Enhancement of early cervical cancer diagnosis with epithelial layer analysis of fluorescence lifetime images.
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ABSTRACT:
SUBMITTER: Gu J
PROVIDER: S-EPMC4428628 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Cervical cancer has high morbidity and mortality rates, affecting hundreds of thousands of women worldwide and requiring more accurate screening for early intervention and follow-up treatment. Cytology is the current dominant clinical screening approach, and though it has been used for decades, it has unsatisfactory sensitivity and specificity. In this work, fluorescence lifetime imaging microscopy (FLIM) was used for the imaging of exfoliated cervical cells in which an endogenous coenzyme involved in metabolism, namely, reduced nicotinamide adenine dinucleotide (phosphate) [NAD(P)H], was detected to evaluate the metabolic status of cells. FLIM images from 71 participants were analyzed by the unsupervised machine learning method to build a prediction model for cervical cancer risk. The FLIM method combined with unsupervised machine learning (FLIM-ML) had a sensitivity and specificity of 90.9% and 100%, respectively, significantly higher than those of the cytology approach. One cancer recurrence case was predicted as high-risk several months earlier using this method as compared to using current clinical methods, implying that FLIM-ML may be very helpful for follow-up cancer care. This study illustrates the clinical applicability of FLIM-ML as a detection method for cervical cancer screening and a convenient tool for follow-up cancer care.
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Project description:Cervical cancer is one of the most common types of malignant tumors in women. In addition, it causes death in the latter stages. Squamous cell carcinoma is the most common and aggressive form of cervical cancer and must be diagnosed early before it progresses to a dangerous stage. Liquid-based cytology (LBC) swabs are best and most commonly used for cervical cancer screening and are converted from glass slides to whole-slide images (WSIs) for computer-assisted analysis. Manual diagnosis by microscopes is limited and prone to manual errors, and tracking all cells is difficult. Therefore, the development of computational techniques is important as diagnosing many samples can be done automatically, quickly, and efficiently, which is beneficial for medical laboratories and medical professionals. This study aims to develop automated WSI image analysis models for early diagnosis of a cervical squamous cell dataset. Several systems have been designed to analyze WSI images and accurately distinguish cervical cancer progression. For all proposed systems, the WSI images were optimized to show the contrast of edges of the low-contrast cells. Then, the cells to be analyzed were segmented and isolated from the rest of the image using the Active Contour Algorithm (ACA). WSI images were diagnosed by a hybrid method between deep learning (ResNet50, VGG19 and GoogLeNet), Random Forest (RF), and Support Vector Machine (SVM) algorithms based on the ACA algorithm. Another hybrid method for diagnosing WSI images by RF and SVM algorithms is based on fused features of deep-learning (DL) models (ResNet50-VGG19, VGG19-GoogLeNet, and ResNet50-GoogLeNet). It is concluded from the systems' performance that the DL models' combined features help significantly improve the performance of the RF and SVM networks. The novelty of this research is the hybrid method that combines the features extracted from deep-learning models (ResNet50-VGG19, VGG19-GoogLeNet, and ResNet50-GoogLeNet) with RF and SVM algorithms for diagnosing WSI images. The results demonstrate that the combined features from deep-learning models significantly improve the performance of RF and SVM. The RF network with fused features of ResNet50-VGG19 achieved an AUC of 98.75%, a sensitivity of 97.4%, an accuracy of 99%, a precision of 99.6%, and a specificity of 99.2%.
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Project description:We used AFM HarmoniX modality to analyse the surface of individual human cervical epithelial cells at three stages of progression to cancer, normal, immortal (pre-malignant) and carcinoma cells. Primary cells from 6 normal strains, 6 cancer, and 6 immortalized lines (derived by plasmid DNA-HPV-16 transfection of cells from 6 healthy individuals) were tested. This cell model allowed for good control of the cell phenotype down to the single cell level, which is impractical to attain in clinical screening tests (ex-vivo). AFM maps of physical (nonspecific) adhesion are collected on fixed dried cells. We show that a surface parameter called fractal dimension can be used to segregate normal from both immortal pre-malignant and malignant cells with sensitivity and specificity of more than 99%. The reported method of analysis can be directly applied to cells collected in liquid cytology screening tests and identified as abnormal with regular optical methods to increase sensitivity.Despite cervical smear screening, sometimes it is very difficult to differentiate cancers cells from pre-malignant cells. By using AFM to analyze the surface properties of human cervical epithelial cells, the authors were able to accurately identify normal from abnormal cells. This method could augment existing protocols to increase diagnostic accuracy.
