High-sensitivity and specificity of laser-induced autofluorescence spectra for detection of colorectal cancer with an artificial neural network.

An artificial neural network (ANN) has been used in various clinical research for the prediction and classification of data in cancer disease. Previous research in this direction focused on the correlation between various input parameters such as age, antigen, and size of tumor growth. Recently, laser-induced autofluorescence (LIAF) techniques have been shown to be a useful noninvasive early diagnostic tool for various cancer diseases. We report on a successful application of ANN to in vitro LIAF spectra. We show that classification of tumor samples with ANN can be done with high sensitivity, specificity, and accuracy. Thus a combination of LIAF techniques and ANN can provide a robust method for clinical diagnosis.

[1]  C. Mello-Thoms,et al.  The perception of breast cancers-a spatial frequency analysis of what differentiates missed from reported cancers , 2003, IEEE Transactions on Medical Imaging.

[2]  Hussein A. Abbass,et al.  An evolutionary artificial neural networks approach for breast cancer diagnosis , 2002, Artif. Intell. Medicine.

[3]  Dietmar Schnorr,et al.  An artificial neural network considerably improves the diagnostic power of percent free prostate‐specific antigen in prostate cancer diagnosis: Results of a 5‐year investigation , 2002, International journal of cancer.

[4]  E. Sevick-Muraca,et al.  Quantitative optical spectroscopy for tissue diagnosis. , 1996, Annual review of physical chemistry.

[5]  S. Shapshay,et al.  Detection of preinvasive cancer cells , 2000, Nature.

[6]  Klaus Jung,et al.  Multicenter evaluation of an artificial neural network to increase the prostate cancer detection rate and reduce unnecessary biopsies. , 2002, Clinical chemistry.

[7]  N. Nishioka,et al.  Identification of Colonic Dysplasia and Neoplasia by Diffuse Reflectance Spectroscopy and Pattern Recognition Techniques , 1998 .

[8]  E Biganzoli,et al.  Prognosis in node-negative primary breast cancer: a neural network analysis of risk profiles using routinely assessed factors. , 2003, Annals of oncology : official journal of the European Society for Medical Oncology.

[9]  Haishan Zeng,et al.  Real‐time endoscopic fluorescence imaging for early cancer detection in the gastrointestinal tract , 1998 .

[10]  N. Nishioka,et al.  Colonic polyp differentiation using time-resolved autofluorescence spectroscopy. , 1998, Gastrointestinal endoscopy.

[11]  H Stepp,et al.  Fluorescence Endoscopy of Gastrointestinal Diseases: Basic Principles, Techniques, and Clinical Experience , 1998, Endoscopy.

[12]  Mesut Remzi,et al.  Novel artificial neural network for early detection of prostate cancer. , 2002, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[13]  T. Watkin,et al.  THE STATISTICAL-MECHANICS OF LEARNING A RULE , 1993 .

[14]  K. Chia,et al.  Population‐based survival analysis of colorectal cancer patients in Singapore, 1968–1992 , 2002, International journal of cancer.

[15]  W. Pitts,et al.  A Logical Calculus of the Ideas Immanent in Nervous Activity (1943) , 2021, Ideas That Created the Future.

[16]  J. Mourant,et al.  Elastic scattering spectroscopy as a diagnostic tool for differentiating pathologies in the gastrointestinal tract: preliminary testing. , 1996, Journal of biomedical optics.

[17]  Brian C. Wilson,et al.  Recent Advances in Light‐Induced Fluorescence Endoscopy (LIFE) of the Gastrointestinal Tract , 1999 .

[18]  T J Flotte,et al.  Ultraviolet laser‐induced fluorescence of colonic tissue: Basic biology and diagnostic potential , 1992, Lasers in surgery and medicine.

[19]  A. Partin,et al.  A neurocomputational model for prostate carcinoma detection , 2003, Cancer.

[20]  G. Zonios,et al.  Diffuse reflectance spectroscopy of human adenomatous colon polyps in vivo. , 1999, Applied optics.

[21]  R J Ott,et al.  Classification of reflectance spectra from pigmented skin lesions, a comparison of multivariate discriminant analysis and artificial neural networks , 2000, Physics in medicine and biology.

[22]  B. Wilson,et al.  In Vivo Fluorescence Spectroscopy and Imaging for Oncological Applications , 1998, Photochemistry and photobiology.

[23]  Gregg Staerkel,et al.  Cervical Precancer Detection Using a Multivariate Statistical Algorithm Based on Laser‐Induced Fluorescence Spectra at Multiple Excitation Wavelengths , 1996, Photochemistry and photobiology.

[24]  S. Argov,et al.  Diagnostic potential of Fourier-transform infrared microspectroscopy and advanced computational methods in colon cancer patients. , 2002, Journal of biomedical optics.

[25]  I Itzkan,et al.  In vivo identification of colonic dysplasia using fluorescence endoscopic imaging. , 1999, Gastrointestinal endoscopy.

[26]  Cheong Hoong Diong,et al.  Changes in in-vivo autofluorescence spectra at different periods in rat colorectal tumor progression , 2001, European Conference on Biomedical Optics.

[27]  R. Alfano,et al.  Laser induced fluorescence spectroscopy from native cancerous and normal tissue , 1984 .