Feasibility study for diagnosis of stomach adenoma and cancer using IR spectroscopy

The feasibility of infrared (IR) spectroscopy as a biomedical analysis tool for the diagnosis of stomach malignancy including adenoma and cancer has been studied using unstained biopsy samples. Biopsy samples were acquired from 11 subjects. IR spectra were collected for these samples using a microscope (aperture: 25 μm × 25 μm). The samples were stained again and the spots where the IR spectra were collected were re-examined by a pathologist to ensure the spectra represented the correct diagnostic information. The spectral features were compared among the averaged spectra of normal and malignant tissues. The spectral contrasts could be correlated to the differences in the molecular structure of the membrane lipids of the two tissue types as well as the variation in their glycogen contents. However, the spectral features between the adenoma and cancer tissues could not be distinguished. Initially we used principal component analysis (PCA) to examine the degree of separation between tissue types. Soft independent modeling of class analogies (SIMCA) was employed to evaluate the prediction accuracy of IR spectroscopy for the diagnosis of stomach adenoma and cancer. The prediction accuracies for normal, adenoma and cancer tissues were 77%, 30% and 87%, respectively, using SIMCA. IR microscopy successfully differentiated normal and malignant tissues. However, a more sophisticated algorithm will be required in order to effectively extract relevant information for the differentiation between stomach adenoma and cancer.     

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.     

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.     

High-wavenumber FT-Raman spectroscopy for in vivo and ex vivo measurements of breast cancer

The identification of normal and cancer breast tissue of rats was investigated using high-frequency (HF) FT-Raman spectroscopy with a near-infrared excitation source on in vivo and ex vivo measurements. Significant differences in the Raman intensities of prominent Raman bands of lipids and proteins structures (2,800?C3,100?cm^?1) as well as in the broad band of water (3,100?C3,550?cm^?1) were observed in mean normal and cancer tissue spectra. The multivariate statistical analysis methods of principal components analysis (PCA) and linear discriminant analysis (LDA) were performed on all high-frequency Raman spectra of normal and cancer tissues. LDA results with the leave-one-out cross-validation option yielded a discrimination accuracy of 77.2, 83.3, and 100% for in vivo transcutaneous, in vivo skin-removed, and ex vivo biopsy HF Raman spectra. Despite the lower discrimination value for the in vivo transcutaneous measurements, which could be explained by the breathing movement and skin influences, our results showed good accuracy in discriminating between normal and cancer breast tissue samples. To support this, the calculated integration areas from the receiver-operating characteristic (ROC) curve yielded 0.86, 0.94, and 1.0 for in vivo transcutaneous, in vivo skin-removed, and ex vivo biopsy measurements, respectively. The feasibility of using HF Raman spectroscopy as a clinical diagnostic tool for breast cancer detection and monitoring is due to no interfering contribution from the optical fiber in the HF Raman region, the shorter acquisition time due to a more intense signal in the HF Raman region, and the ability to distinguish between normal and cancerous tissues.     

Diagnosis of early stage nasopharyngeal carcinoma using ultraviolet autofluorescence excitation-emission matrix spectroscopy and parallel factor analysis

We report the diagnostic ability of ultraviolet (UV)-excited autofluorescence (AF) excitation-emission matrix (EEM) spectroscopy associated with parallel factor (PARAFAC) analysis for differentiating cancer from normal nasopharyngeal tissue. A bifurcated fiber-optic probe coupled with an EEM system was used to acquire tissue AF EEMs using excitation wavelengths between 260 and 400 nm, and emission collection between 280 and 500 nm. A total of 152 AF EEM landscapes were acquired from 13 normal and 16 nasopharyngeal carcinoma (NPC) thawed ex vivo tissue samples from 23 patients. PARAFAC was introduced for curve resolution of individual AF EEM landscapes associated with the endogenous tissue constituents. The significant factors were further fed to a support vector machine (SVM) and cross-validated to construct diagnostic algorithms. Both the EEM intensity landscapes and the PARAFAC model revealed tryptophan, collagen, and elastin to be the three major endogenous fluorophores responsible for the AF signal from normal and NPC tissues. The EEM intensity distribution and PARAFAC factors suggest an increase of tryptophan and a decrease of collagen and elastin in NPC tissues compared to the normal. The classification results obtained from the PARAFAC-SVM modeling yielded a diagnostic accuracy of 94.7% (sensitivity of 95.0% (76/80); specificity of 94.4% (68/72)) for normal and NPC tissue differentiation. This study suggests that UV-excited AF EEM spectroscopy integrated with PARAFAC algorithms has the potential to provide clinical diagnostics of early onset and progression of NPC.     

