Histopathological Image Analysis: A Review <s> 4) Spectral Analysis for Histopathology: <s> Breast biopsy samples were examined with Raman spectroscopy with laser wavelengths ranging from 406 to 830 nm. A combination of a single-stage spectrograph, band reject filter, and CCD detector permitted low laser powers and minimal risk of sample radiation damage. Spectra of formalin-fixed human tissue revealed Raman features for lipids and carotenoids. The best defined lipid features were observed for 782- and 830-nm laser excitation, while carotenoid features were strongest in the 488-515-nm range due to resonance enhancement. Comparison of the spectra with those of fatty acid esters revealed that the major lipid component is a derivative of oleic acid. Lipid and carotenoid Raman bands were superimposed on a luminescent background which was less prominent at longer laser wavelengths. A compact, portable, diode laser spectrometer was tested in a clinical setting with fiber optic sampling. The results indicate that substantial biochemical information is available from near-IR Raman spectroscopy and the technique may have clinical applications. <s> BIB001 </s> Histopathological Image Analysis: A Review <s> 4) Spectral Analysis for Histopathology: <s> Four analytical methods have been applied to infrared microscopic maps of human skin which contained basal cell carcinoma tumors—namely, point spectroscopy, grayscale functional group mapping, digital staining, and fuzzy C-means (FCM) cluster analysis. Spectroscopic interpretation using point spectroscopy allowed discrimination between normal and tumor-bearing skin components. Functional group mapping provided information on the distribution of lipids, proteins, nucleic acids, and collagen within skin sections. While functional group mapping only allowed examination of one material (or a ratio of two materials) at a time, a new approach termed digital staining allowed visualization of the distribution of up to three materials at one time. Digital staining thus gave a more detailed understanding of the tissue section. Finally, application of FCM cluster analysis, a nonsubjective, unsupervised classification methodology, allowed us to group spectra into five distinct clusters, which correlated to distinct tissue components in skin. Tumor-bearing skin was clearly separated from normal skin. When different analytical methods are used in conjunction, a unique understanding of tissues can be obtained. Our results demonstrate that infrared microscopy has potential for the pathological diagnosis of skin tumors. © 1999 John Wiley & Sons, Inc. Biospectroscopy 5: 265–275, 1999 <s> BIB002 </s> Histopathological Image Analysis: A Review <s> 4) Spectral Analysis for Histopathology: <s> A noninvasive tool for skin tumor diagnosis would be a useful clinical adjunct. The purpose of this study was to determine whether near-infrared spectroscopy can be used to noninvasively characterize skin lesions. In vivo visible- and near-infrared spectra (400 2500 nm) of skin neoplasms (actinic keratoses, basal cell carcinomas, banal common acquired melanocytic nevi, dysplastic melanocytic nevi, actinic lentigines, and seborrheic keratoses) were collected by placing a fiberoptic probe on the skin. Paired t tests, repeated measures analysis of variance and linear discriminant analysis were used to determine whether significant spectral differences existed and whether spectra could be classified according to lesion type. Paired t tests showed significant differences (p <s> BIB003 </s> Histopathological Image Analysis: A Review <s> 4) Spectral Analysis for Histopathology: <s> Raman spectroscopy is a vibrational spectroscopic technique that can be used to optically probe the molecular changes associated with diseased tissues. The objective of our study was to explore near-infrared (NIR) Raman spectroscopy for distinguishing tumor from normal bronchial tissue. Bronchial tissue specimens (12 normal, 10 squamous cell carcinoma (SCC) and 6 adenocarcinoma) were obtained from 10 patients with known or suspected malignancies of the lung. A rapid-acquisition dispersive-type NIR Raman spectroscopy system was used for tissue Raman studies at 785 nm excitation. High-quality Raman spectra in the 700-1,800 cm(-1) range from human bronchial tissues in vitro could be obtained within 5 sec. Raman spectra differed significantly between normal and malignant tumor tissue, with tumors showing higher percentage signals for nucleic acid, tryptophan