Rapid diagnosis of latent and active pulmonary tuberculosis by autofluorescence spectroscopy of blood plasma combined with artificial neural network algorithm

The existing clinical diagnostic methods of pulmonary tuberculosis (TB) usually have some of the following limitations, such as time-consuming, invasive, radioactive, insufficiently sensitive and accurate. This study demonstrates the possibility of using blood plasma autofluorescence spectroscopy and Artificial Neural Network (ANN) algorithm for the rapid and accurate diagnosis of latent and active pulmonary TB from healthy subjects. The fluorescence spectra of blood plasma from 18 healthy volunteers, 12 individuals with latent TB infections and 80 active TB patients are measured and analyzed. By optimizing the ANN structure and activation functions, the ANN three-classification model achieves average classification accuracy of 96.3 %, and the accuracy of healthy persons, latent TB infections and active TB patients are 100 %, 83.3 % and 97.5 %, respectively, which is much better than the results of traditional Principal component analysis (PCA) and Linear discriminant analysis (LDA) method. To the best of our knowledge, this is the first research work of differentiating latent, active pulmonary TB cases from healthy samples with autofluorescence spectroscopy. As a rapid, accurate, safe, label-free, non-invasive and cost-effective technique, it can be developed as a promising diagnostic tool for the screening of pulmonary TB disease in the early stage.

Molecular fingerprint of precancerous lesions in breast atypical hyperplasia

OBJECTIVE

To identify atypical hyperplasia (AH) of the breast by shell-isolated nanoparticle-enhanced Raman spectroscopy (SHINERS), and to explore the molecular fingerprinting characteristics of breast AH.

METHODS

Breast hyperplasia was studied in 11 hospitals across China from January 2015 to December 2016. All patients completed questionnaires on women's health. The differences between patients with and without breast AH were compared. AH breast lesions were detected by Raman spectroscopy followed by the SHINERS technique.

RESULTS

There were no significant differences in clinical features and risk-related factors between patients with breast AH (n = 37) and the control group (n = 2576). Fifteen cases of breast AH lesions were detected by Raman spectroscopy. The main different Raman peaks in patients with AH appeared at 880, 1001, 1086, 1156, 1260, and 1610 cm^-1, attributed to the different vibrational modes of nucleic acids, β-carotene, and proteins. Shell-isolated nanoparticles had different enhancement effects on the nucleic acid, protein, and lipid components in AH.

CONCLUSION

Raman spectroscopy can detect characteristic molecular changes in breast AH lesions, and may thus be useful for the non-invasive early diagnosis and for investigating the mechanism of tumorigenesis in patients with breast AH.

Evaluation of aromatic amino acids as potential biomarkers in breast cancer by Raman spectroscopy analysis

The scope of the work undertaken in this paper was to explore the feasibility and reliability of using the Raman signature of aromatic amino acids as a marker in the detection of the presence of breast cancer and perhaps, even the prediction of cancer development in very early stages of cancer onset. To be able to assess this hypothesis, we collected most recent and relevant literature in which Raman spectroscopy was used as an analytical tool in the evaluation of breast cell lines and breast tissue, re-analyzed all the Raman spectra, and extracted all spectral bands from each spectrum that were indicative of aromatic amino acids. The criteria for the consideration of the various papers for this study, and hence, the inclusion of the data that they contained were two-fold: (1) The papers had to focus on the characterization of breast tissue with Raman spectroscopy, and (2) the spectra provided within these papers included the spectral range of 500-1200 cm-1, which constitutes the characteristic region for aromatic amino acid vibrational modes. After all the papers that satisfied these criteria were collected, the relevant spectra from each paper were extracted, processed, normalized. All data were then plotted without bias in order to decide whether there is a pattern that can shed light on a possible diagnostic classification. Remarkably, we have been able to demonstrate that cancerous breast tissues and cells decidedly exhibit overexpression of aromatic amino acids and that the difference between the extent of their presence in cancerous cells and healthy cells is overwhelming. On the basis of this analysis, we conclude that it is possible to use the signature Raman bands of aromatic amino acids as a biomarker for the detection, evaluation and diagnosis of breast cancer.