Delimitation of squamous cell cervical carcinoma using infrared microspectroscopic imaging

Infrared (IR) spectroscopic imaging coupled with microscopy has been used to investigate thin sections of cervix uteri encompassing normal tissue, precancerous structures, and squamous cell carcinoma. Methods for unsupervised distinction of tissue types based on IR spectroscopy were developed. One-hundred and twenty-two images of cervical tissue were recorded by an FTIR spectrometer with a 64×64 focal plane array detector. The 499,712 IR spectra obtained were grouped by an approach which used fuzzy C-means clustering followed by hierarchical cluster analysis. The resulting false color maps were correlated with the morphological characteristics of an adjacent section of hematoxylin and eosin-stained tissue. In the first step, cervical stroma, epithelium, inflammation, blood vessels, and mucus could be distinguished in IR images by analysis of the spectral fingerprint region (950–1480 cm⁻¹). In the second step, analysis in the spectral window 1420–1480 cm⁻¹ enables, for the first time, IR spectroscopic distinction between the basal layer, dysplastic lesions and squamous cell carcinoma within a particular sample. The joint application of IR microspectroscopic imaging and multivariate spectral processing combines diffraction-limited lateral optical resolution on the single cell level with highly specific and sensitive spectral classification on the molecular level. Compared with previous reports our approach constitutes a significant progress in the development of optical molecular spectroscopic techniques toward an additional diagnostic tool for the early histopathological characterization of cervical cancer.     

Differential Proteomic Analysis of Endometriosis

Differential proteins expressing in ectopic and eutopic endometria were investigated by means of proteomic analysis. Five patients in secretary phase were diagnosed as endometriosis by laparoscopy. The five ectopic endometria(two at stage Ⅱ, two at stage Ⅲ and one at stage IV) and five eutopic endometria were surgically excised. One-dimensional electrophoresis coupled with liquid chromatography and mass spectrometry was used to screen and identify differential proteins. Three differential bands in one-dimensional electrophoresis were resolved by liquid chromatography and mass spectrometry and 14 up-regulated proteins were identified, including collagen α-1, α-2, α-3(VI), α-1(XIV) chain, actin, annexin A2, EMILIN-1, ferritin light polypeptide variant, fucosyltransferase 10, myosin-9, protein S100-A9, KIAA1783 protein, and two hypothetical proteins. Our data provides a list of potential biomarkers for endometriosis. The identifications may be used to develop new diagnoses for endometriosis.     

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.     

Organotypic raft cultures as an effective in vitro tool for understanding Raman spectral analysis of tissue

There is a growing body of evidence showing that optical spectroscopy has the potential to be a useful in vivo diagnostic tool. Yet, so far there is no definitive cellular and biochemical understanding for the differences seen in the spectra from different tissue categories and disease states. In this study, we examine the use of organotypic raft cultures as an in vitro model of in vivo tissue conditions in an attempt to overcome some of the limitations of previously used methods. Organotypic raft cultures resembling normal and dysplastic epithelial cervical tissue were constructed and grown at an air-liquid interface for 2 weeks. Raman spectra of normal as well as dysplastic raft cultures were measured and compared with in vivo spectra from the corresponding tissue type. Histologic comparisons ensured that the raft cultures had similar structure and morphology to the corresponding intact tissue types. Raman spectra were also acquired from different layers of tissue. Spectral comparisons show that the Raman spectra of the raft cultures are similar to the spectra acquired from the cervix in vivo for both normal and dysplastic tissues. These results show that organotypic raft cultures are an effective and useful tool for the cellular and biochemical analysis of tissue spectroscopy.     

