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.
Kwek
L C
LC
National Institute of Education, Nanyang Technological University, 1 Nanyang Walk, Singapore 639798. lckwek@nie.edu.sg
Fu
Sheng
S
Chia
T C
TC
Diong
C H
CH
Tang
C L
CL
Krishnan
S M
SM
D000465:Algorithms; D014408:Biomarkers, Tumor / Q000032:analysis*; D015179:Colorectal Neoplasms / Q000145:classification / Q000175:diagnosis* / Q000378:metabolism*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D006801:Humans; D007834:Lasers; D016571:Neural Networks, Computer; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013050:Spectrometry, Fluorescence / Q000379:methods*
D014408:Biomarkers, Tumor