Over the past decade, dramatic increases in computational power and improvement in image analysis algorithms have allowed the development of powerful computer-assisted analytical approaches to radiological data. With the recent advent of whole slide digital scanners, tissue histopathology slides can now be digitized and stored in digital image form. Consequently, digitized tissue histopathology has now become amenable to the application of computerized image analysis and machine learning techniques. Analogous to the role of computer-assisted diagnosis (CAD) algorithms in medical imaging to complement the opinion of a radiologist, CAD algorithms have begun to be developed for disease detection, diagnosis, and prognosis prediction to complement the opinion of the pathologist. In this paper, we review the recent state of the art CAD technology for digitized histopathology. This paper also briefly describes the development and application of novel image analysis technology for a few specific histopathology related problems being pursued in the United States and Europe.

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Histopathological Image Analysis: A Review

This article reviews some of the recent advances in Raman spectroscopy, in areas related to natural tissues and cell biology. It summarizes some of the most widely used peak frequencies and their assignments. The aim of this study is to prepare a database of molecular fingerprints, which will help researchers in defining the chemical structure of the biological tissues introducing most of the important peaks present in the natural tissues. In spite of applying different methods, there seems to be a considerable similarity in defining the peaks of identical areas of the spectra. As a result, it is believed that preparing a unique collection of the frequencies encountered in Raman spectroscopic studies can lead to significant improvements both in the quantity and quality of spectral data and their outcomes. This article is the first review of its kind to provide a precise database on the most important Raman characteristic peak frequencies for researchers aiming to analyze natural tissues by Raman ...

Raman spectroscopy of biological tissues

Bone is a hierarchically structured material with remarkable mechanical performance which may serve as a model for the development of biomimetic materials. Understanding its properties is essential for the assessment of diseases such as osteoporosis. This will lead to a critical evaluation of current therapies and aid in their more targeted development. While the full hierarchical structure of bone is extremely complex and variable, its basic building block, the mineralized collagen fibril, is rather universal. Due to the progress in experimental methods to characterize materials at the nanoscale, new insights have been gained into the structure/mechanical function relation in this nanocomposite. The amount of mineral is usually thought to determine the stiffness of the material, but recent results suggest that the properties of the organic matrix as well as the geometrical arrangement of the two components might have a much larger influence on the properties than traditionally assumed. Some recent results from experiment and numerical modeling leading to these ideas are reviewed.

Structure and mechanical quality of the collagen–mineral nano-composite in bone

http://xrm.phys.northwestern.edu/research/pdf_papers/2006/chan_biophysj_2006.pdf

Micro-Raman Spectroscopy Detects Individual Neoplastic and Normal Hematopoietic Cells

Chemometrics in metabolomics—A review in human disease diagnosis

Optical spectroscopy methods are fast emerging as potential alternatives for early diagnosis of cancer. A Raman spectroscopy method for discrimination of normal and malignant oral tissues has been developed by us earlier. It is necessary to evaluate and establish the validity of the approach before it can be routinely used. In the present study, our Raman spectroscopy investigations are extended further to evaluate the efficacy of the technique to discriminate between normal, inflammatory, premalignant, and malignant conditions in oral tissue. Spectral profiles of normal, malignant, premalignant, and inflammatory conditions show pronounced differences between one another. Spectra of normal tissues can be attributed mainly to lipids whereas pathological tissue spectra are dominated by proteins. Principal components analysis (PCA) of the spectral data sets belonging to the four different categories showed that scores of factors differentiated between normal and all pathological conditions but gave only poor discrimination among the three pathological states. PCA combined with multiparameter limit tests allow match/mismatch criteria to be applied to test samples when pathologically certified calibration sets are available in each class. It is shown that by this method all the four tissue types could be discriminated and diagnosed correctly. The biochemical differences between normal and pathological conditions of oral tissue are also discussed from spectral differences of the different classes of spectra.

https://www.onlinelibrary.wiley.com/doi/pdf/10.1002/bip.20398

Discrimination of normal, inflammatory, premalignant, and malignant oral tissue: A Raman spectroscopy study

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.

Discrimination of normal, benign, and malignant breast tissues by Raman spectroscopy.

Micro-Raman spectra of formalin-fixed oral squamous normal and carcinoma tissues, stored at room temperature for 2 months, have been recorded. Spectra were recorded both in the epithelial and subepithelial regions of the tissues. No noticeable spectral contamination due to formalin was observed. Very significant differences between spectra of normal epithelial and malignant epithelial samples were found. No such differences in spectra of subepithelial malignant and subepithelial normal samples could be observed. This study shows that spectra from the epithelial region changes drastically because of malignancy-induced biochemical changes in this region. Major differences between normal and malignant spectra seem to arise from the protein composition, conformational/structural changes, and possible increase in protein content in malignant epithelia. The differences between normal epithelial and subepithelial spectra, as expected, arise mainly from the collagen in subepithelial tissue. Principal component analysis of the combined sets of spectra-epithelial and subepithelial, normal and malignant- showed that very good discrimination can be achieved by Raman microspectroscopy. This study thus validates the suitability of formalin-fixed tissues for optical pathology in oral malignancy.

Micro-Raman spectroscopy for optical pathology of oral squamous cell carcinoma.

Raman and fluorescence spectroscopy methods are being considered as techniques which could be complementary or even alternative to biopsy, and pathology and clinical assays in many medical applications. The present paper discusses the results of Raman spectral studies on oral tissues for optical pathology. It is shown that Raman spectra of oral tissues can be classified into spectra of normal and malignant sets and a model based on such a classification can be used to analyse oral tissue for detection of oral malignancy. Sensitivity and specificity calculated from 90 test spectra are better than 85 and 90 per cent respectively.

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Optical pathology of oral tissue: A raman spectroscopy diagnostic method

Objective: The objective of this study was to evaluate the applicability of the discrimination parameters Mahalanobis distance, spectral residuals, and limit tests, developed by this group to differentiate normal from malignant colon tissues. Background Data: Colon cancers are diagnosed using fiberoptic endoscopic localization and a subsequent histopathological examination of biopsied tissue, which is highly dependent on the skill and experience of the investigator. There exists a risk of missing significant lesions, especially with carcinoma in situ lesions. Raman spectroscopy, which is sensitive to biochemical variations in the samples and amenable to multivariate statistical tools, can lead to rapid and objective detection of colon cancer. Methods: A total of 102 spectra from 11 normal and 11 malignant ex vivo colon tissues were recorded by conventional near infrared (NIR) Raman spectroscopy (excitation wavelength of 785 nm). Spectral data were analyzed by principal components analysis (PCA) and other ...

Jacob Kurien

Papers

Discrimination of normal, inflammatory, premalignant, and malignant oral tissue: A Raman spectroscopy study

Discrimination of normal, benign, and malignant breast tissues by Raman spectroscopy.

Micro-Raman spectroscopy for optical pathology of oral squamous cell carcinoma.

Optical pathology of oral tissue: A raman spectroscopy diagnostic method

Discrimination of normal and malignant mucosal tissues of the colon by Raman spectroscopy.