Near-Infrared Spectroscopy as a Diagnostic Tool for Distinguishing between Normal and Malignant Colorectal Tissues.

Cancer diagnosis is one of the most important tasks of biomedical research and has become the main objective of medical investigations. The present paper proposed an analytical strategy for distinguishing between normal and malignant colorectal tissues by combining the use of near-infrared (NIR) spe...

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Publicado en:BioMed Research International Vol. 2015; pp. 1 - 8
Autores principales: Chen, Hui, Lin, Zan, Mo, Lin, Wu, Tong, Tan, Chao
Formato: equations & formulas research tables/charts Journal Article
Publicado: Wiley-Blackwell 1/13/2015
Acceso en línea:Ver este registro en EBSCOhost
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      dt: 1/13/2015
      vid: 2015
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      pub: Wiley-Blackwell
      place: Malden, Massachusetts
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        10.1155/2015/472197
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        atl: Near-Infrared Spectroscopy as a Diagnostic Tool for Distinguishing between Normal and Malignant Colorectal Tissues.
      aug:
        au:
          Chen, Hui
          Lin, Zan
          Mo, Lin
          Wu, Tong
          Tan, Chao
        affil: Yibin University Hospital, Yibin, Sichuan 644000, China
      sug:
        subj:
          Technology, Medical
          Spectrophotometry, Infrared
          Colorectal Neoplasms Diagnosis
          Tissue and Organ Harvesting
          Biomedical Engineering
          Human
          Discriminant Analysis
          Funding Source
      ab: Cancer diagnosis is one of the most important tasks of biomedical research and has become the main objective of medical investigations. The present paper proposed an analytical strategy for distinguishing between normal and malignant colorectal tissues by combining the use of near-infrared (NIR) spectroscopy with chemometrics. The successive projection algorithm-linear discriminant analysis (SPA-LDA) was used to seek a reduced subset of variables/wavenumbers and build a diagnostic model of LDA. For comparison, the partial least squares-discriminant analysis (PLS-DA) based on full-spectrum classification was also used as the reference. Principal component analysis (PCA) was used for a preliminary analysis. A total of 186 spectra from 20 patients with partial colorectal resection were collected and divided into three subsets for training, optimizing, and testing the model. The results showed that, compared to PLS-DA, SPA-LDA provided more parsimonious model using only three wavenumbers/variables (4065, 4173, and 5758 cm−1) to achieve the sensitivity of 84.6%, 92.3%, and 92.3% for the training, validation, and test sets, respectively, and the specificity of 100% for each subset. It indicated that the combination of NIR spectroscopy and SPA-LDA algorithm can serve as a potential tool for distinguishing between normal and malignant colorectal tissues.
      pubtype: Academic Journal
      doctype:
        equations & formulas
        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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