Differences and Relationships Between Normal and Atypical Ductal Hyperplasia, Ductal Carcinoma In Situ, and Invasive Ductal Carcinoma Tissues in the Breast Based on Raman Spectroscopy

The aim of this study was to find the differences and relationships between normal, atypical ductal hyperplasia (ADH), ductal carcinoma in situ (DCIS), and invasive ductal carcinoma (IDC) lesions of the breast based on biochemical characteristics determined by Raman spectroscopy (RS). After collecting 39 frozen sections from patients who underwent surgical resection or mammotome biopsy, nine normal tissues, seven ADH, eight DCIS, and 15 IDC lesions were detected using confocal RS. We then used leave-one-out cross-validation (LOOCV) and radial basis function (RBF) to build a support vector machine (SVM) diagnosis model. Pronounced mean Raman spectra differences were observed between normal tissues, ADH, DCIS, and IDC tissues. Most noticeable was the increased protein and reduced lipid levels of ADH tissues compared to normal tissues. The major spectra differences in ADH, DCIS, and IDC spectrograms were evidenced by a red shift with a broad peak of CH2 (1301 cm^-1), the intensity of the stretching vibration peak of carotenoids (1526 cm^-1), a relatively strong band of amide-I (1656 cm^-1), and the nuclear (882 cm^-1) acid peak. Atypical ductal hyperplasia tissues had the largest constituent variations between subjects. During the disease progression, IDC tissues have smaller inter-subject constituent variations than DCIS and ADH tissues. The overall accuracy of SVM model is 74.39%. The sensitivities of normal tissue, ADH, DCIS, and IDC are 62.5%, 50%, 90%, and 66.7%, respectively. The specificities of normal tissue, ADH, DCIS, and IDC are 100%, 100%, 66.7%, and 89.06%, respectively. Atypical ductal hyperplasia shows significant differences and the relationship between normal tissue and malignant disease. Further study to explain the biochemical relationships between these differences will shed more light into a better understanding of the mechanism by which ADH converts to DCIS and to IDC.

Rapid Discrimination of Malignant Breast Lesions from Normal Tissues Utilizing Raman Spectroscopy System: A Systematic Review and Meta-Analysis of In Vitro Studies

Purpose

The aim of this study is to evaluate the diagnostic accuracy of Raman spectroscopy system in the detection of malignant breast lesions through a systemic review and meta-analysis of published studies.

Methods

We conducted a comprehensive literature search of PubMed and Embase from 2000 to June 2015. Published studies that evaluated the diagnostic performance of Raman spectroscopy in distinguishing malignant breast lesions from benign lesions and normal tissues were included in our study. The pooled sensitivity, specificity, diagnostic odds ratio, and the area under the curve of summary receiver-operating characteristic curves was derived. A Revised Tool for the Quality Assessment of Diagnostic Accuracy Studies guidelines was used to assess the quality of included studies.

Results

The initial search produced a total of 157 articles after removing duplicates. Nine studies (8 in vitro and 1 in vivo) were eligible in this meta-analysis. We analyzed the eight in vitro studies with 1756 lesions, the pooled sensitivity and specificity of Raman spectroscopy system for the diagnosis of malignant breast lesions were 0.92 (95% CI 0.86–0.96) and 0.97 (97% CI 0.93–0.98), respectively. Diagnostic odds ratio was 266.70 (95% CI 89.38–795.79), and the area under the curve of summary receiver-operating characteristic curves was 0.98 (95% CI 0.97–0.99). Significant heterogeneity was found between studies. There was no evidence of considerable publication bias.

Conclusions

Raman spectroscopy system is an optical diagnostic technology with great value for detecting malignant breast lesions. At the same time, it has advantages of being non-invasive, real-time, and easy to use. Thus it deserves to be further explored for intra-operatory breast tumor margin detection.

