Fluorescence spectroscopy: a new approach in cervical cancer

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.

Emerging clinical applications in oncology for non-invasive multi- and hyperspectral imaging of cell and tissue autofluorescence

Hyperspectral and multispectral imaging of cell and tissue autofluorescence is an emerging technology in which fluorescence imaging is applied to biological materials across multiple spectral channels. This produces a stack of images where each matched pixel contains information about the sample's spectral properties at that location. This allows precise collection of molecularly specific data from a broad range of native fluorophores. Importantly, complex information, directly reflective of biological status, is collected without staining and tissues can be characterised in situ, without biopsy. For oncology, this can spare the collection of biopsies from sensitive regions and enable accurate tumour mapping. For in vivo tumour analysis, the greatest focus has been on oral cancer, whereas for ex vivo assessment head-and-neck cancers along with colon cancer have been the most studied, followed by oral and eye cancer. This review details the scope and progress of research undertaken towards clinical translation in oncology.

Dual modal fluorescent colposcope combined with near-infrared fluorescent dye TMTP1-PEG4-ICG to detect cervical lesions

To examine the cervical lesions by using the tumor-targeted near-infrared (NIR) fluorescent dyes TMTP1-PEG4-ICG, a dual modal colposcope with visible reflectance imaging and fluorescence imaging was developed. NIR fluorescence imaging and visible light reflectance imaging were integrated together on a colposcope by designing the specific optics and adopting a dual sensor charge coupled device (CCD) camera. Patients with squamous cell carcinoma, adenocarcinoma, cervical intraepithelial neoplasia (CIN) and cervicitis were examined using this dual modal colposcope to validate its potential of cervical cancer detection. Fluorescent dye TMTP1-PEG4-ICG was applied to the cervix 30 minutes before inspection. The fluorescence images were collected after wiping the unbound fluorescent dye using normal saline. Signal to back ratios (SBR) of the fluorescence images were analyzed and compared with the histological analysis. The results suggest that the fluorescent colposcope combined with tumor-specific near-infrared fluorescent dyes TMTP1-PEG4-ICG could help to evaluate cervical lesions in real time.

Fluorescence imaging and Raman spectroscopy applied for the accurate diagnosis of breast cancer with deep learning algorithms

Deep learning is usually combined with a single detection technique in the field of disease diagnosis. This study focused on simultaneously combining deep learning with multiple detection technologies, fluorescence imaging and Raman spectroscopy, for breast cancer diagnosis. A number of fluorescence images and Raman spectra were collected from breast tissue sections of 14 patients. Pseudo-color enhancement algorithm and a convolutional neural network were applied to the fluorescence image processing, so that the discriminant accuracy of test sets, 88.61%, was obtained. Two different BP-neural networks were applied to the Raman spectra that mainly comprised collagen and lipid, so that the discriminant accuracy of 95.33% and 98.67% of test sets were gotten, respectively. Then the discriminant results of fluorescence images and Raman spectra were counted and arranged into a characteristic variable matrix to predict the breast tissue samples with partial least squares (PLS) algorithm. As a result, the predictions of all samples are correct, with minor error of predictive value. This study proves that deep learning algorithms can be applied into multiple diagnostic optics/spectroscopy techniques simultaneously to improve the accuracy in disease diagnosis.

Label-free grading and staging of urothelial carcinoma through multimodal fibre-probe spectroscopy

Urothelial carcinoma (UC) is the most common bladder tumour. Proper treatment requires tumour resection for diagnosing its grade (aggressiveness) and stage (invasiveness). White-light cystoscopy and histopathological examination are the gold standard procedures for clinical and histopathological diagnostics, respectively. However, cystoscopy is limited in terms of specificity, histology requires long tissue processing, both procedures rely on operator's experience. Multimodal optical spectroscopy can provide a powerful tool for detecting, staging and grading bladder tumours in a fast, reliable and label-free modality. In this study, we collected fluorescence, Raman and reflectance spectra from 50 biopsies obtained from 32 patients undergoing transurethral resection of bladder tumour using a multimodal fibre-probe. Principal component analysis allowed distinguishing normal from pathological tissues, as well as discriminating tumour stages and grades. Each individual spectroscopic technique provided high specificity and sensitivity in classifying all tissues; however, a multimodal approach resulted in a considerable increase in diagnostic accuracy (≥95%), which is of paramount importance for tumour grading and staging. The presented method offers the potential for being applied in cystoscopy and for providing an automated diagnosis of UC at the clinical level, with an improvement with respect to current state-of-the-art procedures.

