Wavelet scattering transform and entropy features in fluorescence spectral signal analysis for cervical cancer diagnosis.

Cervical cancer is a prevalent malignant tumor within the female reproductive system and is regarded as a prominent cause of female mortality on a global scale. Timely and precise detection of various phases of cervical cancer holds the potential to substantially enhance both the rate of successful treatment and the duration of patient survival. Fluorescence spectroscopy is a highly sensitive method for detecting the biochemical changes that arise during cancer progression. In our study, fluorescence spectral data is collected from a diverse group of 110 subjects. The potential of the scattering transform technique for the purpose of cancer detection is explored. The processed signal undergoes an initial decomposition into scattering coefficients using the wavelet scattering transform (WST). Subsequently, the scattering coefficients are subjected to computation for fuzzy entropy, dispersion entropy, phase entropy, and spectral entropy, for effectively characterizing the fluorescence spectral signals. These combined features generated through the proposed approach are then fed to 1D convolutional neural network (CNN) classifier to classify them into normal, pre-cancerous, and cancerous categories, thereby evaluating the effectiveness of the proposed methodology. We obtained mean classification accuracy of 97% using 5-fold cross-validation. This demonstrates the potential of combining WST and entropic features for analyzing fluorescence spectroscopy signals using 1D CNN classifier that enables early cancer detection in contrast to prevailing diagnostic methods.

A smartphone-based standalone fluorescence spectroscopy tool for cervical precancer diagnosis in clinical conditions.

Real-time prediction about the severity of noncommunicable diseases like cancers is a boon for early diagnosis and timely cure. Optical techniques due to their minimally invasive nature provide better alternatives in this context than the conventional techniques. The present study talks about a standalone, field portable smartphone-based device which can classify different grades of cervical cancer on the basis of the spectral differences captured in their intrinsic fluorescence spectra with the help of AI/ML technique. In this study, a total number of 75 patients and volunteers, from hospitals at different geographical locations of India, have been tested and classified with this device. A classification approach employing a hybrid mutual information long short-term memory model has been applied to categorize various subject groups, resulting in an average accuracy, specificity, and sensitivity of 96.56%, 96.76%, and 94.37%, respectively using 10-fold cross-validation. This exploratory study demonstrates the potential of combining smartphone-based technology with fluorescence spectroscopy and artificial intelligence as a diagnostic screening approach which could enhance the detection and screening of cervical cancer.

Mueller matrix-based characterization of cervical tissue sections: a quantitative comparison of polar and differential decomposition methods.

Significance: Quantitative optical polarimetry has received considerable recent attention owing to its potential for being an efficient diagnosis and characterizing tool with potential applications in biomedical research and various other disciplines. In this regard, it is crucial to validate various Mueller matrix (MM) decomposition methods, which are utilized to extract and quantify the intrinsic individual polarization anisotropy properties of various complex optical media. Aim: To quantitatively compare the performance of both polar and differential MM decomposition methods for probing the structural and morphological changes in complex optical media through analyzing their intrinsic individual polarization parameters, which are extracted using the respective decomposition algorithms. We also intend to utilize the decomposition-derived anisotropy parameters to distinguish among the cervical tissues with different grades of cervical intraepithelial neoplasia (CIN) and to characterize the healing efficiency of an organic crystal. Approach: Polarization MM of the cervical tissues with different grades of CIN and the different stages of the self-healing crystal are recorded with a home-built MM imaging setup in the transmission detection geometry with a spatial resolution of ≈400 nm. The measured MMs are then processed with both the polar and differential MM decomposition methods to extract the individual polarization parameters of the respective samples. The derived polarization parameters are further analyzed to validate and compare the performance of both the MM decomposition methods for probing and characterizing the structural changes in the respective investigated optical media through their decomposition-derived intrinsic individual polarization properties. Results: Pronounced differences in the decomposed-derived polarization anisotropy parameters are observed for cervical tissue sections with different grades of CIN. While a significant increase in the depolarization parameter (Δ) is obtained with the increment of CIN stages for both the polar [Δ=0.32 for CIN grade one (CIN-I) and Δ=0.53 for CIN grade two (CIN-II))] and differential (Δ=0.35 for CIN-I and Δ=0.56 for CIN-II) decomposition methods, a trend reversal is seen for the linear diattenuation parameter (dL), indicating the structural distortion in the cervical morphology due to the CIN disease. More importantly, with the differential decomposition algorithm, the magnitude of the derived dL parameter decreases from 0.26 to 0.19 with the progression of CIN, which was not being probed by the polar decomposition method. Conclusion: Our results demonstrate that the differential decomposition of MM holds certain advantages over the polar decomposition method to characterize and probe the structural changes in the cervical tissues with different grades of CIN. Although the quantified individual polarization parameters obtained through both the MM decomposition methods can be used as useful metrics to characterize various optical media, in case of complex turbid media such as biological tissues, incorporation of the differential decomposition technique may yield more efficient information. Also, the study highlights the utilization of MM polarimetry with an appropriate decomposition technique as an efficient diagnostic and characterizing tool in the realm of biomedical clinical research, and various other disciplines.