Pursuing shell-isolated nanoparticle-enhanced Raman spectroscopy (SHINERS) for concomitant detection of breast lesions and microcalcifications

Although tissue staining followed by morphologic identification remains the gold standard for diagnosis of most cancers, such determinations relying solely on morphology are often hampered by inter- and intra-observer variability. Vibrational spectroscopic techniques, in contrast, offer objective markers for diagnoses and can afford disease detection prior to alterations in cellular and extracellular architecture by furnishing a rapid "omics"-like view of the biochemical status of the probed specimen. Here, we report a classification approach to concomitantly detect microcalcification status and local pathological state in breast tissue, featuring a combination of vibrational spectroscopy that focuses on the tumor and its microenvironment, and multivariate data analysis of spectral markers reflecting molecular expression. We employ the unprecedented sensitivity and exquisite molecular specificity offered by Au@SiO2 shell-isolated nanoparticle-enhanced Raman spectroscopy (SHINERS) to probe the presence of calcified deposits and distinguish between normal breast tissues, fibroadenoma, atypical ductal hyperplasia, ductal carcinoma in situ (DCIS), and invasive ductal carcinoma (IDC). By correlating the spectra with the corresponding histologic assessment, we developed partial least squares-discriminant analysis derived decision algorithm that provides excellent diagnostic power in the fresh frozen sections (overall accuracy of 99.4% and 93.6% using SHINs for breast lesions with and without microcalcifications, respectively). The performance of this decision algorithm is competitive with or supersedes that of analogous algorithms employing spontaneous Raman spectroscopy while enabling facile detection due to the considerably higher intensity of SHINERS. Our results pave the way for rapid tissue spectral pathology measurements using SHINERS that can offer a novel stain-free route to accurate and economical diagnoses without human interpretation.

Advantages and limitations of Raman spectroscopy for molecular diagnostics: an update

Over the last decade, Raman spectroscopy has gained more and more interest in research as well as in clinical laboratories. As a vibrational spectroscopy technique, it is complementary to the also well-established infrared spectroscopy. Through specific spectral patterns, substances can be identified and molecular changes can be observed with high specificity. Because of a high spatial resolution due to an excitation wavelength in the visible and near-infrared range, Raman spectroscopy combined with microscopy is very powerful for imaging biological samples. Individual cells can be imaged on the subcellular level. In vivo tissue examinations are becoming increasingly important for clinical applications. In this review, we present currently ongoing research in different fields of medical diagnostics involving linear Raman spectroscopy and imaging. We give a wide overview over applications for the detection of atherosclerosis, cancer, inflammatory diseases and pharmacology, with a focus on developments over the past 5 years. Conclusions drawn from Raman spectroscopy are often validated by standard methods, for example, histopathology or PCR. The future potential of Raman spectroscopy and its limitations are discussed in consideration of other non-linear Raman techniques.

Photoacoustic imaging of breast microcalcifications: a preliminary study with 8-gauge core-biopsied breast specimens

Background

We presented the photoacoustic imaging (PAI) tool and to evaluate whether microcalcifications in breast tissue can be detected on photoacoustic (PA) images.

Methods

We collected 21 cores containing microcalcifications (n = 11, microcalcification group) and none (n = 10, control group) in stereotactic or ultrasound (US) guided 8-gauge vacuum-assisted biopsies. Photoacoustic (PA) images were acquired through ex vivo experiments by transmitting laser pulses with two different wavelengths (700 nm and 800 nm). The presence of microcalcifications in PA images were blindly assessed by two radiologists and compared with specimen mammography. A ratio of the signal amplitude occurring at 700 nm to that occurring at 800 nm was calculated for each PA focus and was called the PAI ratio.

Results

Based on the change of PA signal amplitude between 700 nm and 800 nm, 10 out of 11 specimens containing microcalcifications and 8 out of 10 specimens without calcifications were correctly identified on blind review; the sensitivity, specificity, accuracy, positive predictive and negative predictive values of our blind review were 90.91%, 80.0%, 85.71%, 83.33% and 88.89%. The PAI ratio in the microcalcification group was significantly higher than that in the control group (the median PAI ratio, 2.46 versus 1.11, respectively, P = .001). On subgroup analysis in the microcalcification group, neither malignant diagnosis nor the number or size of calcification-foci was proven to contribute to PAI ratios.

Conclusion

Breast microcalcifications generated distinguishable PA signals unlike breast tissue without calcifications. So, PAI, a non-ionizing and non-invasive hybrid imaging technique, can be an alternative in overcoming the limitations of conventional US imaging.

