Optimal excitation wavelengths for in vivo detection of oral neoplasia using fluorescence spectroscopy

There is no satisfactory mechanism to detect premalignant lesions in the upper aero-digestive tract. Fluorescence spectroscopy has potential to bridge the gap between clinical examination and invasive biopsy; however, optimal excitation wavelengths have not yet been determined. The goals of this study were to determine optimal excitation-emission wavelength combinations to discriminate normal and precancerous/cancerous tissue, and estimate the performance of algorithms based on fluorescence. Fluorescence excitation-emission matrices (EEM) were measured in vivo from 62 sites in nine normal volunteers and 11 patients with a known or suspected premalignant or malignant oral cavity lesion. Using these data as a training set, algorithms were developed based on combinations of emission spectra at various excitation wavelengths to determine which excitation wavelengths contained the most diagnostic information. A second validation set of fluorescence EEM was measured in vivo from 281 sites in 56 normal volunteers and three patients with a known or suspected premalignant or malignant oral cavity lesion. Algorithms developed in the training set were applied without change to data from the validation set to obtain an unbiased estimate of algorithm performance. Optimal excitation wavelengths for detection of oral neoplasia were 350, 380 and 400 nm. Using only a single emission wavelength of 472 nm, and 350 and 400 nm excitation, algorithm performance in the training set was 90% sensitivity and 88% specificity and in the validation set was 100% sensitivity, 98% specificity. These results suggest that fluorescence spectroscopy can provide a simple, objective tool to improve in vivo identification of oral cavity neoplasia.     

N2 laser excited autofluorescence spectroscopy of formalin-fixed human breast tissue

The paper reports results of an in vitro study on autofluorescence spectroscopy of fresh and formalin-fixed human breast tissue samples to investigate the effect of formalin fixation on the measured autofluorescence spectra. It also explores the applicability of the approach in discriminating cancerous from the uninvolved sites of the formalin-fixed breast tissues based on their autofluorescence spectra. A probability-based diagnostic algorithm, making use of the theory of relevance vector machine (RVM), a powerful recent approach for statistical pattern recognition, was developed for that purpose. The algorithm provided sensitivity values of up to 97% and specificity values of up to 100% towards cancer for both the independent validation data set as well as for the training data set based on leave-one-out cross-validation. These results suggest that autofluorescence spectroscopy may prove to be a valuable additional in vitro diagnostic modality in clinical pathology setting for discriminating cancerous tissue sites from normal sites.     

Fourier transform infrared imaging analysis in discrimination studies of squamous cell carcinoma

Oral squamous cell carcinoma (OSCC) of the oral cavity and oropharynx represents more than 95% of all malignant neoplasms in the oral cavity. Histomorphological evaluation of this cancer type is invasive and remains a time consuming and subjective technique. Therefore, novel approaches for histological recognition are necessary to identify malignancy at an early stage. Fourier transform infrared (FTIR) imaging has become an essential tool for the detection and characterization of the molecular components of biological processes, such as those responsible for the dynamic properties of tumor progression. FTIR imaging is a modern analytical technique enabling molecular imaging of a complex biological sample and is based on the absorption of IR radiation by vibrational transitions in covalent bonds. One major advantage of this technique is the acquisition of local molecular expression profiles, while maintaining the topographic integrity of the tissue and avoiding time-consuming extraction, purification, and separation steps. With this imaging technique, it is possible to obtain unique images of the spatial distribution of proteins, lipids, carbohydrates, cholesterols, nucleic acids, phospholipids, and small molecules with high spatial resolution. Analysis and visualization of FTIR imaging datasets are challenging and the use of chemometric tools is crucial in order to take advantage of the full measurement. Therefore, methodologies for this task based on the novel developed algorithm for multivariate image analysis (MIA) are often necessary. In the present study, FTIR imaging and data analysis methods were combined to optimize the tissue measurement mode after deparaffinization and subsequent data evaluation (univariate analysis and MIAs). We demonstrate that it is possible to collect excellent IR spectra from formalin-fixed paraffin-embedded (FFPE) tissue microarrays (TMAs) of OSCC tissue sections employing an optimised analytical protocol. The correlation of FTIR imaging to the morphological tissue features obtained by histological staining of the sections demonstrated that many histomorphological tissue patterns can be visualized in the colour images. The different algorithms used for MIAs of FTIR imaging data dramatically increased the information content of the IR images from squamous cell tissue sections. These findings indicate that intra-operative and surgical specimens of squamous cell carcinoma tissue can be characterized by FTIR imaging.     

