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Identification of colitis and cancer in colon biopsies by fourier transform infrared spectroscopy and chemometrics

  • Xiang Li
  • , Qing Bo Li
  • , Guang Jun Zhang*
  • , Yi Zhuang Xu
  • , Xue Jun Sun
  • , Jing Sen Shi
  • , Yuan Fu Zhang
  • , Jin Guang Wu
  • *Corresponding author for this work
  • Ministry of Education's Key Laboratory of Precision Opto-mechatronics Technology
  • Beihang University
  • Peking University
  • Peking University
  • Department of General Surgery
  • Xi'an Jiaotong University

Research output: Contribution to journal › Article › peer-review

Abstract

Cancer is a disease that does great harms to the health of human beings. FT-IR spectroscopy could identify variability at the molecular level in biological specimens. It is a rapid and noninvasive method, which could be used intraoperatively to modify surgical procedures. The aim of this paper is to identify and separate cancer from colitis in endoscopic colon biopsies through the use of FT-IR spectroscopy. A total of 88 endoscopic colon samples, including 41 cases of colitis and 47 cases of colon cancer, were obtained. Specimens were placed on an ATR accessory linked to FT-IR spectrometer with a MCT detector for greater stability and sensitivity. Later, specimens were sent for the histological examination as the reference in the spectral analysis. 41 colitis and 47 cancer specimens were compared. Spectra preprocessed with smoothing and normalization were used for discrimination analysis. PCA was processed to simplify the spectrum data set. Naive Bayes classifier model was constructed for diagnostic classification. Leave-one-out cross-validation method was utilized to assess the discrimination results. The sensitivity of FT-IR detection for cancer achieves 97.6%. The results showed that colon cancer could be distinguished from colitis with high accuracy using FT-IR spectroscopy and chemometrics.

Original languageEnglish
Article number936149
JournalThe Scientific World Journal
Volume2012
DOIs
StatePublished - 2012

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

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