Distinction of malignant melanoma and epidermis using IR micro-spectroscopy and statistical methods

Infrared spectroscopy is widely perceived as a future technology for cancer detection and grading. Malignant melanoma, an aggressive skin cancer, is accessible to non-invasive IR radiation based surface probes for its identification and grading. The present work examines the differences in the IR spectra of melanoma tissues and the surrounding epidermis in skin biopsies with the objective of identifying diagnostic parameters and suitable computational/statistical methods of analysis. Melanoma could be differentiated from the epidermis in biopsies of 55 patients, using parameters derived from absorbance bands originating from molecular vibrations of nucleic acids and/or their bases. Additionally, absorbances from tyrosine and phosphate that are abnormally elevated in malignant melanoma could be used as markers. Two-dimensional plots of these parameters in tandem with advanced statistical methods successfully demonstrate the potential of IR spectroscopy to distinguish between epidermal and melanoma regions with a high classification success. The work underlines the importance of developing data analysis methods in FTIR based diagnosis using melanoma as a model system.     

Synthesis of a 99mTc labeled estrogen-derivative and the determination of its radiopharmaceutical potential on rats

An estrogen derivative 1-(3, 17-α-estradiolyl propin-1-yl-3-(1,4,8,11-tetraazacyclotetradecyl)-propanate (ESTACPA) was synthesized. The product was purified by HPLC and characterized by NMR and IR spectroscopy. The synthesized compound was labeled with ^99mTc. The biodistribution studies were performed on female Albino Wistar rats. The rats were sacrificed and their organs were removed. The radioactivities of the organs were counted using a gamma-counter. The activity per gram tissue was calculated and time versus activity curves were generated. The ^99mTc-ESTACPA uptake by the uterus and ovary such as ER-rich tissues, were observed. The pancreas and stomach also showed a significant uptake. This revised version was published online in July 2006 with corrections to the Cover Date.     

Evaluation and discrimination of simvastatin-induced structural alterations in proteins of different rat tissues by FTIR spectroscopy and neural network analysis

Statins are commonly used to control hypercholesterolemia and to prevent cardiovascular diseases. Among the statins, Simvastatin is one of the most frequently prescribed statins because of its efficacy in reducing LDL lipoprotein cholesterol levels, its tolerability, and its reduction of cardiovascular risk and mortality. Conflicting results have been reported with regard to benefits (pleiotropic effects) as well as risks (adverse effects) of simvastatin on different soft and hard tissues. In the current study, Attenuated Total Reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy was used to obtain detailed information about protein conformational changes due to simvastatin therapy of soft tissues namely liver, testis, sciatic nerve and hard tissues such as femur and tibia. Protein secondary structural changes were predicted by intensity calculations from second derivative spectra and neural network (NN) analysis, using the amide I band (1700-1600 cm(-1)) of FTIR spectra. Moreover, based on protein secondary structural differences, hierarchical cluster analysis was carried out in the 1700-1600 cm(-1) region. The results of our study in liver, testis and sciatic nerve tissues revealed that simvastatin treatment significantly decreased alpha helix structure and beta sheet structure at 1638 cm(-1), while increased the anti-parallel and aggregated beta sheet and random coil structures implying a simvastatin-induced protein denaturation in treated groups. Different to soft tissues, the results of hard tissue studies on femur and tibia bones revealed increased alpha helix structure and decreased anti-parallel beta sheet, aggregated beta sheet and random coil structures implying more strengthened bone tissues in simvastatin-treated groups. Finally, the simvastatin-treated and control groups for all soft and bone tissues were successfully differentiated using cluster analysis. According to the heterogeneity values in the cluster analysis of these tissues, the sciatic nerve tissue was found to be the most affected tissue from simvastatin treatment among the studied soft tissues. In addition, the high heterogeneity value implied high secondary structural difference between control and simvastatin-treated groups in tibia bone tissues. These findings reveal that FTIR spectroscopy with bioinformatic analyses such as neural network and hierarchical clustering, allowed us to determine the simvastatin-induced protein conformational changes as adverse and pleiotropic effects of the drug on different soft and hard tissues.     

