN classification approach achieved high diagnostic performance.
  • Achieved an average sensitivity of 93.33%, specificity of 96.67%, and accuracy of 95.57% in discriminating cervical precancer grades and normal tissues.

Conclusions:

  • Spatially resolved fluorescence spectroscopy, coupled with PCA-ANN, is a highly effective non-invasive technique for early cervical cancer detection.
  • The SRFOP design and data analysis pipeline show significant promise for clinical application in cervical precancer diagnosis.
  • This approach offers a sensitive and specific method for differentiating normal cervical tissue from various grades of precancer.