Monitoring Breast Cancer Response to Treatment Using Stokes Shift Spectroscopy of Blood Plasma

With the emerging trend of personalized cancer treatment, there is a need to develop noninvasive/minimally invasive techniques for treatment monitoring. In this regard, in this work fluorescence analysis of blood plasma of breast cancer patients has been used for the evaluation of response to treatment. This approach delivers information not only about the change in biochemical constituents but also about the altered metabolic pathway. Spectral deconvolution method is employed to compute the fluorescence intensity, peak wavelength, and full-width half maxima for different endogenous fluorophores. The fluorescence measurements were made on blood plasma collected from 10 normal subjects, 10 pre-treated cancer patients, and 10 post-treated patients. Besides, variations in relative concentration of tryptophan, collagen, NADH, and FAD, peak shifts and broadening of peaks are observed for tryptophan, NADH, and FAD, in blood plasma of pre-treated cancer patients indicating both biochemical and microenvironmental changes at cellular level. Further, the spectral profile of blood plasma of post-treated patients found to be similar to blood plasma of normal subjects. Linear discriminant analysis showed that pre-treated and post-treated breast cancer is discriminated with a sensitivity and specificity of 100% and 100% respectively.

Tissue multifractality and hidden Markov model based integrated framework for optimum precancer detection

We report the application of a hidden Markov model (HMM) on multifractal tissue optical properties derived via the Born approximation-based inverse light scattering method for effective discrimination of precancerous human cervical tissue sites from the normal ones. Two global fractal parameters, generalized Hurst exponent and the corresponding singularity spectrum width, computed by multifractal detrended fluctuation analysis (MFDFA), are used here as potential biomarkers. We develop a methodology that makes use of these multifractal parameters by integrating with different statistical classifiers like the HMM and support vector machine (SVM). It is shown that the MFDFA-HMM integrated model achieves significantly better discrimination between normal and different grades of cancer as compared to the MFDFA-SVM integrated model.

Optical techniques for cervical neoplasia detection

This paper provides an overview of the current research in the field of optical techniques for cervical neoplasia detection and covers a wide range of the existing and emerging technologies. Using colposcopy, a visual inspection of the uterine cervix with a colposcope (a binocular microscope with 3- to 15-fold magnification), has proven to be an efficient approach for the detection of invasive cancer. Nevertheless, the development of a reliable and cost-effective technique for the identification of precancerous lesions, confined to the epithelium (cervical intraepithelial neoplasia) still remains a challenging problem. It is known that even at early stages the neoplastic transformations of cervical tissue induce complex changes and modify both structural and biochemical properties of tissues. The different methods, including spectroscopic (diffuse reflectance spectroscopy, induced fluorescence and autofluorescence spectroscopy, Raman spectroscopy) and imaging techniques (confocal microscopy, optical coherence tomography, Mueller matrix imaging polarimetry, photoacoustic imaging), probe different tissue properties that may serve as optical biomarkers for diagnosis. Both the advantages and drawbacks of these techniques for the diagnosis of cervical precancerous lesions are discussed and compared.

An alternative approach for estimating the accuracy of colposcopy in detecting cervical precancer

Introduction

Since colposcopy helps to detect cervical cancer in its precancerous stages, as new strategies and technologies are developed for the clinical management of cervical neoplasia, precisely determining the accuracy of colposcopy is important for characterizing its continued role. Our objective was to employ a more precise methodology to estimate of the accuracy of colposcopy to better reflect clinical practice.

Study design

For each patient, we compared the worst histology result among colposcopically positive sites to the worst histology result among all sites biopsied, thereby more accurately determining the number of patients that would have been underdiagnosed by colposcopy than previously estimated.

Materials and Methods

We utilized data from a clinical trial in which 850 diagnostic patients had been enrolled. Seven hundred and ninety-eight of the 850 patients had been examined by colposcopy, and biopsy samples were taken at colposcopically normal and abnormal sites. Our endpoints of interest were the percentages of patients underdiagnosed, and sensitivity and specificity of colposcopy.

Results

With the threshold of low-grade squamous intraepithelial lesions for positive colposcopy and histology diagnoses, the sensitivity of colposcopy decreased from our previous assessment of 87.0% to 74.0%, while specificity remained the same. The drop in sensitivity was the result of histologically positive sites that were diagnosed as negative by colposcopy. Thus, 28.4% of the 798 patients in this diagnostic group would have had their condition underdiagnosed by colposcopy in the clinic.

Conclusions

In utilizing biopsies at multiple sites of the cervix, we present a more precise methodology for determining the accuracy of colposcopy. The true accuracy of colposcopy is lower than previously estimated. Nevertheless, our results reinforce previous conclusions that colposcopy has an important role in the diagnosis of cervical precancer.