and phenylalanine and lower percentage signals for phospholipids, proline and valine, compared to normal tissue. Raman spectral shape differences between normal and tumor tissue were also observed particularly in the spectral ranges of 1,000-1,100, 1,200-1,400 and 1,500-1,700 cm(-1), which contain signals related to protein and lipid conformations and nucleic acid's CH stretching modes. The ratio of Raman intensities at 1,445 to 1,655 cm(-1) provided good differentiation between normal and malignant bronchial tissue (p < 0.0001). The results of this exploratory study indicate that NIR Raman spectroscopy provides significant potential for the noninvasive diagnosis of lung cancers in vivo based on the optic evaluation of biomolecules. <s> BIB004 </s> Histopathological Image Analysis: A Review <s> 4) Spectral Analysis for Histopathology: <s> Breast cancers are the leading cancers among females. Diagnosis by fine needle aspiration cytology (FNAC) is the gold standard. The widely practiced screening method, mammography, suffers from high false positive results and repeated exposure to harmful ionizing radiation. As with all other cancers survival rates are shown to heavily depend on stage of the cancers (Stage 0, 95%; Stage IV, 75%). Hence development of more reliable screening and diagnosis methodology is of considerable interest in breast cancer management. Raman spectra of normal, benign, and malignant breast tissue show significant differences. Spectral differences between normal and diseased breast tissues are more pronounced than between the two pathological conditions, malignant and benign tissues. Based on spectral profiles, the presence of lipids (1078, 1267, 1301, 1440, 1654, 1746 cm(-1)) is indicated in normal tissue and proteins (stronger amide I, red shifted DeltaCH2, broad and strong amide III, 1002, 1033, 1530, 1556 cm(-1)) are found in benign and malignant tissues. The major differences between benign and malignant tissue spectra are malignant tissues seem to have an excess of lipids (1082, 1301, 1440 cm(-1)) and presence of excess proteins (amide I, amide III, red shifted DeltaCH2, 1033, 1002 cm(-1)) is indicated in benign spectra. The multivariate statistical tool, principal components analysis (PCA) is employed for developing discrimination methods. A score of factor 1 provided a reasonable classification of all three tissue types. The analysis is further fine-tuned by employing Mahalanobis distance and spectral residuals as discriminating parameters. This approach is tested both retrospectively and prospectively. The limit test, which provides the most unambiguous discrimination, is also considered and this approach clearly discriminated all three tissue types. These results further support the efficacy of Raman spectroscopic methods in discriminating normal and diseased breast tissues. <s> BIB005 </s> Histopathological Image Analysis: A Review <s> 4) Spectral Analysis for Histopathology: <s> OBJECTIVE ::: Fourier-transform (FT)-Raman spectroscopy has been used to explore the changes in the vibrational bands of normal, dysplastic (DE) and squamous cell carcinoma (SCC) tissues. ::: ::: ::: BACKGROUND DATA ::: Raman spectroscopy has been applied as a diagnostic tool for the detection of cancers, due to its sensitivity to the changes in molecular composition and conformation that occurs in malignant tissues. The detection of weak Raman signals from biotissues becomes easier by FT-Raman due to fluorescence suppression. ::: ::: ::: METHODS ::: A carcinogen (7,12-dimethybenz[a]anthracene [DMBA]) was applied daily in the oral pouch of 21 hamster to induce oral carcinoma. After 14 weeks, the fragments of squamous cell carcinomas and oral normal tissue were collected and analyzed by FT-Raman spectroscopy, using a 1064-nm Nd:YAG laser line as an excitation source. A total of 123 spectra were obtained and divided in normal and malignant tissue groups, and analyzed statistically through principal components analysis (PCA) and classified using Mahalanobis distance. ::: ::: ::: RESULTS ::: Major differences between normal and malignant spectra seem to arise from the composition, conformational, and structural changes of proteins, and possible increase of its content in malignant epithelia. An algorithm based on PCA was able to separate the samples into two groups--normal and carcinoma. For the algorithm training group, 91% sensitivity and 69% specificity were observed, while the prospective group had 100% sensitivity and 