Detection of collagens in brain tumors based on FTIR imaging and chemometrics

Fourier transform infrared (FTIR) imaging has been used as a molecular histopathology tool on brain tissue sections after intracranial implantation and development of glioma tumors. Healthy brain tissue (contralateral lobe) as well as solid and diffuse tumor tissues were compared for their collagen contents. IR spectra were extracted from IR images for determining the secondary structure of protein contents and compared to pure product spectra of collagens (types I, III, IV, V, and VI). Multivariate statistical analyses of variance and correspondence factorial analysis were performed to differentiate healthy and tumor brain tissues as well as their classification according to their secondary structure profiles. Secondary structure profiles revealed that no collagen was present in healthy tissues; they are also significantly different from solid and diffuse tumors (p < 0.05). Solid and diffuse tumors could be discriminated with respect to the secondary structure profile of fibrillar and non-fibrillar collagens, respectively. We can thus propose to develop FTIR imaging for histopathology examination of tumors on the basis of collagen contents.     

中红外光纤技术用于口腔肿瘤在体原位检测的研究

肿瘤是严重威胁人类健康和生命的疾病,早期诊断和及时治疗是提高肿瘤病人存活率的重要因素,肿瘤的发生和发展一般可分为3个阶段：(1)基因突变;(2)生物分子组成和结构发生改变;(3)细胞和组织形态发生变化,目前常用的影像学方法只能检测较大的肿块,组织标本的病理诊断法需在     

Spectroscopic characterization of human and mouse primary cells,cell lines and malignant cells

Fourier transform infrared (FTIR) spectroscopy is currently being developed as a new optical approach to the diagnosis and characterization of cell or tissue pathology. The advantage of FTIR microspectroscopy over conventional FTIR spectroscopy in the diagnosis of malignancies is that it facilitates inspection of restricted regions of the cell culture or tissue. In this study, we set out to evaluate FTIR microspectroscopy as a diagnostic tool for identifying retrovirus-induced malignancies. Our study showed significant and consistent differences between cultures of different types of cells of both mouse and human origin, i.e. primary fibroblast cells (one to two passages in cell culture), fibroblast cell lines and malignant cells transformed by murine sarcoma virus. An impressive decrease in the levels of phosphate and other metabolites was seen in malignant cells compared with primary cells. The levels of these metabolites in the cell lines were significantly lower than in the primary cells but higher than in the malignant cells. In addition, the peak attributed to the PO2- symmetric stretching mode at 1082 cm(-1) in primary cells shifted significantly to 1085 cm(-1) for the cell line and to 1087 cm(-1) for the malignant cells. These differences taken together with differences in the shapes of various bands throughout the spectrum strongly support the possibility of developing FTIR microspectroscopy for the detection and study of malignant--and possibly premalignant--cells.     

Classification of normal and malignant human gastric mucosa tissue with confocal Raman microspectroscopy and wavelet analysis

Thirty-two samples from the human gastric mucosa tissue, including 13 normal and 19 malignant tissue samples were measured by confocal Raman microspectroscopy. The low signal-to-background ratio spectra from human gastric mucosa tissues were obtained by this technique without any sample preparation. Raman spectral interferences include a broad featureless sloping background due to fluorescence and noise. They mask most Raman spectral feature and lead to problems with precision and quantitation of the original spectral information. A preprocessed algorithm based on wavelet analysis was used to reduce noise and eliminate background/baseline of Raman spectra. Comparing preprocessed spectra of malignant gastric mucosa tissues with those of counterpart normal ones, there were obvious spectral changes, including intensity increase at approximately 1156cm(-1) and intensity decrease at approximately 1587cm(-1). The quantitative criterion based upon the intensity ratio of the approximately 1156 and approximately 1587cm(-1) was extracted for classification of the normal and malignant gastric mucosa tissue samples. This could result in a new diagnostic method, which would assist the early diagnosis of gastric cancer.     

DNA and protein changes caused by disease in human breast tissues probed by the Kubelka-Munk spectral functional

Malignant, fibroadenoma, normal and adipose breast tissues were studied using diffuse reflectance spectroscopy. The absorption spectra of the breast tissues were extracted from the diffuse reflectance spectra using the Kubelka-Munk function (K-M function). The spectral features of the K-M function were identified and compared with those of the absorption spectra. The spectral features of the K-M function were assigned to DNA, protein, beta-carotene and hemoglobin (oxygenated and deoxygenated) molecules in the breast tissue. The amplitudes of the K-M function averaged from 275 to 285 nm and from 255 to 265 nm and were found to be different for malignant, fibroadenoma and normal tissues. These differences were attributed to changes in proteins and DNA. A set of critical parameters was determined for separating malignant tissues from fibroadenoma and normal tissues. This approach should hold for other tissue types such as cervix, uterus and colon.     

