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System transferability of Raman-based oesophageal tissue classification using modern machine learning to support

Nathan Blake1, Riana Gaifulina2, Martin Isabelle3

  • 1Department of Cell and Developmental Biology, University College London, Gower Street, London, WC1E 6BT, UK. nathan.blake.15@ucl.ac.uk.

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Raman spectroscopy shows promise for oncology diagnostics. This study demonstrates that using identical spectrometers and protocols across multiple centers ensures consistent diagnostic accuracy without needing complex data correction methods.

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Area of Science:

  • Biomedical Optics
  • Spectroscopic Diagnostics
  • Oncology

Background:

  • Raman spectroscopy has significant clinical potential in oncology.
  • Widespread adoption is hindered by a lack of evidence for inter-spectrometer data transferability.
  • Consistent diagnostic performance across different instruments is crucial for routine clinical use.

Purpose of the Study:

  • To investigate the multi-center transferability of Raman spectroscopy data for esophageal tissue analysis.
  • To assess if consistent diagnostic performance can be achieved across different centers using identical spectrometers and a common protocol.

Main Methods:

  • Human esophageal tissue samples (n=61 from 51 patients) were analyzed using Raman spectroscopy at three different centers.
  • Spectra were acquired using identical makes and models of spectrometers.
  • A common data acquisition protocol was employed to minimize variability.

Main Results:

  • Raman spectra were classified into one of five pathologies with consistent accuracy and log-loss across centers.
  • Machine learning models trained at one center performed reliably on data from other centers.
  • No computational methods for instrument correction were required during pre-processing.

Conclusions:

  • Multi-center system transferability is achievable with Raman spectroscopy for esophageal tissue analysis.
  • Using the same spectrometer model and a standardized protocol eliminates the need for computational instrument correction.
  • This finding supports the integration of Raman spectroscopy into routine oncology workflows.