55% specificity. ::: ::: ::: CONCLUSION ::: The algorithm based on PCA has the potential for classifying Raman spectra and can be useful for detection of dysplastic and malign oral lesion. <s> BIB006 </s> Histopathological Image Analysis: A Review <s> 4) Spectral Analysis for Histopathology: <s> BACKGROUND ::: Cytomics involves the analysis of cellular morphology and molecular phenotypes, with reference to tissue architecture and to additional metadata. To this end, a variety of imaging and nonimaging technologies need to be integrated. Spectral imaging is proposed as a tool that can simplify and enrich the extraction of morphological and molecular information. Simple-to-use instrumentation is available that mounts on standard microscopes and can generate spectral image datasets with excellent spatial and spectral resolution; these can be exploited by sophisticated analysis tools. ::: ::: ::: METHODS ::: This report focuses on brightfield microscopy-based approaches. Cytological and histological samples were stained using nonspecific standard stains (Giemsa; hematoxylin and eosin (H&E)) or immunohistochemical (IHC) techniques employing three chromogens plus a hematoxylin counterstain. The samples were imaged using the Nuance system, a commercially available, liquid-crystal tunable-filter-based multispectral imaging platform. The resulting data sets were analyzed using spectral unmixing algorithms and/or learn-by-example classification tools. ::: ::: ::: RESULTS ::: Spectral unmixing of Giemsa-stained guinea-pig blood films readily classified the major blood elements. Machine-learning classifiers were also successful at the same task, as well in distinguishing normal from malignant regions in a colon-cancer example, and in delineating regions of inflammation in an H&E-stained kidney sample. In an example of a multiplexed ICH sample, brown, red, and blue chromogens were isolated into separate images without crosstalk or interference from the (also blue) hematoxylin counterstain. ::: ::: ::: CONCLUSION ::: Cytomics requires both accurate architectural segmentation as well as multiplexed molecular imaging to associate molecular phenotypes with relevant cellular and tissue compartments. Multispectral imaging can assist in both these tasks, and conveys new utility to brightfield-based microscopy approaches. <s> BIB007 </s> Histopathological Image Analysis: A Review <s> 4) Spectral Analysis for Histopathology: <s> PURPOSE ::: To evaluate proton magnetic resonance (MR) spectroscopic imaging with high spatial resolution for preoperative grading of suspected World Health Organization grades II and III gliomas. ::: ::: ::: MATERIALS AND METHODS ::: Institutional ethics committee approval and informed consent were obtained for control subjects but were not required for the retrospective component involving patients. Twenty-six patients (10 women, 16 men; mean age, 37.5 years) suspected of having gliomas and 26 age- and sex-matched control subjects underwent proton MR spectroscopy. Absolute metabolite concentrations for choline-containing compounds (Cho), creatine (Cr), and N-acetylaspartate (NAA)-N-acetylaspartylglutamate (total NAA [tNAA]) were calculated by using a user-independent spectral fit program. Metabolic maps of Cho/tNAA ratios were calculated, segmented, and used for MR spectroszpcopy-guided stereotactic brain biopsy. Two-sided paired Student t tests were used to test for statistical significance. ::: ::: ::: RESULTS ::: Significantly lower Cho levels (P = .002) and higher tNAA levels (P = .010) were found in grade II tumors (n = 9) compared with grade III tumors (n = 17). The average Cho/tNAA ratio over the voxels in the tumor center showed a distinct difference (P < .001) between grade II and III gliomas at a threshold of 0.8 (with ratios <0.8 for grade II). The maximum Cr concentration in the tumor showed a clear-cut threshold between grade III oligodendrogliomas and oligoastrocytomas (Cr level, <7 mmol/L) and grade III astrocytomas (Cr level, >7 mmol/L; P = .020). Comparison between the histopathologic findings from the MR spectroscopy-guided biopsy samples (76 biopsies from 26 patients) and molar metabolite values in corresponding voxels located at the biopsy sampling points showed a negative linear correlation for tNAA (r = -0.905) and a positive exponential correlation for Cho (r = 0.769) and Cho/tNAA (r = 0.885). ::: ::: ::: CONCLUSION ::: Proton MR spectroscopic imaging with high spatial resolution allows preoperative grading