Detection of oral mucosal lesions by the fluorescence spectroscopy and classification of cancerous stages by support vector machine.

Detection of oral mucosal lesions has been performed by an in-house developed fluorescence-based portable device in the present study. A laser diode of 405 nm wavelength and a UV-visible spectrometer are utilized in the portable device as excitation and detection sources. At the 405 nm excitation wavelength, the flavin adenine dinucleotide (FAD) band at 500 nm and three porphyrin bands at 634, 676, and 703 nm are observed in the fluorescence spectrum of the oral cavity tissue. We have conducted this clinical study on a total of 189 tissue sites of 36 oral squamous cell carcinoma (OSCC) patients, 18 dysplastic (precancerous) patients, and 34 volunteers. Analysis of the fluorescence data has been performed by using the principal component analysis (PCA) method and support vector machine (SVM) classifier. PCA is applied first in the spectral data to reduce the dimension, and then classification among the three groups has been executed by employing the SVM. The SVM classifier includes linear, radial basis function (RBF), polynomial, and sigmoid kernels, and their classification efficacies are computed. Linear and RBF kernels on the testing data sets differentiated OSCC and dysplasia to normal with an accuracy of 100% and OSCC to dysplasia with an accuracy of 95% and 97%, respectively. Polynomial and sigmoid kernels showed less accuracy values among the groups ranging from 48 to 88% and 51 to 100%, respectively. The result indicates that fluorescence spectroscopy and the SVM classifier can help to identify early oral mucosal lesions with significant high accuracy.

Cervical pre-cancer classification using entropic features and CNN: In vivo validation with a handheld fluorescence probe.

Cervical cancer is one of the most prevalent forms of cancer, with a lengthy latent period and a gradual onset phase. Conventional techniques are found to be severely lacking in real time detection of disease progression which can greatly enhance the cure rate. Due to their high sensitivity and specificity, optical techniques are emerging as reliable tools, particularly in case of cancer. It has been seen that biochemical changes are better highlighted through intrinsic fluorescence devoid of interference from absorption and scattering. Its effectiveness in in-vivo conditions is affected by the fact that the intrinsic spectral signatures vary from patient to patient, as well as in different population groups. Here, we overcome this limitation by collectively enumerating the subtle changes in the spectral profiles and correlations through an information theory based entropic approach, which significantly amplifies the minute spectral variations. In conjunction with artificial intelligence (AI)/machine learning (ML) tools, it yields high specificity and sensitivity with a small dataset from patients in clinical conditions, without artificial augmentation. We have used an in-house developed handheld probe (i-HHP) for extracting intrinsic fluorescence spectra of human cervix from 110 different subjects drawn from diverse population groups. The average classification accuracy of the proposed methodology using 10-fold cross validation is 93.17%. A combination of polarised fluorescence spectra from i-HHP and the proposed classifier is proven to be minimally invasive with the ability to diagnose patients in real time. This paves the way for effective use of relatively smaller sized sensitive fluorescence data with advanced AI/ML tools for early cervical cancer detection in clinics.