The use of Au@SiO2 shell-isolated nanoparticle-enhanced Raman spectroscopy for human breast cancer detection

This study uses the powerful fingerprint features of Raman spectroscopy to distinguish different types of breast tissues including normal breast tissues (NB), fibroadenoma (FD), atypical ductal hyperplasia (ADH), ductal carcinoma in situ (DCIS), and invasive ductal carcinoma (IDC). Thin frozen tissue sections of fresh breast tissues were measured by Raman spectroscopy. Due to the inherent low sensitivity of Raman spectra, Au@SiO2 shell-isolated nanoparticle-enhanced Raman spectroscopy (SHINERS) technique was utilized to provide supplementary and more informative spectral features. A total of 619 Raman spectra were acquired and compared to 654 SHINERS spectra. The maximum enhancement effect of distinct and specific bands was characterized for different tissue types. When applying the new criteria, excellent separation of FD, DCIS, and IDC was obtained for all tissue types. Most importantly, we were able to distinguish ADH from DCIS. Although only a preliminary distinction was characterized between ADH and NB, the results provided a good foundation of criteria to further discriminate ADH from NB and shed more light toward a better understanding of the mechanism of ADH formation. This is the first report to detect the premalignant (ADH and DCIS) breast tissue frozen sections and also the first report exploiting SHINERS to detect and distinguish breast tissues. The results presented in this study show that SHINERS can be applied to accurately and efficiently identify breast lesions. Further, the spectra can be acquired in a minimally invasive procedure and analyzed rapidly facilitating early and accurate diagnosis in vivo/in situ.

Application of Raman spectroscopy to identify microcalcifications and underlying breast lesions at stereotactic core needle biopsy

Microcalcifications are a feature of diagnostic significance on a mammogram and a target for stereotactic breast needle biopsy. Here, we report development of a Raman spectroscopy technique to simultaneously identify microcalcification status and diagnose the underlying breast lesion, in real-time, during stereotactic core needle biopsy procedures. Raman spectra were obtained ex vivo from 146 tissue sites from fresh stereotactic breast needle biopsy tissue cores from 33 patients, including 50 normal tissue sites, 77 lesions with microcalcifications, and 19 lesions without microcalcifications, using a compact clinical system. The Raman spectra were modeled on the basis of the breast tissue components, and a support vector machine framework was used to develop a single-step diagnostic algorithm to distinguish normal tissue, fibrocystic change (FCC), fibroadenoma, and breast cancer, in the absence and presence of microcalcifications. This algorithm was subjected to leave-one-site-out cross-validation, yielding a positive predictive value, negative predictive value, sensitivity, and specificity of 100%, 95.6%, 62.5%, and 100% for diagnosis of breast cancer (with or without microcalcifications) and an overall accuracy of 82.2% for classification into specific categories of normal tissue, FCC, fibroadenoma, or breast cancer (with and without microcalcifications). Notably, the majority of breast cancers diagnosed are ductal carcinoma in situ (DCIS), the most common lesion associated with microcalcifications, which could not be diagnosed using previous Raman algorithm(s). Our study shows the potential of Raman spectroscopy to concomitantly detect microcalcifications and diagnose associated lesions, including DCIS, and thus provide real-time feedback to radiologists during such biopsy procedures, reducing nondiagnostic and false-negative biopsies.

Tumor cell differentiation by label-free fluorescence microscopy

Autofluorescence spectra, images, and decay kinetics of U251-MG glioblastoma cells prior and subsequent to activation of tumor suppressor genes are compared. While phase contrast images and fluorescence intensity patterns of tumor (control) cells and less malignant cells are similar, differences can be deduced from autofluorescence spectra and decay kinetics. In particular, upon near UV excitation, the fluorescence ratio of the free and protein-bound coenzyme nicotinamid adenine dinucleotide depends on the state of malignancy and reflects different cytoplasmic (including lysosomal) and mitochondrial contributions. While larger numbers of fluorescence spectra are evaluated by principal component analysis, a multivariate data analysis method, additional information on cell metabolism is obtained from spectral imaging and fluorescence lifetime imaging microscopy.

