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

Malini, R.; Venkatakrishna, K.; Kurien, Jacob; Pai, Keerthilatha M.; Rao, Lakshmi; Kartha, V. B.; C, Murali Krishna

doi:10.1002/bip.20398

Cited by 278 publications

(218 citation statements)

References 24 publications

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“…8,11,[34][35][36][37][38][39][40][41][42][43][44] Our data is confirmatory, and, in addition, demonstrates changes in the tumor bed, which probably represent the detection of chemical signs of preneoplastic changes.…”

Section: Discussionsupporting

confidence: 68%

Raman spectroscopy can differentiate malignant tumors from normal breast tissue and detect early neoplastic changes in a mouse model

et al. 2007

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Raman spectroscopy shows potential in differentiating tumors from normal tissue. We used Raman spectroscopy with near‐infrared light excitation to study normal breast tissue and tumors from 11 mice injected with a cancer cell line. Spectra were collected from 17 tumors, 18 samples of adjacent breast tissue and lymph nodes, and 17 tissue samples from the contralateral breast and its adjacent lymph nodes. Discriminant function analysis was used for classification with principal component analysis scores as input data. Tissues were examined by light microscopy following formalin fixation and hematoxylin and eosin staining. Discriminant function analysis and histology agreed on the diagnosis of all contralateral normal, tumor, and mastitis samples, except one tumor which was found to be more similar to normal tissue. Normal tissue adjacent to each tumor was examined as a separate data group called tumor bed. Scattered morphologically suspicious atypical cells not definite for tumor were present in the tumor bed samples. Classification of tumor bed tissue showed that some tumor bed tissues are diagnostically different from normal, tumor, and mastitis tissue. This may reflect malignant molecular alterations prior to morphologic changes, as expected in preneoplastic processes. Raman spectroscopy not only distinguishes tumor from normal breast tissue, but also detects early neoplastic changes prior to definite morphologic alteration. © 2007 Wiley Periodicals, Inc. Biopolymers 89: 235–241, 2008. This article was originally published online as an accepted preprint. The “Published Online”date corresponds to the preprint version. You can request a copy of the preprint by emailing the Biopolymers editorial office at biopolymers@wiley.com

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Section: Discussionsupporting

confidence: 68%

Raman spectroscopy can differentiate malignant tumors from normal breast tissue and detect early neoplastic changes in a mouse model

et al. 2007

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“…Each spectrum is treated as an individual sample. 8,23 In our earlier studies, a total of 258 sites/spectra (105 normal, 101 malignant, 52 benign) were recorded on histopathologically certified 29 normal, 24 infiltrating ductal carcinoma and seven fibroadenoma breast tissues. 21 Among them, randomly selected 36 normal, 35 malignant, and 21 benign breast tissue spectra were used for development of spectroscopic models.…”

Section: Sample Collection and Processingmentioning

confidence: 99%

“…6,8,23,24 In brief, the set up comprises of diode laser; SDL-8530 (785 nm, 100 mW) and HR 320 spectrograph (600 g/mm blazed at 900 nm) coupled to Spectrum One liquid N 2 cooled CCD (Jobin Yvon-Spex, Instruments S.A.) as excitation source and detection system, respectively. A holographic filter (HLBF-785.0, Kaiser Optics) was used to filter the excitation source.…”

Section: Laser Raman Spectroscopymentioning

confidence: 99%

Biochemical correlation of Raman spectra of normal, benign and malignant breast tissues: A spectral deconvolution study

et al. 2009

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The aim of this study was to understand and correlate spectral features and biochemical changes in normal, fibroadenoma and infiltrating ductal carcinoma of breast tissues using Raman spectra that were part of the spectroscopic models developed and evaluated by us earlier. Spectra were subjected to curve fitting and intensities plots of resultant curve resolved bands were computed. This study has revealed that fat (1301 and 1440 cm−1), collagen (1246, 1271, and 1671 cm−1) and DNA (1340 and 1480 cm−1) bands have strong presence in normal, benign and malignant breast tissues, respectively. Intensity plots of various combinations of curved resolved bands were also explored to classify tissue types. Combinations of fat (1301 cm−1) and collagen (1246, 1271, and 1671 cm−1)/amide I; DNA (1340 cm−1) and fat (1301 cm−1); collagen (1271 cm−1) and DNA (1480 cm−1) are found to be good discriminating parameters. These results are in tune with findings of earlier studies carried out on western population as well as our molecular biological understanding of normal tissues and neoplastic processes. Thus the finding of this study further demonstrates the efficacy Raman spectroscopic approaches in diagnostic applications as well as in understanding molecular phenomenon in breast cancers. © 2009 Wiley Periodicals, Inc. Biopolymers 91: 539–546, 2009.This article was originally published online as an accepted preprint. The “Published Online”date corresponds to the preprint version. You can request a copy of the preprint by emailing the Biopolymers editorial office at biopolymers@wiley.com

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“…Broad peaks are observed at amide I and amide III regions in cancer model spectra due to protein dominancy as compared to sharp and weak lipid dominant peaks in normal model spectra (Fig. 3) [4]. PCA results have distinguished different tissue types on the basis of biochemical differences amongst them.…”

Section: Resultsmentioning

confidence: 86%

Raman Spectroscopic Analysis of Normal, Dysplastic and Cancerous Oral Mucosa: A Tissue Engineering Approach

Mian¹,

Colley²,

Ullah³

et al. 2015

USES Conference Proceedings

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Head and neck cancer (HNC) is the sixth most common malignancy worldwide. Squamous cell carcinoma, the primary cause of HNC, evolves from normal epithelium through dysplasia before invading the connective tissue to form a carcinoma. Only 5% of suspicious lesions progress to cancer and diagnosis currently relies on histopathological evaluation, which is invasive and time consuming. A non-invasive, real-time point-of-care method could overcome these problems and facilitate regular screening. Infrared (IR) and Raman spectroscopy (RS) can non-invasively provide information regarding biochemical differences between normal and abnormal tissues. In this study, RS was employed to distinguish between different tissues-engineered models. 3D tissue engineered models of normal, dysplastic and head and neck squamous cell carcinoma (HNSCC) using normal oral keratinocytes, dysplastic (D19, D20 and DOK) and HNSCC cell lines (Cal27 , SCC4 and FaDu) were constructed and their biochemical content predicted by interpretation of their spectral characteristics. Spectral features of normal tissue samples were mainly attributed to lipids, whereas, malignant tissue samples were observed to be protein dominant. Visible differences were found between the spectra of normal, dysplastic and cancerous models, specifically in the bands of amide I and III. The spectra of HNSCC models showed a broad and strong peak of amide I instead of the sharp and weak lipid peak in normal models at band centred at 1667 cm -1. A shift at 2937 cm -1 was only observed in DOK, differentiating them from the other tissue types. Principal Component Analysis (PCA) and Cluster Analysis (CA) distinguished noticeable differences between tissues.

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

Cited by 278 publications

References 24 publications

Raman spectroscopy can differentiate malignant tumors from normal breast tissue and detect early neoplastic changes in a mouse model

Raman spectroscopy can differentiate malignant tumors from normal breast tissue and detect early neoplastic changes in a mouse model

Biochemical correlation of Raman spectra of normal, benign and malignant breast tissues: A spectral deconvolution study

Raman Spectroscopic Analysis of Normal, Dysplastic and Cancerous Oral Mucosa: A Tissue Engineering Approach

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