Human Saliva as a Substitute Diagnostic Medium for the Detection of Oral Lesions Using the Stokes Shift Spectroscopy: Discrimination among the Groups by Multivariate Analysis Methods.

OBJECTIVE: Our objective in the present study is to detect oral mucosal lesions non-invasively by probing two solutions with reference to diagnostic technique and non-invasive media. In the diagnostic technique, Stokes shift (SS) spectroscopy (SSS) has been utilized for the detection of oral lesions. In the diagnostic media, human oral tissue and saliva are included. METHODS: SS measurements are carried out on oral squamous cell carcinoma (OSCC), dysplastic (precancer), and normal/control tissue and saliva samples. Measurements are performed on 86 tissue and 86 saliva samples using the commercially available spectrofluorometer. Offset wavelength of 120 nm, which is the Stoke shift of nicotinamide adenine dinucleotide (NADH) has been selected over the other offsets (i.e., 20, 40, 70 and 90 nm). RESULT: Presence of tryptophan, collagen, NADH, and flavin adenine dinucleotide (FAD) bands were noticed in the SS spectra of tissue. Like the tissue spectra, presence of these bands was also found in the SS spectra of saliva except the collagen band. Classification among the samples accomplished by the make use of multivariate analysis methods. In the multivariate analysis methods, principal component analysis (PCA) is applied first on SS data of tissue and saliva and then Mahalanobis distance (MD) model and receiver operating characteristic (ROC) analysis employed successively. Overall accuracy values of 94.91 %, 84.61 %, and 85.24 % were obtained among OSCC to normal, dysplasia to normal, and OSCC to dysplasia for tissue samples and 88.46 %, 90.16 % and 94.91 % accuracy values were obtained for saliva using the SS spectroscopy. CONCLUSION: Obtained results of human saliva are equivalent to human oral tissue using the SS spectroscopy. It indicates that saliva may be utilized as a substitute diagnostic medium and SS spectroscopy as a diagnostic technique for non-invasive detection of oral lesions at the primarily stage.

Smartphone-based fluorescence spectroscopic device for cervical precancer diagnosis: a random forest classification of in vitro data.

Cervical cancer can be treated and cured if diagnosed at an early stage. Optical devices, developed on smartphone-based platforms, are being tested for this purpose as they are cost-effective, robust, and field portable, showing good efficiency compared to the existing commercial devices. This study reports on the applicability of a 3D printed smartphone-based spectroscopic device (3D-SSD) for the early diagnosis of cervical cancer. The proposed device has the ability to evaluate intrinsic fluorescence (IF) from the collected polarized fluorescence (PF) and elastic-scattering (ES) spectra from cervical tissue samples of different grades. IF spectra of 30 cervical tissue samples have been analyzed and classified using a combination of principal component analysis (PCA) and random forest (RF)-based multi-class classification algorithm with an overall accuracy above 90%. The usage of smartphone for image collection, spectral data analysis, and display makes this device a potential contender for use in clinics as a regular screening tool.

In-vivo Testing of Oral Mucosal Lesions with an In-house Developed Portable Imaging Device and Comparison with Spectroscopy Results.

