Characterization of Colorectal Adenocarcinoma Sections by Spatially Resolved FT-IR Microspectroscopy

Abstract

A combination of Fourier transform infrared (FT-IR) spectroscopy and microscopy, FT-IR microspectroscopy, has been used to characterize sections of human colorectal adenocarcinoma. In this report, a database of 2601 high quality FT-IR point spectra from 26 patient samples and seven different histological structures was recorded and analyzed. The computer-based analysis of the IR spectra was carried out in four steps: (1) an initial test for spectral quality, (2) data pre-processing, (3) data reduction and feature selection, and (4) classification of the tissue spectra by multivariate pattern recognition techniques such as hierarchical clustering and artificial neural network analysis. Furthermore, an example of how spectral databases can be utilized to reassemble false color images of tissue samples is presented. The overall classification accuracy attained by optimized artificial neural networks reached 95%, highlighting the great potential of FT-IR microspectroscopy as a potentially valuable, reagent-free technique for the characterization of tissue specimens. However, technical improvements and the compilation of validated spectral databases are essential prerequisites to make the infrared technique applicable to routine and experimental clinical analysis.

Fourier transform infrared (FTIR) spectral mapping of the cervical transformation zone, and dysplastic squamous epithelium

“…This confirms the results by Lasch et al [44] who reported patient-to-patient variations smaller than those due to different tissue types and pathological diagnoses. The spectra from areas of CIN had several characteristic features including pronounced symmetric and asymmetric phosphate bands at 1078 and 1240 cm −1 , a significant reduction in glycogen band intensity and a relatively small amide II/amide I ratio.…”

“…In this study, we employ an unsupervised clustering approach to investigate cervical tissue. The unsupervised clustering approach is well described in the literature [43][44][45] and only a brief overview is given below.…”

Spectroscopic characterization of microorganisms by Fourier transform infrared microspectroscopy

“…8 These maps reflect the spatial distribution of functional groups (chemical mapping) or of the specific IR spectroscopic patterns (pattern mapping), and can be directly compared with the histological specimens produced by conventional staining procedures. 9 Although it is generally accepted that IR spectra of biological materials provide characteristic informa-tion of the chemical composition and structure, because of the overlapping absorbance due to multitude of cellular compounds, the bands observed in the mid-IR range are not highly resolved and hence it is very difficult to gain a comprehensive understanding of the biomolecules from the spectral information. A common strategy is to use multivariate data analysis techniques such as factor analysis, cluster analysis, or artificial neural network analysis to study the general pattern hidden in the microbial spectral data to differentiate, identify, and classify microorganisms without a priori information.…”

Infrared spectral histopathology (SHP): a novel diagnostic tool for the accurate classification of lung cancer

“…These studies were preceded by earlier work from the authors' laboratory on the detection of micrometastases in lymph nodes, 12-14 cervical adeno-and SqCCs, 15 and from other laboratories on the detection of colon cancer, 11,[16][17][18] prostate and breast cancer, 19,20 brain cancers and brain metastases, [21][22][23] as well as a few other organs. 9,24 In addition, similar efforts using Raman spectroscopy, another form of vibrational spectroscopy, have yielded analogous results.…”