Cervical Precancer Detection Using a Multivariate Statistical Algorithm Based on Laser-Induced Fluorescence Spectra at Multiple Excitation Wavelengths

Abstract— A portable fluorimeter was developed and utilized to acquire fluorescence spectra from 381 cervical sites in 95 patients at 337, 380 and 460 nm excitation immediately prior to colposcopy. A multivariate statistical algorithm was used to extract clinically useful information from tissue spectra acquired in vivo. Two full-parameter algorithms were developed using tissue fluorescence emission spectra at all three excitation wavelengths (161 excitation-emission wavelength pairs) for cervical precancer (squamous intraepithelial lesion [SIL]) detection: a screening algorithm that discriminates between SIL and non-SIL with a sensitivity of 82 ± 1.4% and specificity of 68 ± 0.0%, and a diagnostic algorithm that differentiates high-grade SIL from non-high-grade SIL with a sensitivity and specificity of 79 ± 2% and 78 ± 6%, respectively. Multivariate statistical analysis was also employed to reduce the number of fluorescence excitation-emission wavelength pairs needed to redevelop algorithms that demonstrate a minimum decrease in classification accuracy. Two reduced-parameter algorithms that employ fluorescence intensities at only 15 excitation-emission wavelength pairs were developed: the screening algorithm differentiates SIL from non-SIL with a sensitivity of 84 ± 1.5% and specificity of 65 ± 2% and the diagnostic algorithm discriminates high-grade SIL from non-high-grade SIL with a sensitivity and specificity of 78 ± 0.7% and 74 ± 2%, respectively. Both the full-parameter and reduced-parameter screening algorithms discriminate between SIL and non-SIL with a similar specificity (±5%) and a substantially improved sensitivity relative to Pap smear screening. A comparison of the full-parameter and reduced-parameter diagnostic algorithms to colposcopy in expert hands indicates that all three have a very similar sensitivity and specificity for differentiating high-grade SIL from non-high-grade SIL.     

Multiplex chemiluminescence microscope imaging of P16INK4A and HPV DNA as biomarker of cervical neoplasia

Classification of cervical intraepithelial neoplasia (CIN) lesions in low-grade (CIN1) or high-grade (CIN2-3) ones is crucial for optimal patient management, but current histological diagnosis on bioptic samples is often hampered by inter-observer variability. To allow objective classification, we have exploited the peculiar characteristics of chemiluminescence detection, such as high sensitivity and easy quantification of the luminescence signal, to perform sequentially in the same tissue section both an immunohistochemical quantitative detection of p16^INK4A (a protein marker of high-grade CIN lesions) and an in situ hybridization for human papillomavirus (generally accepted as a necessary but insufficient cause of cervical carcinoma). Different label enzymes (alkaline phosphatase and horseradish peroxidase) were employed in order to avoid any interference between the two assays, and quantitative chemiluminescence image analysis was used to obtain objective evaluation of sample positivity. The multiplexed method allowed detection of two complementary biomarkers and provided discrimination between different lesions (non-neoplastic, low-grade and high-grade CIN). This assay might thus represent an accurate and objective diagnostic test providing important information for counseling, selection of therapy and follow up after surgical treatment.     