Progression of oral mucosal lesions is generally marked by changes in the concentration of the intrinsic fluorophores such as collagen, nicotinamide adenine dinucleotide (NADH), flavin adenine dinucleotide (FAD) and porphyrin present in the human oral tissue. In this study, we have probed the changes in FAD and porphyrin by exciting with 405 nm laser light on different sites (tongue, buccal mucosa, lip etc.) of the oral cavity. Testing has been done by an in-house developed fluorescence-based portable imaging device on oral squamous cell carcinoma (OSCC) patients, dysplastic patients and control (normal) group. Fluorescence images recorded from OSCC and dysplastic patients have displayed an enhancement in the red band (porphyrin) as compared to those from the normal volunteers. Porphyrin to FAD intensity ratio (IPorphyrin/IFAD), referred to red to green ratio (Ired/Igreen) has been taken as the diagnostic marker for classification among the groups. Receiver operating characteristic (ROC) analysis applied on IPorphyrin/IFAD is able to discriminate OSCC to normal, dysplasia to normal and OSCC to dysplasia with sensitivities of 100%, 81%, 92% and specificities of 100%, 93% and 92% respectively. Fluorescence imaging probe can capture a large area of oral lesions in a single scan and hence would be useful for initial scanning. On comparison with spectroscopy studies performed by our group, it is found that combining both spectroscopy and imaging as a device may be effective for the early detection of oral lesions. This clinical study was registered on the date 13/10/2017 in the clinical trials registry-India (CTRI) with registration number CTRI/2017/10/010102.

Design, fabrication and testing of 3D printed smartphone-based device for collection of intrinsic fluorescence from human cervix.

Fluorescence spectroscopy has the potential to identify discriminatory signatures, crucial for early diagnosis of cervical cancer. We demonstrate here the design, fabrication and testing of a 3D printed smartphone based spectroscopic device. Polarized fluorescence and elastic scattering spectra are captured through the device using a 405 nm laser and a white LED source respectively. The device has been calibrated by comparison of spectra of standard fluorophores (Flavin adenine dinucleotide, fluorescein, rhodamine, and porphyrin) with the corresponding spectra collected from a commercial spectrometer. A few cervical tissue spectra have also been captured for proof of its applicability as a portable, standalone device for the collection of intrinsic fluorescence spectra from human cervix.

Different orders of scattering through time-resolved Mueller matrix imaging estimates of pre-malignancy in human cervical tissues.

Time-resolved Mueller matrix (MM) imaging polarimetry in transmission mode has been implemented in both epithelium and stromal regions of cervical tissues to explore the various polarization dynamics in connection with the diagnosis of cervical precancer. The picosecond-resolved intensity patterns of various MM elements, resulting from the various orders of scattering, at different time delays provide clear demarcation between the epithelium and stroma of cervical tissue. The time dependent depolarization and retardance maps are seen to differentiate the epithelium from stroma. The average values of time dependent linear, linear-45, and circular depolarization and linear, circular, and scalar retardance parameters in different regimes of scattering from the optically anisotropic stromal region identify the pre-malignancy in cervical tissue. As the disease evolves, time dependent linear depolarization varies to larger values as compared to time dependent circular depolarization. Interestingly, the chirality of the collagen network that rotates the plane of polarized light in either direction in normal samples is limited to only the clockwise direction during the progression of the disease. These results show potential in the early detection and understanding of the mechanisms of morphological changes in cervical cancer development.

Gradient-based one-step fluorescence photoacoustic tomography.

We propose a gradient-based scheme to solve the fluorescence photoacoustic tomographic (FPAT) problem in a fully nonlinear one-step setting, which aims to reconstruct the map of the absorption coefficient of an exogenous fluorophore from boundary photoacoustic pressure data. Adjoint-based gradient evaluation is presented for the FPAT problem in a frequency-domain photoacoustic equation setting. Numerical validations of the resulting Broyden-Fletcher-Goldfarb-Shanno (BFGS) reconstruction scheme are carried out in two dimensions for full- as well as limited-data test cases, and the results are compared with existing Jacobian-based one-step FPAT reconstructions. The reasonably comparable results of the one-step gradient- and Jacobian-based FPAT reconstruction schemes, coupled with the significant computational savings of the former, potentially set up the one-step gradient-based schemes as an advantageous method of choice for FPAT reconstructions. Further reconstruction studies carried out using quantitative photoacoustic tomography (QPAT)-based chromophore reconstructions as inputs to the FPAT inversions show a robustness of fluorophore absorption coefficient reconstructions to the QPAT-obtained inputs.