of gliomas. <s> BIB008 </s> Histopathological Image Analysis: A Review <s> 4) Spectral Analysis for Histopathology: <s> Raman spectroscopy has been shown to have the potential for providing differential diagnosis in the cervix with high sensitivity and specificity in previous studies. The research presented here further evaluates the potential of near-infrared Raman spectroscopy to detect cervical dysplasia in a clinical setting. Using a portable system, Raman spectra were collected from the cervix of 79 patients using clinically feasible integration times (5 seconds on most patients). Multiple Raman measurements were taken from colposcopically normal and abnormal areas prior to the excision of tissue. Data were processed to extract Raman spectra from measured signal, which includes fluorescence and noise. The resulting spectra were correlated with the corresponding histopathologic diagnosis to determine empirical differences between different diagnostic categories. Using histology as the gold standard, logistic regression discrimination algorithms were developed to distinguish between normal ectocervix, squamous metaplasia, and high-grade dysplasia using independent training and validation sets of data. An unbiased estimate of the accuracy of the model indicates that Raman spectroscopy can distinguish between high-grade dysplasia and benign tissue with sensitivity of 89% and specificity of 81%, while colposcopy in expert hands was able to discriminate with a sensitivity and specificity of 87% and 72%. <s> BIB009 </s> Histopathological Image Analysis: A Review <s> 4) Spectral Analysis for Histopathology: <s> Fourier transform infrared (FTIR) spectroscopy provides a unique molecular fingerprint of tissue from endogenous sources of light absorption; however, specific molecular components of the overall FTIR signature of precancer have not been characterized. In attenuated total reflectance mode, infrared light penetrates only a few microns of the tissue surface, and the influence of water on the spectra can be minimized, allowing for the analyses of the molecular composition of tissues. Here, spectra were collected from 98 excised specimens of the distal esophagus, including 38 squamous, 38 intestinal metaplasia (Barrett's), and 22 gastric, obtained endoscopically from 32 patients. We show that DNA, protein, glycogen, and glycoprotein comprise the principal sources of infrared absorption in the 950- to 1,800-cm(-1) regime. The concentrations of these biomolecules can be quantified by using a partial least-squares fit and used to classify disease states with high sensitivity, specificity, and accuracy. Moreover, use of FTIR to detect premalignant (dysplastic) mucosa results in a sensitivity, specificity, positive predictive value, and total accuracy of 92%, 80%, 92%, and 89%, respectively, and leads to a better interobserver agreement between two gastrointestinal pathologists for dysplasia (kappa = 0.72) versus histology alone (kappa = 0.52). Here, we demonstrate that the concentration of specific biomolecules can be determined from the FTIR spectra collected in attenuated total reflectance mode and can be used for predicting the underlying histopathology, which will contribute to the early detection and rapid staging of many diseases. <s> BIB010 </s> Histopathological Image Analysis: A Review <s> 4) Spectral Analysis for Histopathology: <s> BackgroundWe present an analysis of the utility of multispectral versus standard RGB imagery for routine H&E stained histopathology images, in particular for pixel-level classification of nuclei. Our multispectral imagery has 29 spectral bands, spaced 10 nm within the visual range of 420–700 nm. It has been hypothesized that the additional spectral bands contain further information useful for classification as compared to the 3 standard bands of RGB imagery. We present analyses of our data designed to test this hypothesis.ResultsFor classification using all available image bands, we find the best performance (equal tradeoff between detection rate and false alarm rate) is obtained from either the multispectral or our "ccd" RGB imagery, with an overall increase in performance of 0.79% compared to the next best performing image type. For classification using single image bands, the single best multispectral band (in the red portion of the spectrum) gave a performance increase of 0.57%, compared to performance of the single best RGB band (red). Additionally, red bands had the highest coefficients/preference