Multi-class classification algorithm for optical diagnosis of oral cancer.

We report development of a direct multi-class spectroscopic diagnostic algorithm for discrimination of high-grade cancerous tissue sites from low-grade as well as precancerous and normal squamous tissue sites of human oral cavity. The algorithm was developed making use of the recently formulated theory of total principal component regression (TPCR). The in vivo autofluorescence spectral data acquired from patients screened for neoplasm of oral cavity at the Government Cancer Hospital, Indore, was used to train and validate the algorithm. The diagnostic algorithm based on TPCR was found to provide satisfactory performance in classifying the tissue sites in four different classes - high-grade squamous cell carcinoma, low-grade squamous cell carcinoma, leukoplakia, and normal squamous tissue. The classification accuracy for these four classes was observed to be approximately 94%, 100%, 100% and 91% for the training data set (based on leave-one-out cross-validation), and was approximately 90%, 90%, 85% and 88%, respectively for the corresponding classes for the independent validation data set.

[1]  R M Cothren,et al.  Detection of dysplasia at colonoscopy using laser-induced fluorescence: a blinded study. , 1996, Gastrointestinal endoscopy.

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

[3]  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.

[4]  R Marchesini,et al.  Light-induced fluorescence spectroscopy of adenomas, adenocarcinomas and non-neoplastic mucosa in human colon. I. In vitro measurements. , 1992, Journal of photochemistry and photobiology. B, Biology.

[5]  Asima Pradhan,et al.  Wavelet transform of breast tissue fluorescence spectra: a technique for diagnosis of tumors , 2003 .

[6]  Anita Mahadevan-Jansen,et al.  In Vivo Brain Tumor Demarcation Using Optical Spectroscopy¶ , 2001, Photochemistry and photobiology.

[7]  P. Gupta,et al.  Nonlinear pattern recognition for laser‐induced fluorescence diagnosis of cancer , 2003, Lasers in surgery and medicine.

[8]  S. Majumder,et al.  Support vector machine for optical diagnosis of cancer. , 2005, Journal of biomedical optics.

[9]  Andres F. Zuluaga,et al.  Fluorescence Excitation Emission Matrices of Human Tissue: A System for in vivo Measurement and Method of Data Analysis , 1999 .

[10]  H. J. van Staveren,et al.  Classification of clinical autofluorescence spectra of oral leukoplakia using an artificial neural network: a pilot study. , 2000, Oral oncology.

[11]  Xin Yuan,et al.  Classification of in vivo autofluorescence spectra using support vector machines. , 2004, Journal of biomedical optics.

[12]  P. Gupta,et al.  Relevance Vector Machine for Optical Diagnosis of Cancer , 2022 .

[13]  Nirmala Ramanujam,et al.  Comparison of multiexcitation fluorescence and diffuse reflectance spectroscopy for the diagnosis of breast cancer (March 2003) , 2003, IEEE Transactions on Biomedical Engineering.

[14]  George Filippidis,et al.  Artificial neural networks for discriminating pathologic from normal peripheral vascular tissue , 2001, IEEE Trans. Biomed. Eng..

[15]  N. Ramanujam.  Fluorescence spectroscopy of neoplastic and non-neoplastic tissues. , 2000, Neoplasia.

[16]  Weida Tong,et al.  Multi-class cancer classification by total principal component regression (TPCR) using microarray gene expression data , 2005, Nucleic acids research.

[17]  S. Majumder,et al.  Breast cancer diagnosis using N2 laser excited autofluorescence spectroscopy , 1997, Lasers in surgery and medicine.

[18]  G. Pack,et al.  Treatment of cancer and allied diseases , 1940 .

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

[20]  M F Mitchell,et al.  Statistical techniques for diagnosing CIN using fluorescence spectroscopy: SVD and CART , 1995, Journal of cellular biochemistry. Supplement.

[21]  R Richards-Kortum,et al.  Fluorescence spectroscopy for in vivo characterization of ovarian tissue , 2001, Lasers in surgery and medicine.

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

[23]  C T Chen,et al.  A Probability‐based Multivariate Statistical Algorithm for Autofluorescence Spectroscopic Identification of Oral Carcinogenesis , 1999, Photochemistry and photobiology.

[24]  L. Deckelbaum,et al.  Laser-induced fluorescence spectroscopy of human colonic mucosa. Detection of adenomatous transformation. , 1990, Gastroenterology.

[25]  A pilot study on the use of autofluorescence spectroscopy for diagnosis of the cancer of human oral cavity , 2000 .

[26]  I Itzkan,et al.  Early diagnosis of upper aerodigestive tract cancer by autofluorescence. , 1996, Archives of otolaryngology--head & neck surgery.

[27]  R. Richards-Kortum,et al.  Optimal Excitation Wavelengths for In Vivo Detection of Oral Neoplasia Using Fluorescence Spectroscopy¶ , 2000, Photochemistry and photobiology.

[28]  S. Majumder,et al.  N2 laser excited autofluorescence spectroscopy of formalin-fixed human breast tissue. , 2005, Journal of photochemistry and photobiology. B, Biology.

[29]  D. J. Hand,et al.  Recent advances in error rate estimation , 1986, Pattern Recognit. Lett..

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

[31]  Kagan Tumer,et al.  Ensembles of radial basis function networks for spectroscopic detection of cervical precancer , 1998, IEEE Transactions on Biomedical Engineering.