In vivo diagnosis of cervical precancer using Raman spectroscopy and genetic algorithm techniques

This study aimed to evaluate the clinical utility of applying near-infrared (NIR) Raman spectroscopy and genetic algorithm-partial least squares-discriminant analysis (GA-PLS-DA) to identify biomolecular changes of cervical tissues associated with dysplastic transformation during colposcopic examination. A total of 105 in vivo Raman spectra were measured from 57 cervical sites (35 normal and 22 precancer sites) of 29 patients recruited, in which 65 spectra were from normal sites, while 40 spectra were from cervical precancerous lesions (i.e., 7 low-grade CIN and 33 high-grade CIN). The GA feature selection technique incorporated with PLS was utilized to study the significant biochemical Raman bands for differentiation between normal and precancer cervical tissues. The GA-PLS-DA algorithm with double cross-validation (dCV) identified seven diagnostically significant Raman bands in the ranges of 925-935, 979-999, 1080-1090, 1240-1260, 1320-1340, 1400-1420, and 1625-1645 cm(-1) related to proteins, nucleic acids and lipids in tissue, and yielded a diagnostic accuracy of 82.9% (sensitivity of 72.5% (29/40) and specificity of 89.2% (58/65)) for precancer detection. The results of this exploratory study suggest that Raman spectroscopy in conjunction with GA-PLS-DA and dCV methods has the potential to provide clinically significant discrimination between normal and precancer cervical tissues at the molecular level.     

Objective Detection of Oral Carcinoma with Multispectral Fluorescence Lifetime Imaging In Vivo

Successful early detection and demarcation of oral carcinoma can greatly impact the associated morbidity and mortality rates. Current methods for detection of oral cancer include comprehensive visual examination of the oral cavity, typically followed by tissue biopsy. A noninvasive means to guide the clinician in making a more objective and informed decision toward tissue biopsy can potentially improve the diagnostic yield of this process. To this end, we investigate the potential of fluorescence lifetime imaging (FLIM) for objective detection of oral carcinoma in the hamster cheek pouch model of oral carcinogenesis in vivo. We report that systematically selected FLIM features can differentiate between low‐risk (normal, benign and low‐grade dysplasia) and high‐risk (high‐grade dysplasia and cancer) oral lesions with sensitivity and specificity of 87.26% and 93.96%, respectively. We also show the ability of FLIM to generate “disease” maps of the tissue which can be used to evaluate relative risk of neoplasia. The results demonstrate the potential of multispectral FLIM with objective image analysis as a noninvasive tool to guide comprehensive oral examination.     

A far-red fluorescent contrast agent to image epidermal growth factor receptor expression

Recent developments in optical technologies have the potential to improve the speed and accuracy of screening and diagnosis of curable precancerous lesions and early cancer, thereby decreasing the costs of detection and management of epithelial malignancies. The development of molecular-specific contrast agents for markers of early neoplastic transformation could improve the detection and molecular characterization of premalignant lesions. In the oral cavity, epidermal growth factor receptor (EGFR) overexpression has been identified in early stages of premalignant lesions of the oral squamous cell carcinoma; therefore, real-time assessment of EGFR expression could serve as a biomarker for oral neoplasia. The purpose of our study was to develop a molecular-specific optical contrast agent targeted against EGFR for in vivo assessment of epithelial neoplasia using a monoclonal antibody and the far-red fluorescent dye, Alexa Fluor 660 streptavidin. In addition to demonstrating the specificity of the contrast agent for EGFR in cell lines, we document the ability to achieve penetration through 500 microm thick epithelial layers using multilayer tissue constructs and permeability-enhancing agents. Finally, using the fluorescence intensity of the contrast agent on fresh oral cavity tissue sections, we were able to distinguish abnormal from normal oral tissue. This contrast agent should have important clinical applications for use in conjunction with fluorescence spectroscopy or imaging (or both) to facilitate tumor detection and demarcation.     

Near-Infrared Raman Spectroscopy for In Vitro Detection of Cervical Precancers

Abstract— In this study, we investigate the potential of near-infrared Raman spectroscopy to differentiate cervical precancers from normal tissues, inflammation and metaplasia and to differentially diagnose low-grade and high-grade precancers. Near infrared Raman spectra were measured from 36 biopsies from 18 patients in vitro. Detection algorithms were developed and evaluated relative to histopathologic examination. Algorithms based on empirically selected peak intensities, ratios of peak intensities and a combination of principal component analysis for data reduction and Fisher discriminant analysis for classification were investigated. Spectral peaks were tentatively identified from measured spectra of potential chromophores. Empirically selected normalized intensities can differentiate precancers from other tissues with an average sensitivity and specificity of 88 ± 4% and 92 ± 4%. Ratios of un-normalized intensities can differentiate precancers from other tissues with a sensitivity and specificity of 82% and 88% and high-grade from low-grade lesions with a sensitivity and specificity of 100%. Using multivariate methods, intensities at eight frequencies can be used to differentiate precancers from all other tissues with a sensitivity and specificity of 82% and 92% in an unbiased test. Raman algorithms can potentially separate benign abnormalities such as inflammation and metaplasia from precancers. Comparison of tissue spectra to published and measured chromophore spectra indicate that the most likely primary contributors to the tissue spectra are collagen, nucleic acids, phospholipids and glucose 1-phos-phate. These results suggest that near-infrared Raman spectroscopy can be used for cervical precancer diagnosis and may be able to accurately separate samples with inflammation and metaplasia from precancer.     