Precision of Raman spectroscopy measurements in detection of microcalcifications in breast needle biopsies

Microcalcifications are an early mammographic sign of breast cancer and a target for stereotactic breast needle biopsy. We developed Raman spectroscopy decision algorithms to detect breast microcalcifications, based on fit coefficients (FC) derived by modeling tissue Raman spectra as a linear combination of the Raman spectra of 9 chemical and morphologic components of breast tissue. However, little or no information is available on the precision of such measurements and its effect on the ability of Raman spectroscopy to make predictions for breast microcalcification detection. Here we report the precision, that is, the closeness of agreement between replicate Raman spectral measurements--and the model FC derived from them--obtained ex vivo from fresh breast biopsies from patients undergoing stereotactic breast needle biopsy, using a compact clinical Raman system. The coefficients of variation of the model FC averaged 0.03 for normal breast tissue sites, 0.12 for breast lesions without, and 0.22 for breast lesions with microcalcifications. Imprecision in the FC resulted in diagnostic discordance among replicates only for line-sitters, that is, tissue sites with FC values near the decision line or plane. The source of this imprecision and their implications for the use of Raman spectroscopy for guidance of stereotactic breast biopsies for microcalcifications are also discussed. In summary, we conclude that the precision of Raman spectroscopy measurements in breast tissue obtained using our compact clinical system is more than adequate to make accurate and repeatable predictions of microcalcifications in breast tissue using decision algorithms based on model FC. This provides strong evidence of the potential of Raman spectroscopy guidance of stereotactic breast needle biopsies for microcalcifications.

Raman spectroscopy of normal oral buccal mucosa tissues: study on intact and incised biopsies

Oral squamous cell carcinoma is one of among the top 10 malignancies. Optical spectroscopy, including Raman, is being actively pursued as alternative/adjunct for cancer diagnosis. Earlier studies have demonstrated the feasibility of classifying normal, premalignant, and malignant oral ex vivo tissues. Spectral features showed predominance of lipids and proteins in normal and cancer conditions, respectively, which were attributed to membrane lipids and surface proteins. In view of recent developments in deep tissue Raman spectroscopy, we have recorded Raman spectra from superior and inferior surfaces of 10 normal oral tissues on intact, as well as incised, biopsies after separation of epithelium from connective tissue. Spectral variations and similarities among different groups were explored by unsupervised (principal component analysis) and supervised (linear discriminant analysis, factorial discriminant analysis) methodologies. Clusters of spectra from superior and inferior surfaces of intact tissues show a high overlap; whereas spectra from separated epithelium and connective tissue sections yielded clear clusters, though they also overlap on clusters of intact tissues. Spectra of all four groups of normal tissues gave exclusive clusters when tested against malignant spectra. Thus, this study demonstrates that spectra recorded from the superior surface of an intact tissue may have contributions from deeper layers but has no bearing from the classification of a malignant tissues point of view.

Raman spectroscopic methods for classification of normal and malignant hypopharyngeal tissues: an exploratory study

Laryngeal cancer is more common in males. The present study is aimed at exploration of potential of conventional Raman spectroscopy in classifying normal from a malignant laryngopharyngeal tissue. We have recorded Raman spectra of twenty tissues (aryepiglottic fold) using an in-house built Raman setup. The spectral features of mean malignant spectrum suggests abundance proteins whereas spectral features of mean normal spectrum indicate redundancy of lipids. PCA was employed as discriminating algorithm. Both, unsupervised and supervised modes of analysis as well as match/mismatch "limit test" methodology yielded clear classification among tissue types. The findings of this study demonstrate the efficacy of conventional Raman spectroscopy in classification of normal and malignant laryngopharyngeal tissues. A rigorous evaluation of the models with development of suitable fibreoptic probe may enable real-time Raman spectroscopic diagnosis of laryngopharyngeal cancers in future.

