Label-free imaging and spectroscopy for early detection of cervical cancer

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.

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.

Cervical Cancer Detection Techniques: A Chronological Review

Cervical cancer is known as a major health problem globally, with high mortality as well as incidence rates. Over the years, there have been significant advancements in cervical cancer detection techniques, leading to improved accuracy, sensitivity, and specificity. This article provides a chronological review of cervical cancer detection techniques, from the traditional Pap smear test to the latest computer-aided detection (CAD) systems. The traditional method for cervical cancer screening is the Pap smear test. It consists of examining cervical cells under a microscope for abnormalities. However, this method is subjective and may miss precancerous lesions, leading to false negatives and a delayed diagnosis. Therefore, a growing interest has been in shown developing CAD methods to enhance cervical cancer screening. However, the effectiveness and reliability of CAD systems are still being evaluated. A systematic review of the literature was performed using the Scopus database to identify relevant studies on cervical cancer detection techniques published between 1996 and 2022. The search terms used included "(cervix OR cervical) AND (cancer OR tumor) AND (detect* OR diagnosis)". Studies were included if they reported on the development or evaluation of cervical cancer detection techniques, including traditional methods and CAD systems. The results of the review showed that CAD technology for cervical cancer detection has come a long way since it was introduced in the 1990s. Early CAD systems utilized image processing and pattern recognition techniques to analyze digital images of cervical cells, with limited success due to low sensitivity and specificity. In the early 2000s, machine learning (ML) algorithms were introduced to the CAD field for cervical cancer detection, allowing for more accurate and automated analysis of digital images of cervical cells. ML-based CAD systems have shown promise in several studies, with improved sensitivity and specificity reported compared to traditional screening methods. In summary, this chronological review of cervical cancer detection techniques highlights the significant advancements made in this field over the past few decades. ML-based CAD systems have shown promise for improving the accuracy and sensitivity of cervical cancer detection. The Hybrid Intelligent System for Cervical Cancer Diagnosis (HISCCD) and the Automated Cervical Screening System (ACSS) are two of the most promising CAD systems. Still, deeper validation and research are required before being broadly accepted. Continued innovation and collaboration in this field may help enhance cervical cancer detection as well as ultimately reduce the disease's burden on women worldwide.

The emerging applications and advancements of Raman spectroscopy in pediatric cancers

Although the survival rate of pediatric cancer has significantly improved, it is still an important cause of death among children. New technologies have been developed to improve the diagnosis, treatment, and prognosis of pediatric cancers. Raman spectroscopy (RS) is a non-destructive analytical technique that uses different frequencies of scattering light to characterize biological specimens. It can provide information on biological components, activities, and molecular structures. This review summarizes studies on the potential of RS in pediatric cancers. Currently, studies on the application of RS in pediatric cancers mainly focus on early diagnosis, prognosis prediction, and treatment improvement. The results of these studies showed high accuracy and specificity. In addition, the combination of RS and deep learning is discussed as a future application of RS in pediatric cancer. Studies applying RS in pediatric cancer illustrated good prospects. This review collected and analyzed the potential clinical applications of RS in pediatric cancers.

Spectroscopic Study of Time-Varying Optical Redox Ratio in NADH/FAD Solution

Autofluorescence imaging has been widely applied as advanced noninvasive diagnostics for in vivo and ex vivo tissues. The optical redox ratio (ORR), which is defined as the fluorescence intensity ratio between reduced nicotine adenine dinucleotide (NADH) and oxidized flavin adenine dinucleotide (FAD), has been used as a diagnostic parameter strongly, because NADH and FAD play an important role in energetic and respiratory metabolism as coenzymes. The ORR method has provided successful assessment in cancer diagnosis including breast, cervical, and oral cancer; few studies have been reported about optical and chemical interference between two molecules resulting in a change in ORR values. In this study, we investigated the variations in ORR values of NADH/FAD mixtures dissolved in tris(hydroxymethyl)aminomethane, phosphate buffer, and deionized water environments. In vitro solutions were prepared in various concentration ratios and the experimental and theoretical ORR values were obtained from fluorescence and absorption spectra in time series. Based on the spectroscopic analysis, we concluded that the inner filter effect causes an instant decrease in FAD fluorescence just after dissolution and that the oxidation-reduction coupled with oxygenation reaction results in time-varying decreases in NADH fluorescence and FAD emission.

Efficacy of Raman Spectroscopy in the Diagnosis of Uterine Cervical Neoplasms: A Meta-Analysis

Background

Uterine cervical neoplasms is widely concerned due to its high incidence rate. Early diagnosis is extremely important for prognosis. The purpose of this article is evaluating the efficacy of Raman spectroscopy in the diagnosis of suspected uterine cervical neoplasms.

Methods

We searched PubMed, Embase, Cochrane Central Register of Controlled Trials (CENTRAL), and Web of science up to September 1, 2021. By analyzing the true positive (TP), false positive (FP), true negative (TN) and false negative (FN) of six included study, we evaluated the pooled and grouping sensitivity, specificity, positive, and negative likelihood ratios (LR), and diagnostic odds ratio (DOR), with 95% confidence intervals (CI), based on random effects models. The overall diagnostic accuracy of Raman spectrum was evaluated by SROC curve analysis and AUC.