Assessment of anisotropy of collagen structures through spatial frequencies of Mueller matrix images for cervical pre-cancer detection.

Analysis of spatial frequency of Mueller matrix (MM) images in the Fourier domain yields quantifying parameters of anisotropy in the stromal region in normal and precancerous tissue sections of human uterine cervix. The spatial frequencies of MM elements reveal reliable information of microscopic structural organization arising from the different orientations of collagen fibers in the connective tissue and their randomization with disease progression. Specifically, the local disorder generated in the normal periodic and regular structure of collagen during the growth of the cervical cancer finds characteristic manifestation in the Fourier spectrum of the selected Mueller matrix elements encoding the anisotropy effects through retardance and birefringence. In contrast, Fourier spectra of differential polarization gated images are limited to only one orientation of collagen. Fourier spectra of first row elements M11, M12, M13, and M14 and first column elements M11, M21, M31, and M41 discriminates cervical inter-epithelial neoplasia (CIN)-I from normal cervical tissue samples with 95%-100% sensitivity and specificity. FFT spectra of first and fourth row elements classify CIN-I and CIN-II grades of cervical cancerous tissues with 90%-100% sensitivity and 87%-100% specificity. Normal and CIN-II grade samples are successfully discriminated through Fourier spectra of every MM element while that of M31 element arises as the key classifier among normal, CIN-I, and CIN-II grades of cervical cancer with 100% sensitivity and specificity. These results demonstrate the promise of spatial frequency analysis of Mueller matrix images as a novel, to the best of our knowledge, approach for cancer/precancer detection.

Spatial autocorrelation analysis on two-dimensional images of Mueller matrix for diagnosis and differentiation of cervical precancer.

The spatial autocorrelation and correlation map of amplitude and phase anisotropy along with depolarization parameter from the stroma of uterine cervix utilizing their Mueller matrix (MM) images have been reported for early diagnosis of cervical cancer and differentiation of precancerous stages. The comparative results of the evaluation of the spatial autocorrelation over MM images of optically anisotropic collagen structures from normal and various grades of cervical precancer reflect significant alterations which are correlated with the pathological changes. The spatially varying polarizance from different region of anisotropic stromal region gets correlated within a given spatial lag during the precancerous changes. The diattenuation governing elements M12, M13 and M14 clearly discriminate normal and various grades of precancerous cervical tissue through their autocorrelation profile and correlation map. Evaluation of autocorrelation of spatially varying linear birefringence and linear-45 birefringence characterized by MM elements M34 and M43 and M24 and M42 are not found to differ between the precancer grades, indicating that these changes may be arising from highly directional collagen network while the changes displayed by MM elements M23 and M32 faithfully represent that the chirality of the stromal region is compromised as the cervical cancer evolves and only one type of nature dominates.

Mapping of retardance, diattenuation and polarizance vector on Poincare sphere for diagnosis and classification of cervical precancer.

The mapping of diattenuation, polarizance and retardance vector (normalized Stokes vector) on Poincare sphere, evaluated from Mueller matrix of optically anisotropic stromal region of cervical tissues, is presented for cervical precancer detection and its staging. This reveals that the changes in the polarization states shown by these normalized Stokes vectors corresponds to the degradation of linearly arranged collagen fibers, breakage of the collagen cross links in the stromal region and change in the density of scattering sites when cervical cancer evolves. The distinct nature of real and imaginary parts of the refractive index for linear, linear-45 and circular polarization from the optically anisotropic stromal region underscore the various polarization structures of the connective tissue region which get modified during the pathological changes. It has been found that versatility of these vectors for normal and precancerous cervical tissue of various grades may be utilized as a key distinction for qualitative staging of cervical precancer tissue. Quantitative classification of precancerous stages of cervical precancer has been determined with 95%-100% sensitivity and 93%-100% specificity through the evaluation of linear and circular diattenuation, linear polarizance and linear birefringence from the components of the respective vectors.

