Asima Pradhan

Enhanced epithelial fluorescence sensitivity using an oblique-angled fiber optic probe for epithelial cancer diagnosis: a modified Monte Carlo simulation study

This study aims to analyze spatially resolved fluorescence for probing various depths of the epithelial tissue using an optimized illumination and collection configuration. The enhanced epithelial sensitivity enables precise detection of alterations in the tissue’s optical properties associated with disease progression. The elastic scattering and fluorescence of a cervical tissue-mimicking phantom were simulated using the Monte Carlo method, which was modified to incorporate oblique illumination, oblique collection, and the propagation of fluorescence photons. The results demonstrate that oblique illumination and collection in a parallel configuration (O-O-P) exhibit high epithelial sensitivity at both small and large source–detector separations. Additionally, spatially resolved fluorescence enables high-sensitivity probing of various depths within the epithelium layer. We modeled a fiber-optic probe employing an O-O-P configuration with 45° beveled fibers. It was observed that this multi-collection fiber-optic probe effectively differentiates cervical precancer grades by analyzing fluorescence intensity variations, which correspond to changes in tissue optical properties during disease progression.

Fluorescence Mueller matrix imaging of the stromal region in unstained cervical tissue sections: quantitative categorization of CIN grades

This study introduces a fluorescence Mueller matrix imaging system coupled with an appropriate analysis method for assessing anisotropy in the stromal regions of human ectocervical tissue sections. The system effectively captures detailed changes in tissue morphology and biochemistry, driven by interactions between intrinsic fluorophores and their microenvironment. Measurements from the connective tissue near the epithelium reveal significant alterations in fluorescence diattenuation and polarizance in both ground and excited states. Pronounced differences in the decomposition-derived anisotropy parameters are observed for unstained cervical tissue sections varying from healthy to different grades of cervical intraepithelial neoplasia (CIN). A significant reduction in the parameters—circular depolarization (M44 decreases from 0.65 in healthy tissue to 0.05 in CIN-III), fluorescence linear diattenuation (α L drops from 0.25 to 0.07), and fluorescence linear polarizance (β L drops from 0.27 to 0.08)—is observed due to the degradation of the collagen fibrous structure present within the stromal region of the human cervix. The polarization parameters demonstrate a linear relationship between healthy and different CIN grades as the cancer progresses. Additionally, a strong correlation was identified between α L and β L , confirmed through linear curve fitting across all sites examined at different CIN grades. This fluorescence Mueller matrix imaging technique, coupled with a suitable decomposition method, serves as a diagnostic tool that minimizes the need for extensive clinical tests. It effectively detects early stage cervical cancer by identifying significant decreases in fluorescence linear diattenuation and polarizance, offering valuable quantitative insights into tissue changes across various pre-cancerous grades.

Empirical Mode Decomposition and Grassmann Manifold‐Based Cervical Cancer Detection

ABSTRACTCervical cancer is a prevalent malignancy affecting the female reproductive system and is recognized as a prominent factor to female mortality on a global scale. Timely and precise detection of various stages of cervical cancer plays a crucial role in enhancing the chances of successful treatment and extending patient survival. Fluorescence spectroscopy stands out as a highly sensitive method for identifying biochemical alterations associated with cancer and numerous other pathological conditions. In our study, empirical mode decomposition (EMD) and Grassmann manifold (GM) learning are explored for reliable cancer detection using fluorescence spectral signals collected from 110 subjects representing various categories of the human cervix. Initially, EMD is used to decompose the signal into several multi‐feature intrinsic mode functions (IMFs) on a spectral scale. Each IMF demonstrates uniqueness by capturing the inherent frequency characteristics within the signal, thus facilitating the extraction of signal features. The GM representation of IMFs is employed for investigating the non‐linear subspace structure within spectral signals, which is subsequently followed by a low‐rank representation to transform and analyze the spectral signals. The GM allows for the extraction of relevant information, reduction of dimensionality, and exploration of complex relationships within data, ultimately contributing to improved diagnosis. Mutual information is further used for feature selection to reduce the number of features and hence the computational cost. When the selected features were employed for classification, the Random Forest (RF) classifier attained a high five‐fold validation accuracy of 99% and exhibited a minimal standard deviation of 0.02. Other state‐of‐the‐art machine learning classifiers were also used and compared with the RF model.

Spatially Resolved Fibre‐Optic Probe for Cervical Precancer Detection Using Fluorescence Spectroscopy and PCA‐ANN‐Based Classification Algorithm: An In Vitro Study

ABSTRACTCervical cancer can be detected at an early stage through the changes occurring in biochemical and morphological properties of epithelium layer. Fluorescence spectroscopy has the ability to identify these subtle changes non‐invasively and in real time with good accuracy in comparison with conventional techniques. In this paper, we report the usage of a custom designed spatially resolved fibre‐optic probe (SRFOP), which consists of 77 fibres in two concentric rings, for the detection of cervical cancer using fluorescence spectroscopy technique. The aim of this study is to classify different grades of cervical precancer on the basis of their fluorescence spectra followed by a robust classification algorithm. Fluorescence spectra of 28 cervical tissue samples of different categories have been recorded using six detector fibres of FOP at different spatial locations with the source fibre (SF). A 405 nm laser diode source has been utilised to excite the samples and a USB 4000 Ocean Optics spectrometer to collect the output spectra in the wavelength range 400–700 nm. Principal component analysis (PCA) was applied to the collected spectra to reduce the dimensionality of the data while preserving the most significant features for classification. The first 10 principal components, which captured the majority of the variance in the spectra, were selected as input features for the classification model. Classification was then performed using an artificial neural network (ANN) with a specific architecture, including an input layer, hidden layers, and a softmax activation function in the output layer. Experimental and classification results both demonstrate that proximal fibres (PFs) perform better than distal fibres (DFs) in capturing the discriminatory features present in the epithelium layer of cervical tissue samples as PF collect most of the signal from the epithelium layer. The combined approach of spatially resolved fluorescence spectroscopy and PCA‐ANN classification techniques is able to discriminate different grades of cervical precancer and normal with an average sensitivity, specificity and accuracy of 93.33%, 96.67% and 95.57%, respectively.

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

Abstract 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

AbstractReal‐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.

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

Abstract 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.

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

AbstractThe 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.

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.

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

AbstractCervical 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.

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.