Nonlinear pattern recognition for laser-induced fluorescence diagnosis of cancer

Abstract

Background and Objectives: Use of laser induced fluorescence (LIF) spectroscopy for the diagnosis of cancer requires an appropriate diagnostic algorithm for spectral pattern recognition. While most of the diagnostic algorithms reported in the literature use standard linear feature extraction techniques like principal component analysis (PCA), partial least square (PLS) analysis etc., use of nonlinear techniques is expected to provide improved discrimination. We report here the performance of an algorithm based on nonlinear Maximum Representation and Discrimination Feature (MRDF) method for diagnosis of early stage cancer of human oral cavity. The diagnostic efficacy of the algorithm has been compared with a linear PCA based algorithm. Study Design/Materials and Methods: The diagnostic algorithms were developed based on spectral data acquired in an in-vivo LIF study, at the outpatient department (OPD) of the Government Cancer Hospital, Indore, involving 16 patients with cancer of oral cavity and 13 normal volunteers with healthy oral cavity. In-vivo autofluorescence spectra were recorded using a N₂ laser based portable fluorimeter. The patients had no prior confirmed malignancy, were suspected on visual examination of having early cancer of the oral cavity and were diagnosed of squamous cell carcinoma (SCC) on the basis of histopathology of biopsy taken from abnormal site subsequent to acquisition of spectra. The spectra were acquired from a total of 171 tissue sites from patients, of which 83 were from SCC and 88 were from uninvolved squamous tissue, and 154 sites from healthy squamous tissue from normal volunteers. In each patient, the normal tissue sites interrogated were from the adjacent apparently uninvolved region of the oral cavity. Each site was treated separately and classified via the diagnostic algorithm developed. Instead of the spectral data from uninvolved sites of patients, the data from normal volunteers were used as the normal database for the development of diagnostic algorithms. Results: The nonlinear diagnostic algorithm based on MRDF provided a sensitivity of 93% and a specificity of 96% towards cancer for the training set data and a sensitivity of 95% and a specificity of 96% towards cancer for the validation set data. When implemented on the spectral data of the uninvolved oral cavity sites from the patients it yielded a specificity of 96%. On the other hand, the linear PCA based algorithm provided a sensitivity of 83% and a specificity of 66% towards cancer for the training set data and a sensitivity of 80% and a specificity of 58% towards cancer for the validation set data. When spectral data of the uninvolved oral cavity sites from the patients were considered as the unknown data set, it resulted in a specificity value of 56%. Conclusions: The nonlinear MRDF algorithm provided significantly improved diagnostic performance as compared to the linear PCA based algorithm in discriminating the cancerous tissue sites of the oral cancer patients from the healthy squamous tissue sites of normal volunteers as well as the uninvolved tissue sites of the oral cavity of the patients with cancer.