Development of a spatially offset Raman spectroscopy probe for breast tumor surgical margin evaluation

The risk of local recurrence for breast cancers is strongly correlated with the presence of a tumor within 1 to 2 mm of the surgical margin on the excised specimen. Previous experimental and theoretical results suggest that spatially offset Raman spectroscopy (SORS) holds much promise for intraoperative margin analysis. Based on simulation predictions for signal-to-noise ratio differences among varying spatial offsets, a SORS probe with multiple source-detector offsets was designed and tested. It was then employed to acquire spectra from 35 frozen-thawed breast tissue samples in vitro. Spectra from each detector ring were averaged to create a composite spectrum with biochemical information covering the entire range from the tissue surface to ∼2 mm below the surface, and a probabilistic classification scheme was used to classify these composite spectra as "negative" or "positive" margins. This discrimination was performed with 95% sensitivity and 100% specificity, or with 100% positive predictive value and 94% negative predictive value.

Laser raman spectroscopy as a potential chair-side microbiological diagnostic device

INTRODUCTION

Culture-dependent and -independent techniques are time-consuming processes requiring highly trained personnel to identify microorganisms contained within a sample. Rapid chair-side identification of microorganisms could reduce the lag time between patient presentation and ideal treatment. As a first step toward this goal, this study aims to determine if laser Raman spectroscopy (LRS) can discern uniqueness among 10 different species of bacteria contained within a medium in unprocessed and processed samples.

METHODS

Ten bacterial species were individually grown on blood agar plates for 3 days. Checkerboard DNA-DNA hybridization was used for species verification. For the unprocessed samples, a 1.0-cm diameter agar sample, with undisturbed bacterial growth, was transferred for each species to a barium fluoride crystal (BaF(2)) slide and laser scanned for a total of 15 seconds per sample. For the processed samples, bacterial cells were harvested, washed, and resuspended in phosphate-buffered saline buffer at 10(9) cells/mL concentration. Each suspension was laser scanned for 15 seconds on a BaF(2) slide. Select regions of Raman spectra for each species/agar and species/suspension combination were processed using a two-sided t test.

RESULTS

For the 10 bacterial species, 45 bacteria pair combinations were tested for each group. In both groups, LRS was capable of statistically distinguishing among a majority of bacterial pairings based on RS signature differences of means.

CONCLUSIONS

Results show each bacterial species generated restricted ranges of unique spectral signatures that were not masked by their containing medium. Chair-side LRS is a promising technique that differentiates among oral bacterial species with a high degree of specificity.

Comparative evaluation of spectroscopic models using different multivariate statistical tools in a multicancer scenario

Cancer is now recognized as one of the major causes of morbidity and mortality. Histopathological diagnosis, the gold standard, is shown to be subjective, time consuming, prone to interobserver disagreement, and often fails to predict prognosis. Optical spectroscopic methods are being contemplated as adjuncts or alternatives to conventional cancer diagnostics. The most important aspect of these approaches is their objectivity, and multivariate statistical tools play a major role in realizing it. However, rigorous evaluation of the robustness of spectral models is a prerequisite. The utility of Raman spectroscopy in the diagnosis of cancers has been well established. Until now, the specificity and applicability of spectral models have been evaluated for specific cancer types. In this study, we have evaluated the utility of spectroscopic models representing normal and malignant tissues of the breast, cervix, colon, larynx, and oral cavity in a broader perspective, using different multivariate tests. The limit test, which was used in our earlier study, gave high sensitivity but suffered from poor specificity. The performance of other methods such as factorial discriminant analysis and partial least square discriminant analysis are at par with more complex nonlinear methods such as decision trees, but they provide very little information about the classification model. This comparative study thus demonstrates not just the efficacy of Raman spectroscopic models but also the applicability and limitations of different multivariate tools for discrimination under complex conditions such as the multicancer scenario.

Chemometrics in metabolomics--a review in human disease diagnosis

Metabolomics is a post genomic research field concerned with developing methods for analysis of low molecular weight compounds in biological systems, such as cells, organs or organisms. Analyzing metabolic differences between unperturbed and perturbed systems, such as healthy volunteers and patients with a disease, can lead to insights into the underlying pathology. In metabolomics analysis, large amounts of data are routinely produced in order to characterize samples. The use of multivariate data analysis techniques and chemometrics is a commonly used strategy for obtaining reliable results. Metabolomics have been applied in different fields such as disease diagnosis, toxicology, plant science and pharmaceutical and environmental research. In this review we take a closer look at the chemometric methods used and the available results within the field of disease diagnosis. We will first present some current strategies for performing metabolomics studies, especially regarding disease diagnosis. The main focus will be on data analysis strategies and validation of multivariate models, since there are many pitfalls in this regard. Further, we highlight the most interesting metabolomics publications and discuss these in detail; additional studies are mentioned as a reference for the interested reader. A general trend is an increased focus on biological interpretation rather than merely the ability to classify samples. In the conclusions, the general trends and some recommendations for improving metabolomics data analysis are provided.

