Distinction of cervical cancer biopsies by use of infrared microspectroscopy and probabilistic neural networks

A. Podshyvalov, R. K. Sahu, S. Mark, K. Kantarovich, H. Guterman, J. Goldstein, R. Jagannathan, S. Argov, S. Mordechai

Research output: Contribution to journalArticlepeer-review

50 Scopus citations

Abstract

Fourier-transform infrared spectroscopy has shown alterations of spectral characteristics of cells and tissues as a result of carcinogenesis. The research reported here focuses on the diagnosis of cancer in formalin-fixed biopsied tissue for which immunochemistry is not possible and when PAP-smear results are to be confirmed. The data from two groups of patients (a control group and a group of patients diagnosed with cervical cancer) were analyzed. It was found that the glucose/phosphate ratio decreases (by 23-49%) and the RNA/DNA ratio increases (by 38-150%) in carcinogenic compared with normal tissue. Fourier-transform microspectroscopy was used to examine these tissues. This type of study in larger populations may help to set standards or classes with which to use treated biopsied tissue to predict the possibility of cancer. Probabilistic neural networks and statistical tests as parts of these biopsies predict the possibility of cancer with a high degree of accuracy (>95%).

Original languageEnglish
Pages (from-to)3725-3734
Number of pages10
JournalApplied Optics
Volume44
Issue number18
DOIs
StatePublished - 20 Jun 2005

ASJC Scopus subject areas

  • Atomic and Molecular Physics, and Optics
  • Engineering (miscellaneous)
  • Electrical and Electronic Engineering

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