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Project description:The phasor approach is used in fluorescence lifetime imaging microscopy for several purposes, notably to calculate the metabolic index of single cells and tissues. An important feature of the phasor approach is that it is a fit-free method allowing immediate and easy to interpret analysis of images. In a recent paper, we showed that three or four intensity fractions of exponential components can be resolved in each pixel of an image by the phasor approach using simple algebra, provided the component phasors are known. This method only makes use of the rule of linear combination of phasors rather than fits. Without prior knowledge of the components and their single exponential decay times, resolution of components and fractions is much more challenging. Blind decomposition has been carried out only for cuvette experiments wherein the statistics in terms of the number of photons collected is very good. In this paper, we show that using the phasor approach and measurements of the decay at phasor harmonics 2 and 3, available using modern electronics, we could resolve the decay in each pixel of an image in live cells or mice liver tissues with two or more exponential components without prior knowledge of the values of the components. In this paper, blind decomposition is achieved using a graphical method for two components and a minimization method for three components. This specific use of the phasor approach to resolve multicomponents in a pixel enables applications where multiplexing species with different lifetimes and potentially different spectra can provide a different type of super-resolved image content.
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Project description:A method of rapidly differentiating lung tumor from healthy tissue is extraordinarily needed for both the diagnosis and the intraoperative margin assessment. We assessed the ability of fluorescence lifetime imaging microscopy (FLIM) for differentiating human lung cancer and normal tissues with the autofluorescence, and also elucidated the mechanism in tissue studies and cell studies. A 15-patient testing group was used to compare FLIM results with traditional histopathology diagnosis. Based on the endogenous fluorescence lifetimes of the testing group, a criterion line was proposed to distinguish normal and cancerous tissues. Then by blinded examined 41 sections from the validation group of other 16 patients, the sensitivity and specificity of FLIM were determined. The cellular metabolism was studied with specific perturbations of oxidative phosphorylation and glycolysis in cell studies. The fluorescence lifetime of cancerous lung tissues is consistently lower than normal tissues, and this is due to the both decrease of reduced nicotinamide adenine dinucleotide (NADH) and flavin adenine dinucleotide (FAD) lifetimes. A criterion line of lifetime at 1920 ps can be given for differentiating human lung cancer and normal tissues.The sensitivity and specificity of FLIM for lung cancer diagnosis were determined as 92.9% and 92.3%. These findings suggest that NADH and FAD can be used to rapidly diagnose lung cancer. FLIM is a rapid, accurate and highly sensitive technique in the judgment during lung cancer surgery and it can be potential in earlier cancer detection.
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Project description:Optical imaging of tissue autofluorescence has the potential to provide rapid label-free screening and detection of surface tumors for clinical applications, including when combined with endoscopy. Quantitative imaging of intensity-based contrast is notoriously difficult and spectrally resolved imaging does not always provide sufficient contrast. We demonstrate that fluorescence lifetime imaging (FLIM) applied to intrinsic tissue autofluorescence can directly contrast a range of surface tissue tumors, including in gastrointestinal tissues, using compact, clinically deployable instrumentation achieving wide-field fluorescence lifetime images of unprecedented clarity. Statistically significant contrast is observed between cancerous and healthy colon tissue for FLIM with excitation at 355 nm. To illustrate the clinical potential, wide-field fluorescence lifetime images of unstained ex vivo tissue have been acquired at near video rate, which is an important step towards real-time FLIM for diagnostic and interoperative imaging, including for screening and image-guided biopsy applications.
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Project description:BackgroundCervical cancer is an important public health problem. Conventional colposcopy is inefficient in the diagnosis of cervical lesions and massive biopsies result in trauma. There is an urgent need for a new clinical strategy to triage women with abnormal cervical screening results immediately and effectively. In this study, the high-resolution microendoscopy combined with methylene blue cell staining technology was used to perform real-time in vivo imaging of the cervix for the first time.MethodsA total of 41 patients were enrolled in the study. All patients underwent routine colposcopy and cervical biopsy, and high-resolution images of methylene blue-stained cervical lesions were obtained in vivo using microendoscopy. The cell morphological features of benign and neoplastic cervical lesions stained with methylene blue under microendoscopy were analyzed and summarized. The microendoscopy and histopathology findings of the high-grade squamous intraepithelial lesion (HSIL) and more severe lesions were compared.ResultsThe overall consistency of microendoscopy diagnosis with pathological diagnosis was 95.12% (39/41). Diagnostic cell morphological features of cervicitis, low-grade squamous intraepithelial lesion (LSIL), HSIL, adenocarcinoma in situ, and invasive cancer were clearly demonstrated in methylene blue stained microendoscopic images. In HSIL and more severe lesions, microendoscopic methylene blue cell staining technology can show the microscopic diagnostic features consistent with histopathology.ConclusionsThis study was an initial exercise in the application of the microendoscopy imaging system combined with methylene blue cell staining technology to cervical precancerous lesions and cervical cancer screening. The results provided the basis for a novel clinical strategy for triage of women with abnormal cervical screening results using in vivo non-invasive optical diagnosis technology.
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