in our classifiers. Principal components analysis of the multispectral imagery indicates only two significant image bands, which is not surprising given the presence of two stains.ConclusionOur results indicate that multispectral imagery for routine H&E stained histopathology provides minimal additional spectral information for a pixel-level nuclear classification task than would standard RGB imagery. <s> BIB011 </s> Histopathological Image Analysis: A Review <s> 4) Spectral Analysis for Histopathology: <s> Magnetic resonance spectroscopy (MRS) has been shown to have great clinical potential as a supplement to magnetic resonance imaging in the detection of prostate cancer (CaP). MRS provides functional information in the form of changes in the relative concentration of specific metabolites including choline, creatine, and citrate which can be used to identify potential areas of CaP. With a view to assisting radiologists in interpretation and analysis of MRS data, some researchers have begun to develop computer-aided detection (CAD) schemes for CaP identification from spectroscopy. Most of these schemes have been centered on identifying and integrating the area under metabolite peaks which is then used to compute relative metabolite ratios. However, manual identification of metabolite peaks on the MRspectra, and especially via CAD, is a challenging problem due to low signal-to-noise ratio, baseline irregularity, peak overlap, and peak distortion. In this article the authors present a novel CAD scheme that integrates nonlinear dimensionality reduction (NLDR) with an unsupervised hierarchical clustering algorithm to automatically identify suspicious regions on the prostate using MRS and hence avoids the need to explicitly identify metabolite peaks. The methodology comprises two stages. In stage 1, a hierarchical spectralclustering algorithm is used to distinguish between extracapsular and prostatic spectra in order to localize the region of interest (ROI) corresponding to the prostate. Once the prostate ROI is localized, in stage 2, a NLDR scheme, in conjunction with a replicated clustering algorithm, is used to automatically discriminate between three classes of spectra (normal appearing, suspicious appearing, and indeterminate). The methodology was quantitatively and qualitatively evaluated on a total of 18 1.5 T in vivo prostate T2-weighted (w) and MRS studies obtained from the multisite, multi-institutional American College of Radiology (ACRIN) trial. In the absence of the precise ground truth for CaP extent on the MRimaging for most of the ACRIN studies, probabilistic quantitative metrics were defined based on partial knowledge on the quadrant location and size of the tumor. The scheme, when evaluated against this partial ground truth, was found to have a CaP detection sensitivity of 89.33% and specificity of 79.79%. The results obtained from randomized threefold and fivefold cross validation suggest that the NLDR based clustering scheme has a higher CaP detection accuracy compared to such commonly used MRS analysis schemes as z score and PCA. In addition, the scheme was found to be robust to changes in system parameters. For 6 of the 18 studies an expert radiologist laboriously labeled each of the individual spectra according to a five point scale, with 1 ∕ 2 representing spectra that the expert considered normal and 3 ∕ 4 ∕ 5 being spectra the expert deemed suspicious. When evaluated on these expert annotated datasets, the CAD system yielded an average sensitivity (cluster corresponding to suspicious spectra being identified as the CaP class) and specificity of 81.39% and 64.71%, respectively. <s> BIB012
In IR spectroscopy, McIntosh et al. BIB002 investigated the use of infrared spectroscopy for the characterization of in vitro basal cell carcinoma (BCC) specimens, exploiting the fact that mid-IR light is absorbed by a variety of skin components. Point spectroscopy was performed using an IR spectrometer and an aperture of 20 m 20 m from carefully selected regions containing only one type of skin lesion. Their analysis of the normalized spectra employing linear discriminant analysis (LDA) identified absorption bands that arise mainly from CH2 and CH3 absorptions in dermal spectra that are similar to those seen in samples rich in protein and collagen in particular. H&E staining for standard histological examination was carried out after the spectra had been obtained. In a more recent paper, McIntosh et al. BIB003 utilized