Raman Spectroscopy in Combination with Background Near-infrared Autofluorescence Enhances the In Vivo Assessment of Malignant Tissues

Authors

  • Zhiwei Huang,

    1. Cancer Imaging Department, British Columbia Cancer Research Centre, Vancouver, BC, Canada
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  • Harvey Lui,

    1. Division of Dermatology, University of British Columbia and Vancouver Coastal Health Research Institute, Vancouver, BC, Canada
    2. Skin Cancer Research Group, British Columbia Cancer Agency, Vancouver, BC, Canada
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  • David I. McLean,

    1. Division of Dermatology, University of British Columbia and Vancouver Coastal Health Research Institute, Vancouver, BC, Canada
    2. Skin Cancer Research Group, British Columbia Cancer Agency, Vancouver, BC, Canada
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  • Mladen Korbelik,

    1. Cancer Imaging Department, British Columbia Cancer Research Centre, Vancouver, BC, Canada
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  • Haishan Zeng

    Corresponding author
    1. Cancer Imaging Department, British Columbia Cancer Research Centre, Vancouver, BC, Canada
    2. Skin Cancer Research Group, British Columbia Cancer Agency, Vancouver, BC, Canada
    • To whom correspondence should be addressed: Cancer Imaging Department, British Columbia Cancer Research Centre, 675 West 10th Avenue, Vancouver, BC, Canada V5Z 1L3. Fax: +1 604 675 8099; e-mail: hzeng@bccrc.ca

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ABSTRACT

The diagnostic ability of optical spectroscopy techniques, including near-infrared (NIR) Raman spectroscopy, NIR autofluorescence spectroscopy and the composite Raman and NIR autofluorescence spectroscopy, for in vivo detection of malignant tumors was evaluated in this study. A murine tumor model, in which BALB/c mice were implanted with Meth-A fibrosarcoma cells into the subcutaneous region of the lower back, was used for this purpose. A rapid-acquisition dispersive-type NIR Raman system was employed for tissue Raman and NIR autofluorescence spectroscopic measurements at 785-nm laser excitation. High-quality in vivo NIR Raman spectra associated with an autofluorescence background from mouse skin and tumor tissue were acquired in 5 s. Multivariate statistical techniques, including principal component analysis (PCA) and linear discriminant analysis (LDA), were used to develop diagnostic algorithms for differentiating tumors from normal tissue based on their spectral features. Spectral classification of tumor tissue was tested using a leave-one-out, cross-validation method, and the receiver operating characteristic (ROC) curves were used to further evaluate the performance of diagnostic algorithms derived. Thirty-two in vivo Raman, NIR fluorescence and composite Raman and NIR fluorescence spectra were analyzed (16 normal, 16 tumors). Classification results obtained from cross-validation of the LDA model based on the three spectral data sets showed diagnostic sensitivities of 81.3%, 93.8% and 93.8%; specificities of 100%, 87.5% and 100%; and overall diagnostic accuracies of 90.6%, 90.6% and 96.9% respectively, for tumor identification. ROC curves showed that the most effective diagnostic algorithms were from the composite Raman and NIR autofluorescence techniques.

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