Application of FT–IR microspectroscopy in diagnosing thyroid neoplasms

Metabolic Profile Characterization of Different Thyroid Nodules Using FTIR Spectroscopy: A Review

Thyroid cancer’s incidence has increased in the last decades, and its diagnosis can be a challenge. Further and complementary testing based in biochemical alterations may be important to correctly identify thyroid cancer and prevent unnecessary surgery. Fourier-transform infrared (FTIR) spectroscopy is a metabolomic technique that has already shown promising results in cancer metabolome analysis of neoplastic thyroid tissue, in the identification and classification of prostate tumor tissues and of breast carcinoma, among others. This work aims to gather and discuss published information on the ability of FTIR spectroscopy to be used in metabolomic studies of the thyroid, including discriminating between benign and malignant thyroid samples and grading and classifying different types of thyroid tumors.

Metabolic fingerprinting as a diagnostic tool

Within the framework of systems biology, functional analyses at all 'omic levels have seen an intense level of activity during the first decade of the twenty-first century. These include genomics, transcriptomics, proteomics, metabolomics and lipidomics. It could be said that metabolomics offers some unique advantages over the other 'omics disciplines and one of the core approaches of metabolomics for disease diagnostics is metabolic fingerprinting. This review provides an overview of the main metabolic fingerprinting approaches used for disease diagnostics and includes: infrared and Raman spectroscopy, Nuclear magnetic resonance (NMR) spectroscopy, followed by an introduction to a wide range of novel mass spectrometry-based methods, which are currently under intense investigation and developmental activity in laboratories worldwide. It is hoped that this review will act as a springboard for researchers and clinicians across a wide range of disciplines in this exciting era of multidisciplinary and novel approaches to disease diagnostics.

Metabolic fingerprinting in disease diagnosis: biomedical applications of infrared and Raman spectroscopy

The ability to diagnose the early onset of disease, rapidly, non-invasively and unequivocally has multiple benefits. These include the early intervention of therapeutic strategies leading to a reduction in morbidity and mortality, and the releasing of economic resources within overburdened health care systems. Some of the routine clinical tests currently in use are known to be unsuitable or unreliable. In addition, these often rely on single disease markers which are inappropriate when multiple factors are involved. Many diseases are a result of metabolic disorders, therefore it is logical to measure metabolism directly. One of the strategies employed by the emergent science of metabolomics is metabolic fingerprinting; which involves rapid, high-throughput global analysis to discriminate between samples of different biological status or origin. This review focuses on a selective number of recent studies where metabolic fingerprinting has been forwarded as a potential tool for disease diagnosis using infrared and Raman spectroscopies.

Characterization of human cervical precancerous tissue through the fourier transform infrared microscopy with mapping method

OBJECTIVE

The aim of this study was to evaluate directly the utility of Fourier transform infrared microscopy (FTIR-MC) in detecting cervical precancer by comparison with the pathological examination of biopsy tissue.

METHODS

Cervical biopsy specimens were obtained from the outpatient department at Kaohsiung Municipal Ta-Tung Hospital. Freshly isolated tissue was washed in isotonic saline and then frozen for use. Two successive slices were cut with a microtome. One unstained slice was used for reflectance FTIR-MC analysis; the other slice was stained with hematoxylin and eosin and used for position reference by microscopic mapping.

RESULTS

The spectral region from 950 to 1500 cm(-1) is the most critical region in the IR spectrum of the tissue. There were two special peaks that had growth and decline in the IR spectra. The one at 1150 cm(-1) disappeared and the other at 1240 cm(-1) broadened when the tissue had pathological changes. From the results, we found that the ratio of the areas of the two regions between 1130 to 1180 cm(-1) and 1180 to 1260 cm(-1) was an exceptionally useful factor in discriminating precancerous tissues from normal tissues.

CONCLUSION

We carried out microscopic mapping of the tissues and demonstrated that the color map reflects pathological changes in the cervical tissues.

The potential role of Fourier transform infrared spectroscopy and imaging in cancer diagnosis incorporating complex mathematical methods

Infrared spectroscopy and imaging technology can provide new diagnostic capabilities for cancer research applications and to physicians directly involved in patient treatment. A methodology that permits the phenotypical description of cells and tissues is introduced, as well as a variety of new evaluation techniques that allow researchers and physicians to evaluate the infrared data at different levels of sophistication. The detection of cell alterations in the case of Chronic Lymphocytic Leukemia (CLL) demonstrates the capability of infrared spectroscopy to identify and stage this type of cancer, providing new prospects for diagnosis and treatment. The pathological study of oral tissue affected by Squamous Cell Carcinoma (SCC) illustrates the potential of Infrared Spectroscopy and Imaging for tissue diagnosis and cancer staging based in altered cell biochemistry, without using stains or any other marker technology. An example of combined fluid, cell and tissue analysis of thyroid cancers based on infrared technology is introduced to demonstrate the possibility of earlier detection of gland abnormalities and biochemical alterations in cell extracts using fine needle aspirates. Simple statistical techniques such as bivariate histogram analysis can distinguish between normal and altered cells and tissues when applied to infrared spectra and images. More complex mathematical techniques such as Principal Component Analysis (PCA) or Artificial Neural Networks (ANN) provide additional evaluation capabilities that can relate spectra of an unknown sample to an infrared reference database of known cell states. It is documented how this new infrared technology could enhance the diagnoses, treatment decisions and prognoses of patients in the field of cancer medicine.