LDA to analyze the near-IR (NIR) absorption spectrum for noninvasive, in vivo characterization of skin neoplasms. Their rationale for using NIR light was that the mid-IR light could be completely absorbed by samples greater than 10-15 m in thickness, therefore limiting the utility of mid-IR spectroscopy to in vitro analysis. In Raman spectroscopy, Frank et al. BIB001 examined Raman spectra from breast and observed that visible laser excitation could be used to reveal Raman features for lipids and carotenoids. Huang et al. BIB004 explored the use of a rapid acquisition NIR Raman spectroscopy system for in vitro diagnosis of lung cancer. Student's -test was performed to discriminate between normal and malignant bronchial tissues using the ratio of Raman intensity at two specific wavelengths. Chowdary et al. BIB005 showed that the Raman spectra could be useful for discriminating between normal and diseased breast tissues, although a simple principle component analysis (PCA) of spectra was employed for discrimination purposes. Analyzing the Raman spectra of malignant breast tissues, they concluded that malignant tissues had an excess of lipids and proteins. Robichaux-Viehoever et al. BIB009 investigated the use of NIR Raman spectra for the detection of cervical dysplasia and achieved high correlation between the results of their spectral analysis and the histopathology diagnosis. Recently, Wang et al. BIB010 have shown that FT-IR spectroscopy can be effectively used for detecting premalignant (dysplastic) mucosa and leads to better inter-observer agreement, in terms of the -statistic. Oliveira et al. BIB006 have explored a setup involving a fixed-wavelength (1064 nm) laser line as an excitation source and FT-Raman for generating the spectra. Spectral analysis using PCA and Mahalanobis distance were used to detect dysplastic and malignant oral lesions. Their results using LDA showed effective separation of spectra of benign lesions from those of premalignant and malignant lesions. Over the years, MS and HS imaging have demonstrated an enormous potential in remote-sensing applications, leading many researchers to expect promise about their usefulness in histopathology BIB007 . This promise has been demonstrated by and  in their work on the diagnosis of colon adenocarcinoma. However, two recent studies BIB011 ,  have found that the additional spectral information does not significantly improve the classification results. This may be due to the fact that most MS and HS imaging methods employ the visible part of light spectrum which may not be very useful in terms of highlighting important biochemical characteristics, as opposed to the near-IR or mid-IR spectrum. The number of stains present in the sample, as well as the characteristics of the stains themselves will also directly affect the performance of MS and HS image analysis methods. In MRS, several studies, such as BIB008 , can be found in the literature that report high correlation between automatic grading of in vivo tumors and their corresponding post-operative histopathology findings. However, MRS spectral analysis has traditionally been limited to rather simplistic ratio tests. Tiwari et al. BIB012 recently proposed an unsupervised spectral clustering-based algorithm for diagnosis of prostate cancer from the MRS spectra, reporting higher specificity compared to the popular -score scheme, routinely used for the analysis of MRS data. Spectral analysis using different modalities discussed above has demonstrated its potential for diagnosis and grading of cancer in tissue samples. However, as outlined above, most of the proposed algorithms in the literature make use of linear sub-space projection methods (PCA, LDA, etc.) for analysis of the spectral data despite the fact that spectral signatures of different types of tissue in the high-dimensional space may not be linearly separable. Furthermore, a few challenges are limiting the success of such methods in the realm of optical diagnostics. These include, but are not limited to, storage and transmission, registration, processing and analysis of large amounts of data generated by spectral data acquisition methods. The problem is further compounded when gathering spectral imaging data for whole slides. However, the availability of powerful computational resources at increasingly low prices and recent advances in image analysis have meant that more sophisticated methods can now be applied for analyzing large amounts of spectral data.