Classification of Laser Induced Fluorescence spectra from normal and malignant tissues using Learning Vector Quantization neural network in bladder cancer diagnosis

In the present work we discuss the potential of recently developed classification algorithm, learning vector quantization (LVQ), for the analysis of laser induced fluorescence (LIF) spectra, recorded from normal and malignant bladder tissue samples. The algorithm is prototype based and inherently regularizing, which is desirable, for the LIF spectra because of its high dimensionality and features being settled at widely spaced intervals (sparseness). We discuss the effect of different parameters influencing the performance of LVQ in LIF data classification. Further, we compare and cross validate the classification accuracy of LVQ with other classifiers (eg. SVM and multi layer perception) for the same data set. Good agreement has been obtained between LVQ based classification of spectroscopy data and histopathology results which demonstrate the use of LVQ classifier in bladder cancer diagnosis.

[1]  Hyeoncheol Kim,et al.  Feature Selection using Multi-Layer Perceptron in HIV-1 Protease Cleavage Data , 2008, 2008 International Conference on BioMedical Engineering and Informatics.

[2]  Y. Yasui,et al.  An Automated Peak Identification/Calibration Procedure for High-Dimensional Protein Measures From Mass Spectrometers , 2003, Journal of biomedicine & biotechnology.

[3]  Thomas Villmann,et al.  Classification of mass-spectrometric data in clinical proteomics using learning vector quantization methods , 2007, Briefings Bioinform..

[4]  C. Marsit,et al.  Promoter hypermethylation is associated with current smoking, age, gender and survival in bladder cancer. , 2007, Carcinogenesis.

[5]  John N. Tsitsiklis,et al.  Neuro-dynamic programming: an overview , 1995, Proceedings of 1995 34th IEEE Conference on Decision and Control.

[6]  E. Petricoin,et al.  Use of proteomic patterns in serum to identify ovarian cancer , 2002, The Lancet.

[7]  Cesare Furlanello,et al.  Machine learning methods for predictive proteomics , 2007, Briefings Bioinform..

[8]  Ofer Nativ,et al.  Detection of bladder cancer in human urine by metabolomic profiling using high performance liquid chromatography/mass spectrometry. , 2008, The Journal of urology.

[9]  Olli Simula,et al.  A learning vector quantization algorithm for probabilistic models , 2000, 2000 10th European Signal Processing Conference.

[10]  N. Cristianini.  Kernel Methods (Kernel‐Based Learning Methods, Kernel Machine) , 2004 .

[11]  Pierangelo Veltri.  Algorithms and tools for analysis and management of mass spectrometry data , 2008, Briefings Bioinform..

[12]  S. Sumathi,et al.  Introduction to neural networks using MATLAB 6.0 , 2006 .

[13]  Beat Kleiner,et al.  Graphical Methods for Data Analysis , 1983 .

[14]  J. Platt.  Sequential Minimal Optimization : A Fast Algorithm for Training Support Vector Machines , 1998 .

[15]  Teuvo Kohonen,et al.  Self-Organizing Maps , 2010 .

[16]  Michel Verleysen,et al.  The Curse of Dimensionality in Data Mining and Time Series Prediction , 2005, IWANN.

[17]  T. Breslin,et al.  Diagnosing Breast Cancer by Using Raman Spectroscopy , 2006 .

[18]  A. Pothen,et al.  Protocols for disease classification from mass spectrometry data , 2003, Proteomics.

[19]  David Haussler,et al.  Exploiting Generative Models in Discriminative Classifiers , 1998, NIPS.

[20]  T. Ørntoft,et al.  Proteome profiling of bladder squamous cell carcinomas: identification of markers that define their degree of differentiation. , 1997, Cancer research.

[21]  D. Michaud,et al.  Hormonal and reproductive factors and the risk of bladder cancer in women. , 2006, American journal of epidemiology.

[22]  Robert A. Lordo,et al.  Learning from Data: Concepts, Theory, and Methods , 2001, Technometrics.

[23]  Emanuel F. Petricoin,et al.  High-resolution serum proteomic features for ovarian cancer detection. , 2004 .

[24]  Jorma Laaksonen,et al.  LVQ_PAK: The Learning Vector Quantization Program Package , 1996 .

[25]  C Murali Krishna,et al.  Autofluorescence of oral tissue for optical pathology in oral malignancy. , 2004, Journal of photochemistry and photobiology. B, Biology.

[26]  A. Gazdar,et al.  Filling the void: urinary markers for bladder cancer risk and diagnosis. , 2001, Journal of the National Cancer Institute.

[27]  E. Messing,et al.  Promotion of bladder cancer development and progression by androgen receptor signals. , 2007, Journal of the National Cancer Institute.

[28]  M. Spitz,et al.  Roles of tumor suppressor and telomere maintenance genes in cancer and aging--an epidemiological study. , 2005, Carcinogenesis.

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

[30]  J. Roodenburg,et al.  Autofluorescence characteristics of healthy oral mucosa at different anatomical sites , 2003, Lasers in surgery and medicine.

[31]  J. Petrik,et al.  National Academy of Clinical Biochemistry Guidelines for the Use of Tumor Markers in Breast Cancer , 2007 .

[32]  K. Schomacker,et al.  Laser induced autofluorescence diagnosis of bladder cancer. , 1996, The Journal of urology.

[33]  John A. Byers,et al.  Dirichlet tessellation of bark beetle spatial attack points. , 1992 .

[34]  David Ward,et al.  Comparison of statistical methods for classification of ovarian cancer using mass spectrometry data , 2003, Bioinform..

[35]  Manfred Glesner,et al.  Is LVQ really good for classification?-an interesting alternative , 1993, IEEE International Conference on Neural Networks.

[36]  Chunsheng Jiang,et al.  Novel optical detection system for in vivo identification and localization of cervical intraepithelial neoplasia. , 2006, Journal of biomedical optics.

[37]  S. Goodison,et al.  Bladder cancer associated glycoprotein signatures revealed by urinary proteomic profiling. , 2007, Journal of proteome research.

[38]  R. Richards-Kortum,et al.  Raman spectroscopy for the detection of cancers and precancers. , 1996, Journal of biomedical optics.

[39]  Peter J. Halls,et al.  Dirichlet neighbours: revisiting Dirichlet tessellation for neighbourhood analysis , 2001 .

[40]  A. Vlahou,et al.  Challenges of using mass spectrometry as a bladder cancer biomarker discovery platform , 2008, World Journal of Urology.

[41]  G. Wright,et al.  Development of a novel proteomic approach for the detection of transitional cell carcinoma of the bladder in urine. , 2001, The American journal of pathology.