Results

After screening with inclusion and exclusion criteria, a total of six study were included in the study. The pooled sensitivity and specificity was 0.98 (95% Cl, 0.93–0.99) and 0.95 (95% Cl, 0.89–0.98). The total PLR and NLR were 21.05 (95% CI, 8.23–53.86) and 0.03 (95% CI, 0.01–0.07), respectively. And the AUC of the SROC curve which show the overall diagnostic accuracy was 0.99 (0.98–1.00).

Conclusion

Through analysis, we confirmed the role of Raman spectroscopy (RS) in the diagnosis of suspected uterine cervical tumors.

Systematic Review Registration

[https://www.crd.york.ac.uk/prospero/], identifier [CRD42021284966].

The detection of cervical neoplasia via optical ımaging: a pilot clinical study

PURPOSE

The present study aims to develop a new high-resolution imaging system for the early diagnosis of cervical neoplasia based on increased vessel density of the cervical tissue.

METHODS

An optical device was developed to obtain high contrast and resolution images of vascular structures of the cervix in the present study. The device utilizes a telecentric lens to capture cervix images under light illumination with a wavelength of 550 nm emitted from LEDs. Images were obtained using the telecentric lens with or without acetic acid application to the cervix. Image processing algorithms were used to contrast and extract the skeleton of the vascular structures on the cervix. In the evaluation of the vascular density, the cervical images were divided into 12 o'clock positions, and the fractal dimension of the vascularity was calculated for each dial area between the o'clock positions. The region with the largest fractal dimension was accepted as the region with the highest probability of lesion. The range of vessel sizes was split into small classes of "bins" for each dial area with the highest fractal dimension. To validate the system's success in differentiating between normal and HSIL lesions, forty five patients who underwent colposcopy and biopsy were included in a pilot study.

RESULTS

The system correctly classified four HSIL cases out of five and failed to detect one HSIL case, achieving an accuracy rate of 97.8% with an 80% sensitivity and 100% specificity.

CONCLUSION

The developed high-resolution optical imaging system may potentially be used in detecting cervical neoplasia just before the biopsy and reduce the number of false-positive cases.

FLIM as a Promising Tool for Cancer Diagnosis and Treatment Monitoring

Fluorescence lifetime imaging microscopy (FLIM) has been rapidly developed over the past 30 years and widely applied in biomedical engineering. Recent progress in fluorophore-dyed probe design has widened the application prospects of fluorescence. Because fluorescence lifetime is sensitive to microenvironments and molecule alterations, FLIM is promising for the detection of pathological conditions. Current cancer-related FLIM applications can be divided into three main categories: (i) FLIM with autofluorescence molecules in or out of a cell, especially with reduced form of nicotinamide adenine dinucleotide, and flavin adenine dinucleotide for cellular metabolism research; (ii) FLIM with Förster resonance energy transfer for monitoring protein interactions; and (iii) FLIM with fluorophore-dyed probes for specific aberration detection. Advancements in nanomaterial production and efficient calculation systems, as well as novel cancer biomarker discoveries, have promoted FLIM optimization, offering more opportunities for medical research and applications to cancer diagnosis and treatment monitoring. This review summarizes cutting-edge researches from 2015 to 2020 on cancer-related FLIM applications and the potential of FLIM for future cancer diagnosis methods and anti-cancer therapy development. We also highlight current challenges and provide perspectives for further investigation.

Fluorescence biomarkers of malignant melanoma detectable in urine

Malignant melanoma (MM) is a cancerous transformation of melanocytes. It is a disease with the worst response to therapy and, compared to other malignancies, presents much earlier with metastases. MM still belongs to relatively late-detected malignant diseases. Even so, the MM mortality rate is up to 96% for a relatively small incidence (5%). The gold standard for MM diagnosis is a histopathological examination that requires invasive surgery. An invasive sampling method of a biological material can be a stressful factor for the patient, which is often the reason why patients do not seek medical assistance as soon as possible. Our goal was to find a link between metabolites in urine and the stage of MM. Two excitation peaks at 360–370 nm and 450 nm were characterised in spectra of urine samples. The emission spectra have shown one significant peak at 410–460 nm. After addition of glutathione reductase to the samples, fluorescence dropped down only in patient samples and hidden fluorophores appeared. Malignant diseases are associated with the presence of specific metabolites that can be detected fluorescently in biological material such as urine, which can be a suitable alternative for an early detection of cancer or for tracking changes during and after treatment.