黄酮类化合物^13C核磁共振谱的模式识别分类研究

黄酮类化合物广泛存在于植物的各个部位,根据其结构,主要分为黄酮、黄酮醇、双氢黄酮、异黄酮等。对于大多数植物化学工作者来说,解析一个未知化合物都必须经历先确定骨架类型,后确定基团位置这一过程。而前者则需要对该类型的数据规律有充分的认识,否则,就可能导致错误结果。     

Neural Network Classification of Mutagens Using Structural Fragment Data

Abstract

A neural network was applied to a large, structurally heterogeneous data set of mutagens and nonmutagens to investigate structure-property relationships. Substructural data comprising a total of 1280 fragments were used as inputs. The training of the back-propagation networks was directed by an algorithm which selected an optimal subset of fragments in order to maximize their discriminating power, and a good predictive network.

The system comprised three programs: the first used a keyfile of 100 fragments to generate training and test files, the second was the network itself and a procedure for ranking the effectiveness of these fragments and the third randomly replaced the lowest fragments. This cycle was then repeated. After running on a 386/33 PC several networks produced approximately 11% failures in the test set and 6% in the training set.

By simplifying the output of the hidden layer it was possible to describe the hidden layer states in terms of clusters of mutagens and non-mutagens. Some of these clusters were structurally homogeneous and contained known mutagenic and non-mutagenic structural classes. This analysis provided a useful means of demonstrating how the network was classifying the data.     

Robust classification of low-grade cervical cytology following analysis with ATR-FTIR spectroscopy and subsequent application of self-learning classifier eClass

Although the UK cervical screening programme has reduced mortality associated with invasive disease, advancement from a high-throughput predictive methodology that is cost-effective and robust could greatly support the current system. We combined analysis by attenuated total reflection Fourier-transform infrared spectroscopy of cervical cytology with self-learning classifier eClass. This predictive algorithm can cope with vast amounts of multidimensional data with variable characteristics. Using a characterised dataset [set A: consisting of UK cervical specimens designated as normal (n = 60), low-grade (n = 60) or high-grade (n = 60)] and one further dataset (set B) consisting of n = 30 low-grade samples, we set out to determine whether this approach could be robustly predictive. Variously extending the training set consisting of set A with set B data produced good classification rates with three two-class cascade classifiers. However, a single three-class classifier was equally efficient, producing a user-friendly, applicable methodology with improved interpretability (i.e., better classification with only one set of fuzzy rules). As data from set B were added incrementally to the training set, the model learned and evolved. Additionally, monitoring of results of the set B low-grade specimens (known to be low-grade cervical cytology specimens) provided the opportunity to explore the possibility of distinguishing patients likely to progress towards invasive disease. eClass exhibited a remarkably robust predictive power in a user-friendly fashion (i.e., high throughput, ease of use) compared to other classifiers (k-nearest neighbours, support vector machines, artificial neural networks). Development of eClass to classify such datasets for applications such as screening exhibits robustness in identifying a dichotomous marker of invasive disease progression.     