经腹彩色多普勒超声检查对子宫内膜异位症的诊断价值

本研究选取我院100例子宫内膜异位症患者作为病例组、同期健康妇女100例作为对照组,探讨了经腹彩色多普勒超声检查对子宫内膜异位症患者子宫动脉血流特征、子宫特征的诊断价值.结果显示,病例组的子宫体积、子宫内膜厚度测定值均大于对照组(P＜0.05);摆动子宫颈后,病例组的EDV测定值低于对照组,RI测定值大于对照组(P＜(...     

Ex vivo and in vivo diagnosis of C6 glioblastoma development by Raman spectroscopy coupled to a microprobe

The potential of Raman spectroscopy for ex vivo and in vivo classification of normal and glioblastoma brain tumor development was investigated. High-quality spectra of normal and tumor tissues were obtained using a portable Raman spectrometer coupled to a microprobe with a signal integration time of 5 s. Ex vivo results demonstrated that by using the biochemical information contained in the spectra, we were able to distinguish between normal brain features (white and gray matter), invasion, and tumor tissues with a classification accuracy of 100%. Differences between these features resulted from variations in their lipid signal contributions, which probably reflect differences in the level of myelinization. This finding supports the ability of in vivo Raman spectroscopy to delineate tumor margins during surgery. After implanting C6 cells in rat brain, we monitored, in vivo, the development of glioblastoma tumor from days 0 to 20 post-implantation (PI). The classification exhibited a clear separation of the data into two clusters: one cluster was associated with normal brain tissues (cortex), and the second was related to data measured from tumor evolution. The second cluster could be divided into two subclusters, one associated with tumor tissue from 4 to 13 days PI and the second related to tumor tissue from 15 to 20 days PI. Histological analysis reveals that the differences between these two subclusters are: the presence of a massive infiltration zone in the brain tissue from 4 to 13 days PI, and; a maturation of the tumor characterized by the appearance of edematous and necrotic zones, as well as a diminution in the proliferative and invasive area, from 15 days. This work demonstrates the potential of Raman spectroscopy to provide diagnostic information for the early detection of tumors in vivo.     

Application of multivariate data-analysis techniques to biomedical diagnostics based on mid-infrared spectroscopy

The objective of this contribution is to review the application of advanced multivariate data-analysis techniques in the field of mid-infrared (MIR) spectroscopic biomedical diagnosis. MIR spectroscopy is a powerful chemical analysis tool for detecting biomedically relevant constituents such as DNA/RNA, proteins, carbohydrates, lipids, etc., and even diseases or disease progression that may induce changes in the chemical composition or structure of biological systems including cells, tissues, and bio-fluids. However, MIR spectra of multiple constituents are usually characterized by strongly overlapping spectral features reflecting the complexity of biological samples. Consequently, MIR spectra of biological samples are frequently difficult to interpret by simple data-analysis techniques. Hence, with increasing complexity of the sample matrix more sophisticated mathematical and statistical data analysis routines are required for deconvoluting spectroscopic data and for providing useful results from information-rich spectroscopic signals. A large body of work relates to the combination of multivariate data-analysis techniques with MIR spectroscopy, and has been applied by a variety of research groups to biomedically relevant areas such as cancer detection and analysis, artery diseases, biomarkers, and other pathologies. The reported results indeed reveal a promising perspective for more widespread application of multivariate data analysis in assisting MIR spectroscopy as a screening or diagnostic tool in biomedical research and clinical studies. While the authors do not mean to ignore any relevant contributions to biomedical analysis across the entire electromagnetic spectrum, they confine the discussion in this contribution to the mid-infrared spectral range as a potentially very useful, yet underutilized frequency region. Selected representative examples without claiming completeness will demonstrate a range of biomedical diagnostic applications with particular emphasis on the advantageous interaction between multivariate data analysis and MIR spectroscopy.