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Background and Objectives: Nonmelanoma skin cancers, including basal cell carcinoma (BCC) and squamous cell carcinoma (SCC), are the most common skin cancers, presenting nearly as many cases as all other cancers combined. The current gold-standard for clinical diagnosis of these lesions is histopathologic examination, an invasive, time-consuming procedure. There is thus considerable interest in developing a real-time, automated, noninvasive tool for nonmelanoma skin cancer diagnosis. In this study, we explored the capability of Raman microspectroscopy to provide differential diagnosis of BCC, SCC, inflamed scar tissue, and normal tissue in vivo., Study Design: Based on the results of previous in vitro studies, we developed a portable confocal Raman system with a handheld probe for clinical study. Using this portable system, we measured Raman spectra of 21 suspected nonmelanoma skin cancers in 19 patients with matched normal skin spectra. These spectra were input into nonlinear diagnostic algorithms to predict pathological designation., Results: All of the BCC (9/9), SCC (4/4), and inflamed scar tissues (8/8) were correctly predicted by the diagnostic algorithm, and 19 out of 21 normal tissues were correctly classified. This translates into a 100% (21/21) sensitivity and 91% (19/21) specificity for abnormality, with a 95% (40/42) overall classification accuracy., Conclusions: These findings reveal Raman microspectroscopy to be a viable tool for real-time diagnosis and guidance of nonmelanoma skin cancer resection.

Background and Objectives: A probability-based, robust diagnostic algorithm is an essential requirement for successful clinical use of optical spectroscopy for cancer diagnosis. This study reports the use of the theory of relevance vector machine (RVM), a recent Bayesian machine-learning framework of statistical pattern recognition, for development of a fully probabilistic algorithm for autofluorescence diagnosis of early stage cancer of human oral cavity. It also presents a comparative evaluation of the diagnostic efficacy of the RVM algorithm with that based on support vector machine (SVM) that has recently received considerable attention for this purpose., Study Design/materials and Methods: The diagnostic algorithms were developed using in vivo autofluorescence spectral data acquired from human oral cavity with a N(2) laser-based portable fluorimeter. The spectral data of both patients as well as normal volunteers, enrolled at Out Patient department of the Govt. Cancer Hospital, Indore for screening of oral cavity, were used for this purpose. The patients selected had no prior confirmed malignancy and were diagnosed of squamous cell carcinoma (SCC), Grade-I on the basis of histopathology of biopsy taken from abnormal site subsequent to acquisition of spectra. Autofluorescence spectra were recorded from a total of 171 tissue sites from 16 patients and 154 healthy squamous tissue sites from 13 normal volunteers. Of 171 tissues sites from patients, 83 were SCC and the rest were contralateral uninvolved squamous tissue. 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 diagnostic algorithms based on RVM were found to provide classification performance comparable to the state-of-the-art SVMs, while at the same time explicitly predicting the probability of class membership. The sensitivity and specificity towards cancer were up to 88% and 95% for the training set data based on leave- one-out cross validation and up to 91% and 96% 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 up to 91%., Conclusions: The Bayesian framework of RVM formulation makes it possible to predict the posterior probability of class membership in discriminating early SCC from the normal squamous tissue sites of the oral cavity in contrast to dichotomous classification provided by the non-Bayesian SVM. Such classification is very helpful in handling asymmetric misclassification costs like assigning different weights for having a false negative result for identifying cancer compared to false positive. The results further demonstrate that for comparable diagnostic performances, the RVM-based algorithms use significantly fewer kernel functions and do not need to estimate any hoc parameters associated with the learning or the optimization technique to be used. This implies a considerable saving in memory and computation in a practical implementation., (Copyright 2005 Wiley-Liss, Inc.)

We report the application of a support vector machine (SVM) for the development of diagnostic algorithms for optical diagnosis of cancer. Both linear and nonlinear SVMs have been investigated for this purpose. We develop a methodology that makes use of SVM for both feature extraction and classification jointly by integrating the newly developed recursive feature elimination (RFE) in the framework of SVM. This leads to significantly improved classification results compared to those obtained when an independent feature extractor such as principal component analysis (PCA) is used. The integrated SVM-RFE approach is also found to outperform the classification results yielded by traditional Fisher's linear discriminant (FLD)-based algorithms. All the algorithms are developed using spectral data acquired in a clinical in vivo laser-induced fluorescence (LIF) spectroscopic study conducted on patients being screened for cancer of the oral cavity and normal volunteers. The best sensitivity and specificity values provided by the nonlinear SVM-RFE algorithm over the data sets investigated are 95 and 96% toward cancer for the training set data based on leave-one-out cross validation and 93 and 97% toward cancer for the independent validation set data. When tested on the spectral data of the uninvolved oral cavity sites from the patients it yielded a specificity of 85%., (Copyright 2005 Society of Photo-Optical Instrumentation Engineers.)

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 N(2) 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., (Copyright 2003 Wiley-Liss, Inc.)