Autofluorescence of breast tissues: evaluation of discriminating algorithms for diagnosis of normal, benign, and malignant conditions

OBJECTIVE

We evaluated different discriminating algorithms for classifying laser-induced fluorescence spectra of normal, benign, and malignant breast tissues that were obtained with 325-nm excitation.

BACKGROUND DATA

Mammography and histopathology are the conventional gold standard methods of screening and diagnosis of breast cancers, respectively. The former is prone to a high rate of false-positive results and poses the risk of repeated exposure to ionizing radiation, whereas the latter suffers from subjective interpretations of morphological features. Thus the development of a more reliable detection and screening methodology is of great interest to those practicing breast cancer management. Several studies have demonstrated the efficacy of optical spectroscopy in diagnosing cancer and other biomedical applications.

MATERIALS AND METHODS

Autofluorescence spectra of normal, benign, and malignant breast tissues, with 325-nm excitation, were recorded. The data were subjected to diverse discriminating algorithms ranging from intensities and ratios of curve-resolved bands to principal components analysis (PCA)-derived parameters.

RESULTS

Intensity plots of collagen and NADPH, two known fluorescent biomarkers, yielded accurate classification of the different tissue types. PCA was carried out on both unsupervised and supervised methods, and both approaches yielded accurate classification. In the case of the supervised classification, the developed standard sets were verified and evaluated. The limit test approach provided unambiguous and objective classification, and this method also has the advantage of being user-friendly, so untrained personnel can directly compare unknown spectra against standard sets to make diagnoses instantly, objectively, and unambiguously.

CONCLUSION

The results obtained in this study further support the efficacy of 325-nm-induced autofluorescence, and demonstrate the suitability of limit test analysis as a means of objectively and unambiguously classifying breast tissues.

Protein profile study of breast-tissue homogenates by HPLC-LIF

Proteomics is a promising approach for molecular understanding of neoplastic processes including response to treatment. Widely used 2D-gel electrophoresis/Liquid chromatography coupled with mass spectrometry (LC-MS) are time consuming and not cost effective. We have developed a high-sensitivity (femto/subfemtomoles of protein/20 mul) High Performance Liquid Chromatography-Laser Induced Fluorescence HPLC-LIF instrument for studying protein profiles of biological samples. In this study, we have explored the feasibility of classifying breast tissues by multivariate analysis of chromatographic data. We have analyzed 13 normal, 17 malignant, 5 benign and 4 post-treatment breast-tissue homogenates. Data was analyzed by Principal Component Analysis PCA in both unsupervised and supervised modes on derivative and baseline-corrected chromatograms. Our findings suggest that PCA of derivative chromatograms gives better classification. Thus, the HPLC-LIF instrument is not only suitable for generation of chromatographic data using femto/subfemto moles of proteins but the data can also be used for objective diagnosis via multivariate analysis. Prospectively, identified fractions can be collected and analyzed by biochemical and/or MS methods.

Spatially offset Raman spectroscopy of layered soft tissues

Raman spectroscopy has been widely used for cancer diagnosis, but conventional forms provide limited depth information. Spatially offset Raman spectroscopy (SORS) can solve the depth issue, but it has only been used to detect hard tissues such as bone. We explore the feasibility of using SORS to discriminate two layers of soft tissue. Measurements were taken with individual source and detector fibers at a number of spatial offsets from samples consisting of various thicknesses of normal human breast tissues overlying breast tumors. Results show that SORS can detect tumors beneath normal tissue, marking, to the best of our knowledge, the first application of SORS for discriminating two layers of soft tissue.