Quantification of glucose diffusion in human lung tissues by using Fourier domain optical coherence tomography

In this study, we report permeability coefficients of 30% glucose diffusion by the optical coherence tomography signal slope (OCTSS) method in four kinds of human lung tissue in vitro: normal lung tissue, benign granulomatosis lung tissue, squamous cell carcinoma and adenocarcinoma tumor. To quantify the permeability coefficient of the agent, the monitored region was 80 μm thickness at a tissue depth of ca 230 μm from the surface. The permeability coefficients of 30% glucose from 10 independent experiments were averaged and found to be (1.35 ± 0.13) × 10(-5) cm s(-1) from the normal lung tissue, (1.78 ± 0.21) × 10(-5) cm s(-1) from the benign granulomatosis lung tissue, (2.88 ± 0.19) × 10(-5) cm s(-1) from the adenocarcinoma tumor and (3.53 ± 0.25) × 10(-5) cm s(-1) from the squamous cell carcinoma, respectively. It could be clearly seen that the permeability coefficients of 30% glucose increase ca 32%, 113% and 162% in the benign granulomatosis, adenocarcinoma tumor and squamous cell carcinoma of human lung tissue compared with that from the normal lung tissue, respectively. Therefore, we inferred from this pilot study that the OCT imaging is a feasible method to distinguish normal and cancer lung tissue.     

Discrimination of Osteonecrosis and Normal Tissues by Near-Infrared Spectroscopy and Successive Projections Algorithm-Linear Discriminant Analysis

Osteonecrosis of femoral head (ONFH) is a disease characterized by an impaired blood flow in the bone. The pathogenesis is still unknown, which makes an exact diagnosis troublesome and heavily dependent on experience. Exploring the information of molecular level by modern spectroscopy may help to discover the underlying pathogenesis and find its diagnostic application in clinical medicine. The study focuses on the combination of near-infrared (NIR) spectroscopy and classification models for discriminating ONFH and normal tissues. A total of 128 surgical specimens was prepared and NIR spectra were recorded by an integrating sphere. The experiment data set was divided into three subsets, i.e., the training set, validation set, and test set. Successive projection algorithm-linear discriminant analysis (SPA-LDA) was used to compress variables and build the diagnostic model. Partial least square-discriminant analysis (PLS-DA) was used as the reference. Principal component analysis (PCA) was used for exploratory analysis. The results showed that compared to PLS-DA, SPA-LDA provided a more parsimonious model using only seven variables and achieved better performance, i.e., sensitivity of 90.5 and 85%, and specificity of 100 and 95.5% for the validation and test sets, respectively. It indicated that NIR spectroscopy combined with SPA-LDA algorithm was a feasible aid tool for discriminating ONFH from normal tissue.     

Evaluation of the accuracy of hTERT gene aberrant methylation using electrochemical hybridization assay and liquid-based cytology in screening for oral squamous cell carcinoma

In order to improve the survival rate of oral squamous cell carcinoma (OSCC) patients, a reliable diagnostic method for early OSCC detection is required that is minimally invasive, less burdensome to the patient, and has high sensitivity and specificity. Therefore, we performed the detection of abnormal methylation at three locations in the hTERT promoter region of oral exfoliated cells by employing the ferrocenylnaphthalene diimide (FND)-based electrochemical hybridization assay (FND-EHA) using three types of DNA probe-immobilized electrodes. We also performed liquid cytology using oral exfoliated cells and compared these obtained data to evaluate whether FND- EHA can be used as an OSCC screening system. The results showed a good correlation between this method and conventional OSCC screening, and cytology. In addition, FND-EHA was also able to determine samples that had been ambiguously determined by liquid cytology. This indicates that FND-EHA may be useful as an OSCC screening system.     

Study of the extraction process and in vivo inhibitory effect of ganoderma triterpenes in oral mucosa cancer