Two dimensional multifractal detrended fluctuation analysis of low coherence images for diagnosis of cervical pre-cancer.

We report detection of cervical pre-cancer through their low coherence images by applying two dimensional multifractal detrended fluctuation analysis. Low coherent backscattered images of pre-cancerous cervical tissue sections were captured using a common path interferometric setup. The captured images contain both depth and lateral information of the spatial variation in refractive index (RI) occurring with progression of cervical pre-cancer. A two-dimensional multifractal detrended fluctuation analysis (2D MFDFA) was applied on these low coherent images to study the variations occurring in their fractal nature. Long-range correlations were observed in the RI fluctuations and the strength of multifractality was found to be stronger for higher grades of cervical pre-cancer. A combination of derived multifractal parameters, namely, the generalized Hurst exponent and width of singularity spectrum showed clear differences among the different grades of pre-cancers. Normal, CIN-I and CIN-II were clearly discriminated by application of support vector machine (SVM) using radial Bessel function (RBF) kernel. The specificities and sensitivities between normal and CIN-I, CIN-I and CIN-II and normal and CIN-II were found to be 94%, 88% and 93%, 96% and 98%, 100% respectively.

Comparative study of one-step and two-step quantitative fluorescence photoacoustic tomography.

Fluorescence optical tomography (FOT) is a well-known imaging technique, where fluorescent biological markers are injected to tag targeted tissues (tumors, proteins), and the absorption coefficient of fluorophore is reconstructed to provide contrast-enhanced images. Conventional FOT is known to have lack of stability to noise and shallow imaging depth due to strong optical scattering in biological tissue. Photoacoustic tomography (PAT) has been previously proposed to combine with FOT to resolve this issue. We propose a fully nonlinear one-step reconstruction in a diffuse-approximation modeled fluorescence photoacoustic tomographic (FPAT) setting, where the absorption coefficient of exogenous fluorophore is recovered directly from the photoacoustic data. Computational validations in two dimensions in single- and dual-grid reconstruction settings using full as well as partial data have been provided in support of the proposed algorithm. One-step schemes are particularly useful with respect to dual representations of field (optical and pressure) variables and optical parameters, especially in limited-data settings, which effectively help in constraining the optimization search space. We have compared the results of one- and two-step FPAT schemes and concluded that the one-step reconstructions are superior as compared with the corresponding two-step reconstructions. To the best of our knowledge, these are the first comparisons of one-step and two-step reconstructions in FPAT.

Poly-L-Lysine functionalised MWCNT-rGO nanosheets based 3-d hybrid structure for femtomolar level cholesterol detection using cantilever based sensing platform.

In this work we have developed a novel rGO-MWCNT (reduced graphene oxide-multiwalled carbon nanotube) nanocomposite material with Poly-L-Lysine functionalization which can be used for detection of biomolecules with enhanced sensitivity. The reduced GO sheets are found to play a major role as a connector and helps in the assembly of bundles of carbon nanotubes (CNTs) which may sometime play a role of upstanding nanostructures. The overall composite structure is further fully functionalized resulting in an overall high density of amino groups that can be used to capture biomolecules. The sensitivity of the as synthesized film is tested by the oxidation of cholesterol through cholesterol oxidase enzyme that is biochemically immobilized over these composite films. The test for the immobilization density of the novel films are carried out by mounting these films on sensitive thin section static micro/nano-cantilever platforms. The platforms have capability to measure cholesterol traces in blood upto an extent of 100 femto molar through deflection /bending of the cantilevers due to surface reaction. The films developed show a promise of high immobilization density which is further confirmed through fluorescence studies using FITC labeling of functionalized MWCNT-PLL and rGO-PLL films respectively.

Concentration of FAD as a marker for cervical precancer detection.

