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Photo-stability of urine is of crucial importance for the applicability of fluorescence spectroscopy of urine samples for diagnosis of cancer. We report the results of a detailed study on fluorescence photo-bleaching of human urine samples. We also present the results of a preliminary investigation on evaluation of the applicability of photo-bleaching characteristics of urine for discriminating patients with oral cancer from healthy volunteers. The time-lapse fluorescence induced by continuous shining of 405 nm radiation from a diode laser was recorded from the urine samples obtained from 18 patients with oral cancer as well as from 22 healthy volunteers with history of no known major illness in the past two months. The integrated fluorescence intensity (ΣI), calculated for each spectrum, was found to decrease with time till a point after which no further decrease was observed. Further, while significant differences were observed in the spectra of cancerous patients and healthy volunteers, these differences were found to be varying with time till the intensities of the observed fluorescence spectra corresponding to the two categories of urine samples became stable. The curve, generated by plotting ΣI vs. time, was found to be best fitted (R 2  > 0.95) with a double-exponential decay function. The photo-bleaching constants, obtained from curve-fitting, were found to have statistically significant differences corresponding to the urine samples of cancerous patients and healthy volunteers. A classification algorithm developed based on nearest-mean classifier (NMC) and applied on the photo-bleaching constants in leave-one-subject-out cross-validation mode was found to provide a sensitivity and specificity of up to ∼ 86% in discriminating the two categories of urine samples., (Copyright © 2019 Elsevier B.V. All rights reserved.)

Background: We report the results of a clinical in vivo study to evaluate the potential of fluorescence spectroscopy for differential diagnosis of oral mucosal malignant and potentially malignant lesions., Materials and Methods: The study involved 26 healthy volunteers and 144 patients enrolled for routine medical examination of the oral cavity at the outpatient department of the Tata Memorial Hospital, Mumbai. In vivo autofluorescence spectra were recorded using a N2 laser based portable fluorimeter developed in-house. The different tissue sites investigated belonged to either of the four histopathologic categories: 1) squamous cell carcinoma (SCC), 2) oral sub-mucous fibrosis (OSMF), 3) leukoplakia (LP) and 4) normal squamous tissue. A multivariate statistical algorithm capable of direct multi-class classification was used to predict pathological designations., Results: With respect to histopathology as the "gold standard", the diagnostic algorithm was found to provide an accuracy of 82, 76, 81 and 85% based on leave-one-patient-out cross-validation in classifying the oral tissue spectra into four different pathology classes - SCC, OSMF, LP, and normal squamous tissue - respectively. When the algorithm was employed for delineating the normal oral tissues from all the abnormal oral tissues including SCC, OSMF and LP put together, a sensitivity of 98% and a specificity of 100% were obtained., Conclusion: The results suggest that it is possible to objectively classify the oral tissue into different pathology classes based on their in vivo autofluorescence spectra. Thus, the technique can potentially improve oral screening efforts in low resource settings where clinical expertise and resources are limited.

Background and Objective: Most women with early stage breast cancer have the option of breast conserving therapy, which involves a partial mastectomy for removal of the primary tumor, usually followed by radiotherapy. The presence of tumor at or near the margin is strongly correlated with the risk of local tumor recurrence, so there is a need for a non-invasive, real-time tool to evaluate margin status. This study examined the use of autofluorescence and diffuse reflectance spectroscopy and spectral imaging to evaluate margin status intraoperatively., Materials and Methods: Spectral measurements were taken from the surface of the tissue mass immediately following removal during partial mastectomies and/or from tissues immediately after sectioning by surgical pathology. A total of 145 normal spectra were obtained from 28 patients, and 34 tumor spectra were obtained from 12 patients., Results: After correlation with histopathology, a multivariate statistical algorithm classified the spectra as normal (negative margins) or tumor (positive margins) with 85% sensitivity and 96% specificity. A separate algorithm achieved 100% classification between neo-adjuvant chemotherapy-treated tissues and non-treated tissues. Fluorescence and reflectance-based spectral images were able to demarcate a calcified lesion on the surface of a resected specimen as well., Conclusion: Fluorescence and reflectance spectroscopy could be a valuable tool for examining the superficial margin status of excised breast tumor specimens, particularly in the form of spectral imaging to examine entire margins in a single acquisition.

For a given diagnostic problem, important considerations are the relative performances of the various optical biopsy techniques. A comparative evaluation of fluorescence, diffuse reflectance, combined fluorescence and diffuse reflectance, and Raman spectroscopy in discriminating different histopathologic categories of human breast tissues is reported. Optical spectra were acquired ex vivo from a total of 74 breast tissue samples belonging to 4 distinct histopathologic categories: invasive ductal carcinoma (IDC), ductal carcinoma in situ (DCIS), fibroadenoma (FA), and normal breast tissue. A probability-based multivariate statistical algorithm capable of direct multiclass classification was developed to analyze the diagnostic content of the spectra measured from the same set of breast tissue sites with these different techniques. The algorithm uses the theory of nonlinear maximum representation and discrimination feature for feature extraction, and the theory of sparse multinomial logistic regression for classification. The results reveal that the performance of Raman spectroscopy is superior to that of all others in classifying the breast tissues into respective histopathologic categories. The best classification accuracy was observed to be approximately 99%, 94%, 98%, and 100% for IDC, DCIS, FA, and normal breast tissues, respectively, on the basis of leave-one-sample-out cross-validation, with an overall accuracy of approximately 99%.

This paper reports the development of a probability-based spectroscopic diagnostic algorithm capable of simultaneously discriminating tumor core and tumor margins from normal human brain tissues. The algorithm uses a nonlinear method for feature extraction based on maximum representation and discrimination feature (MRDF) and a Bayesian method for classification based on sparse multinomial logistic regression (SMLR). Both the autofluorescence and the diffuse-reflectance spectra acquired in vivo from patients undergoing craniotomy or temporal lobectomy at the Vanderbilt University Medical Center were used to train and validate the algorithm. The classification accuracy was observed to be approximately 96%, 80%, and 97% for the tumor, tumor margin, and normal brain tissues, respectively, for the training data set and approximately 96%, 94%, and 100%, respectively, for the corresponding tissue types in an independent validation data set. The inherently multi-class nature of the algorithm facilitates a rapid and simultaneous classification of tissue spectra into various tissue categories without the need for a hierarchical multi-step binary classification scheme. Further, the probabilistic nature of the algorithm makes it possible to quantitatively assess the certainty of the classification and recheck the samples that are classified with higher relative uncertainty.

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.)

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.)