The aim of the reported study was to optimize the extraction process for ganoderma triterpenes and to investigate the in vivo inhibitory effect of ganoderma triterpenes on the genesis and progression of oral cancer. Single-factor and orthogonal methods were used to investigate the effects of extraction solvent, solvent amount, extraction time, extraction temperature, and number of extractions, on the extraction rate for ganoderma triterpenes. A golden hamster model with cheek pouch dynamic canceration was established to receive oral treatment of ganoderma triterpenes water solution. Animals were continuously monitored, oral tissue samples were collected for histopathologic examination, and changes in the expression of VEGF (vascular endothelial growth factor) and Caspase-3 were detected by immunohistochemical methods. Optimization of the experimental conditions allowed the identification of the optimal extraction conditions: 90% ethanol as the extraction solvent, a solvent amount by the liquid-material ratio of 35 mL/g, extraction time of 2 h and extraction temperature of 80 °C. Under these conditions, the average extraction rate of ganoderma triterpenes was 1.09%. Tests in golden hamsters showed that compared with the model group during the same period, animals in the treatment group had better conditions, constantly larger number of normal cases shown by histopathologic results (P < 0.01), and consistently smaller numbers of cases with paraplasm (P < 0.05). Immunohistochemical results showed that compared with the model group, the treatment group had significantly lower (P < 0.05) rates of positive VEGF expression in the normal state, simple epithelial hyperplasia, epithelial dysplasia or squamous cell carcinoma disease stages. Caspase-3 expression showed a tendency toward a gradual increase with the worsening of disease severity in each group. Compared with the model group, the treatment group had significantly lower (P < 0.05) rates of positive Caspase-3 in the normal state, simple epithelial hyperplasia, epithelial dysplasia or squamous cell carcinoma disease grades. Using the optimized extraction process, ganoderma triterpenes could be extracted with high efficiency, and the results of animal tests showed inhibitory effects of ganoderma triterpenes on oral mucosa cancer.     

Fluorescence spectroscopy combined with 5-aminolevulinic acid-induced protoporphyrin IX fluorescence in detecting oral premalignancy

BACKGROUND: Early detection of premalignant/malignant lesions in the oral cavity can certainly improve the patient's prognosis. This study presents fluorescence imaging with the topical application of 5-aminolevulinic as a way to improve detection of various oral tissue pathologies. This procedure depends mainly on comparing the intensity of red and green fluorescence emitted from tissues during examination. MATERIALS AND METHODS: Seventy-one patients who presented with clinically suspicious oral leukoplakia were recruited for this study. Each of the patients was required to have 5-aminolevulinic acid in the form of mouth rinse prior to fluorescence imaging. Following this a surgical biopsy was acquired from the exact examination site. The results of the fluorescence spectroscopy have been compared with histopathology. RESULTS: A Student's t-test was applied to test the viability of the ratio between red and green fluorescence. The red-to-green ratio was found to increase significantly when the lesion was identified as dysplastic or carcinoma in situ. By applying a threshold line to discriminate between normal and dysplastic lesions; a sensitivity of 83-90% and specificity of 79-89% were obtained. CONCLUSION: Fluorescence spectroscopy combined with 5-aminolevulinic acid-induced protoporphyrin IX was found as a valuable tool in the diagnosis of oral premalignancy. This technique offers the potential to be advantageous over other non-optical techniques in terms of providing real-time diagnosis, in situ monitoring, cost effectiveness and more tolerated by patient compared to surgical biopsy.     

Classification of malignant gliomas by infrared spectroscopic imaging and linear discriminant analysis

Infrared (IR) spectroscopy provides a sensitive molecular fingerprint for tissue without external markers. Supervised classification models can be trained to identify the tissue type based on the spectroscopic fingerprint. Infrared imaging spectrometers equipped with multi-channel detectors combine the spectral and spatial information. Tissue areas of 4 x 4 mm(2) can be analyzed within a few minutes in the macroscopic imaging mode. An approach is described to apply this methodology to human astrocytic gliomas, which are graded according to their malignancy from one to four. Multiple IR images of three tissue sections from one patient with a malignant glioma are acquired and assigned to the six classes normal brain tissue, astrocytoma grade II, astrocytoma grade III, glioblastoma multiforme grade IV, hemorrhage, and other tissue by a linear discriminant analysis model which was trained by data from a single-channel detector. Before the model is applied here, the spectra are shown to be virtually identical. The first specimen contained approximately 95% malignant glioma regions, that means astrocytoma grade III or glioblastoma. The smaller percentage of 12-34% malignant glioma in the second specimen is consistent with its location at the tumor periphery. The detection of less than 0.2% malignant glioma in the third specimen points to a location outside the tumor. The results were correlated with the cellularity of the tissue which was obtained from the histopathologic gold standard. Potential applications of IR spectroscopic imaging as a rapid tool to complement established diagnostic methods are discussed.