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Attenuated total reflection-Fourier transform infrared spectroscopy (ATR-FTIR) offers a cost-effective method for diagnosing digestive tract cancers (DTC). This study developed a machine learning protocol using ATR-FTIR blood serum data to accurately classify DTC and its stages.

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Area of Science:

  • Biomedical Spectroscopy
  • Medical Diagnostics
  • Computational Biology

Background:

  • Attenuated total reflection-Fourier transform infrared spectroscopy (ATR-FTIR) is a cost-effective method for analyzing biomolecular markers in biofluids for disease diagnosis.
  • Accurate and early diagnosis of digestive tract cancers (DTC) remains challenging, requiring methods to detect subtle molecular changes.
  • Understanding the spectral changes in blood serum associated with DTC is crucial for developing effective diagnostic tools.

Purpose of the Study:

  • To develop and validate a diagnostic protocol for digestive tract cancers (DTC) using in-situ blood-based ATR-FTIR spectroscopy.
  • To identify specific infrared molecular fingerprints (IMFs) indicative of DTC and its various pathological stages.
  • To assess the efficacy of machine learning algorithms in classifying DTC based on spectroscopic data.

Main Methods:

  • Collected 252 blood serum samples from patients with liver cancer (LC), gastric cancer (GC), colorectal cancer (CC), and healthy controls.
  • Acquired ATR-FTIR spectra and processed them to generate a 2-dimensional second derivative spectrum (2D-SD-IR) feature dataset.
  • Employed Partial Least-Squares Discriminant Analysis (PLS-DA) and backpropagation (BP) neural networks for classification of DTC and pathological stages.

Main Results:

  • The 2D-SD-IR feature dataset effectively captured infrared molecular fingerprints (IMFs) of DTC.
  • PLS-DA and BP neural networks achieved high classification performance, with 100% sensitivity and an average specificity exceeding 95% for differentiating DTC and pathological stages.
  • Mutual validation with clinical blood biochemistry testing confirmed the improved classification performance of the proposed machine learning protocol.

Conclusions:

  • The developed 2D-SD-IR-based machine learning protocol significantly enhances the classification accuracy of digestive tract cancers (DTC) using blood serum ATR-FTIR data.
  • ATR-FTIR spectroscopy, combined with advanced data mining and machine learning, presents a promising approach for the rapid and accurate diagnosis of DTC.
  • This method offers a potential tool for early detection and staging of DTC, complementing existing diagnostic techniques.