Discriminating different grades of cervical intraepithelial neoplasia based on label-free phasor fluorescence lifetime imaging microscopy

This study proposed label-free fluorescence lifetime imaging and phasor analysis methods to discriminate different grades of cervical intraepithelial neoplasia (CIN). The human cervical tissue lesions associated with cellular metabolic abnormalities were detected by the status changes of important coenzymes in cells and tissues, reduced nicotinamide adenine dinucleotide (phosphate) (NAD(P)H) and flavin adenine dinucleotide (FAD). Fluorescence lifetime imaging microscopy (FLIM) was used to study human cervical tissues, human cervical epithelial cells, and standard samples. Phasor analysis was applied to reveal the interrelation between the metabolic changes and cancer development, which can distinguish among different stages of cervical lesions from low risk to high risk. This approach also possessed high sensitivity, especially for healthy sites of CIN3 tissues, and indicated the dominance of the glycolytic pathway over oxidative phosphorylation in high-grade cervical lesions. This highly adaptive, sensitive, and rapid diagnostic tool exhibits a great potential for cervical precancer diagnosis.

Green fluorescent protein emission obscures metabolic fluorescent lifetime imaging of NAD(P)H

Autofluorescence of endogenous molecules can provide valuable insights in both basic research and clinical applications. One such technique is fluorescence lifetime imaging (FLIM) of NAD(P)H, which serves as a correlate of glycolysis and electron transport chain rates in metabolically active tissue. A powerful advantage of NAD(P)H-FLIM is the ability to measure cell-specific metabolism within heterogeneous tissues. Cell-type specific identification is most commonly achieved with directed green fluorescent protein (GFP) expression. However, we demonstrate that NAD(P)H-FLIM should not be analyzed in GFP-expressing cells, as GFP molecules themselves emit photons in the blue spectrum with short fluorescence lifetimes when imaged using two-photon excitation at 750 nm. This is substantially different from the reported GFP emission wavelength and lifetime after two-photon excitation at 910 nm. These blue GFP photons are indistinguishable from free NAD(P)H by both emission spectra and fluorescence lifetime. Therefore, NAD(P)H-FLIM in GFP-expressing cells will lead to incorrect interpretations of metabolic rates, and thus, these techniques should not be combined.

Volumetric analysis of breast cancer tissues using machine learning and swept-source optical coherence tomography

In breast cancer, 20%-30% of cases require a second surgery because of incomplete excision of malignant tissues. Therefore, to avoid the risk of recurrence, accurate detection of the cancer margin by the clinician or surgeons needs some assistance. In this paper, an automated volumetric analysis of normal and breast cancer tissue is done by a machine learning algorithm to separate them into two classes. The proposed method is based on a support-vector-machine-based classifier by dissociating 10 features extracted from the A-line, texture, and phase map by the swept-source optical coherence tomographic intensity and phase images. A set of 88 freshly excised breast tissue [44 normal and 44 cancers (invasive ductal carcinoma tissues)] samples from 22 patients was used in our study. The algorithm successfully classifies the cancerous tissue with sensitivity, specificity, and accuracy of 91.56%, 93.86%, and 92.71% respectively. The present computational technique is fast, simple, and sensitive, and extracts features from the whole volume of the tissue, which does not require any special tissue preparation nor an expert to analyze the breast cancer as required in histopathology. Diagnosis of breast cancer by extracting quantitative features from optical coherence tomographic images could be a potentially powerful method for cancer detection and would be a valuable tool for a fine-needle-guided biopsy.

Early detection of colorectal adenocarcinoma: a clinical decision support tool based on plasma porphyrin accumulation and risk factors

BACKGROUND

An increase in naturally-occurring porphyrins has been described in the blood of subjects bearing different kinds of tumors, including colorectal, and this is probably related to a systemic alteration of heme metabolism induced by tumor cells. The aim of our study was to develop an artificial neural network (ANN) classifier for early detection of colorectal adenocarcinoma based on plasma porphyrin accumulation and risk factors.

METHODS

We measured the endogenous fluorescence of blood plasma in 100 colorectal adenocarcinoma patients and 112 controls using a conventional spectrofluorometer. Height, weight, personal and family medical history, use of alcohol, red meat, vegetables and tobacco were all recorded. An ANN model was built up from demographic data and from the integral of the fluorescence emission peak in the range 610-650 nm. We used the Receiver Operating Characteristic (ROC) curve to assess performance in distinguishing colorectal adenocarcinoma patients and controls. A liquid chromatography-high resolution mass spectrometry (LC-HRMS) analytical method was employed to identify the agents responsible for native fluorescence.

RESULTS

The fluorescence analysis indicated that the integral of the fluorescence emission peak in the range 610-650 nm was significantly higher in colorectal adenocarcinoma patients than controls (p < 0.0001) and was weakly correlated with the TNM staging (Spearman's rho = 0.224, p = 0.011). LC-HRMS measurements showed that the agents responsible for the fluorescence emission were mainly protoporphyrin-IX (PpIX) and coproporphyrin-I (CpI). The overall accuracy of our ANN model was 88% (87% sensitivity and 90% specificity) with an area under the ROC curve of 0.83.

CONCLUSIONS

These results confirm that tumor cells accumulate a diagnostic level of endogenous porphyrin compounds and suggest that plasma porphyrin concentrations, indirectly measured through fluorescence analysis, may be useful, together with risk factors, as a clinical decision support tool for the early detection of colorectal adenocarcinoma. Our future efforts will be aimed at examining how plasma porphyrin accumulation correlates with survival and response to therapy.