Diagnostic potential of Fourier-transform infrared microspectroscopy and advanced computational methods in colon cancer patients

Shmuel Argov, Jagannathan Ramesh, Ahmad Salman, Igor Sinelnikov, Jed Goldstein, Hugo Guterman, Shaul Mordechai

Research output: Contribution to journalArticlepeer-review

142 Scopus citations

Abstract

Colon cancer is the third leading class of cancer causing increased mortality in developed countries. A polyp is one type of lesion observed in a majority of colon cancer patients. Here, we report a microscopic Fourier transform infrared (FTIR) study of normal, adenomatous polyp and malignant cells from biopsies of 24 patients. The goal of our study was to differentiate an adenomatous polyp from a malignant cell using FTIR microspectroscopy and artificial neural network (ANN) analysis. FTIR spectra and biological markers such as phosphate, RNA/DNA derived from spectra, were useful in identifying normal cells from abnormal ones that consisted of adenomatous polyp and malignant cells. However, the biological markers failed to differentiate between adenomatous polyp and malignant cases. By employing a combination of wavelet features and an ANN based classifier, we were able to classify the different cells as normal, adenomatous polyp and cancerous in a given tissue sample. The percentage of success of classification was 89%, 81%, and 83% for normal, adenomatous polyp, and malignant cells, respectively. A comparison of the method proposed with the pathological method is also discussed.

Original languageEnglish
Pages (from-to)248-254
Number of pages7
JournalJournal of Biomedical Optics
Volume7
Issue number2
DOIs
StatePublished - 1 Apr 2002

Keywords

  • Artificial neural network
  • Colon cancer
  • Diagnosis
  • Fourier transform infrared microspectroscopy

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Biomaterials
  • Atomic and Molecular Physics, and Optics
  • Biomedical Engineering

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