We report the ex vivo results of an in-house fabricated portable device based on polarized fluorescence measurements in the clinical environment. This device measures the polarized fluorescence and elastic scattering spectra with 405-nm laser and white light sources, respectively. The dominating fluorophore with 405-nm excitation is flavin adenine dinucleotide (FAD) with a fluorescence peak around 510 nm. The measured spectra are highly modulated by the interplay of scattering and absorption effects. Due to this, valuable information gets masked. To reduce these effects, intrinsic fluorescence was extracted by normalizing polarized fluorescence spectra with polarized elastic scattering spectra obtained. A number of fluorophores contribute to the fluorescence spectra and need to be decoupled to understand their roles in the progression of cancer. Nelder-Mead method has been utilized to fit the spectral profile with Gaussian to decouple the different bands of contributing fluorophores (FAD and porphyrin). The change in concentration of FAD during disease progression manifests in the change in ratio of total area to FWHM of its Gaussian profile. Receiver operating characteristic (ROC) curve analysis has been used to discriminate different grades of cervical precancer by using the ratio as input parameter. The sensitivity and specificity for discrimination of normal samples from CIN I (cervical intraepithelial neoplasia) are 75% and 54%, respectively. Further, the normal samples can be discriminated from CIN II samples with 100% and 82% sensitivity and specificity, respectively, and the CIN I from CIN II samples can also be discriminated with 100% sensitivity and 90% specificity, respectively. The results show that the change in the concentration of (FAD) can be used as a marker to discriminate the different grades of the cancer and biochemical changes at an early stage of the cancer can also be monitored with this technique.

In vivo detection of oral precancer using a fluorescence-based, in-house-fabricated device: a Mahalanobis distance-based classification.

In vivo detection of oral precancer has been carried out by a fluorescence-based, in-house-developed handheld probe on three groups: oral squamous cell carcinoma (OSCC), dysplastic (precancer), and control (normal). Measurements have been performed on a total of 141 patients and volunteers of different age groups. Excitation wavelength of 405 nm was used and fluorescence emission spectra were recorded in the scan range of 450.14 to 763.41 nm at very low incident power (122 µW) from different oral sites buccal mucosa (BM), lateral boarder of tongue (LBT), and dorsal surface of tongue (DST). Spectral profiles are found to vary among the three groups as well as among the different oral sites. Major and minor bands of flavin adenine dinucleotide (FAD) and porphyrins near 500, 634, 676, 689, and 703 nm have been obtained. Porphyrin contribution is found to be more dominant than the FAD in OSCC and dysplastic groups as compared to the control group. A better classification has been observed using the entire spectral range rather than restricting to individual bands, by application of principal component analysis (PCA), Mahalanobis distance model, and receiver operating characteristic analysis (ROC). ROC on Mahalanobis distance differentiates OSCC to normal, dysplastic to normal, and OSCC to dysplastic with sensitivities from 71% to 98%, 92% to 94% and 81% to 93% and specificities 91% to 100%, 86% to 100% and 79% to 97% for oral sites BM, LBT and DST. LBT and DST appear to be more sensitive to dysplasia detection as compared to BM.

Spatio-temporal map for early cancer detection: Proof of concept.

A spatio-temporal map of human cervical tissue is obtained from time-resolved fluorescence images with the dynamic contrast enhanced through principal component analysis (PCA) for clear demarcation of regions of normal and pre-cancerous conditions. Changes in the properties of fluorescence in different environments are captured through fluorescence lifetime maps in the human cervical tissue sample. The correlation embodied in the second principal component (PC) representing sectorial information free of background of the first PC, segregates fluorescence activities, as illustrated in the PC maps. It significantly enhances the contrast of the images which are majorly handicapped by the variations in fluorophore environment. The result is validated on phantoms, mimicking the changes in the environment of normal and abnormal tissues. This spatio-temporal map illustrates the potential of time resolved auto-fluorescence imaging of cervical tissue in combination with PCA to clearly demarcate normal and abnormal regions with enhanced contrast.