Raman Spectroscopy for Early Detection of Laryngeal Malignancy: Preliminary Results

Stone, Edwin M.; Stavroulaki, Pelagia; Kendall, Catherine; Birchall, Martin A.; Barr, Hugh

doi:10.1097/00005537-200010000-00037

Cited by 209 publications

(215 citation statements)

References 27 publications

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“…The qualitative findings of reduced glycogen content and increased nucleic acid content in malignant, compared to benign pathologies, fit in with the findings of previous studies of the larynx (Stone et al, 2000), colon , oesophagus Figure 3 The mean Raman spectra recorded from BPH and all prostate cancers studied.…”

Section: Discussionsupporting

confidence: 87%

The use of Raman spectroscopy to identify and grade prostatic adenocarcinoma in vitro

et al. 2003

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Raman spectroscopy is an optical technique, which provides a measure of the molecular composition of tissue. Raman spectra were recorded in vitro from both benign and malignant prostate biopsies, and used to construct a diagnostic algorithm. The algorithm was able to correctly identify each pathological group studied with an overall accuracy of 89%. The technique shows promise as a method for objectively grading prostate cancer.

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Section: Discussionsupporting

confidence: 87%

The use of Raman spectroscopy to identify and grade prostatic adenocarcinoma in vitro

et al. 2003

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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: 67%

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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“…In the past decade, optical spectroscopic methods such as Raman spectroscopy have been comprehensively investigated for cancer and precancer diagnosis and evaluation (3)(4)(5)(6)(7)(8)(9)(10)(11)(12). Raman spectroscopy measures inelastic light scattering and is a vibrational spectroscopic technique that can provide specific spectroscopic fingerprints based on the molecular composition and structures of biological tissues (3,4,(8)(9)(10).…”

Section: Introductionmentioning

confidence: 99%

“…Less water absorption makes it easy to detect other tissue components and results in deeper light penetration into the tissue. As such, NIR Raman spectroscopy has received great interest for in vivo and in vitro diagnosis of malignancies in a number of organs including the colon (3,(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15). NIR Raman spectroscopy studies showed that tissue Raman spectral features could be used to correlate with the molecular and structural changes associated with neoplastic transformations (5,7,10,12), demonstrating the feasibility of NIR Raman spectroscopy technique for early cancer detection.…”

Section: Introductionmentioning

confidence: 99%

Classification of colonic tissues using near-infrared Raman spectroscopy and support vector machines

Widjaja

Zheng²,

Huang³

2008

Int J Oncol

111

137

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Abstract. The ability of combining near-infrared (NIR) Raman spectroscopy with support vector machines (SVM) for improving multi-class classification between different histopathological groups in tissues was evaluated in this study. A total of 105 colonic tissue specimens from 59 patients including 41 normal, 18 hyperplastic polyps and 46 adenocarcinomas were used for this purpose. A rapid-acquisition dispersive-type NIR Raman system was utilized for tissue Raman spectroscopic measurements at 785-nm laser excitation. A total of 817 tissue Raman spectra were acquired and subjected to principal components analysis (PCA) for SVM-based multi-class classification, in which 324 Raman spectra were from normal, 184 from polyps and 309 from adenocarcinomatous colonic tissue. Two types of SVM (i.e., C-SVM and ν-SVM) with three different kernel functions (linear, polynomial and Gaussian radial basis function (RBF) in combination with PCA were used to develop effective diagnostic algorithms for classification of Raman spectra of different colonic tissues. The performance of various SVMbased algorithms was evaluated and compared using a leave-one-out, cross-validation method. The results showed that in the C-SVM classification, the maximum overall diagnostic accuracy of 99.3, 99.4 and 99.9% can be achieved using the linear, polynomial and RBF kernels, respectively; while in the ν-SVM classification, the maximum overall diagnostic accuracy of 98.4, 98.5 and 99.6% can be obtained using the linear, polynomial and RBF kernels, respectively. All the polyps can be identified from normal and adenocarcinomatous tissue using the C-SVM algorithms. The RBF C-SVM algorithm was proven to be the best classifier for providing the highest diagnostic accuracy (99.9%) for multi-class classification. This study demonstrates that NIR Raman spectroscopy in combination with a powerful SVM technique has great potential for providing an effective and accurate diagnostic schema for cancer diagnosis in the colon.

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Raman Spectroscopy for Early Detection of Laryngeal Malignancy: Preliminary Results

Cited by 209 publications

References 27 publications

The use of Raman spectroscopy to identify and grade prostatic adenocarcinoma in vitro

The use of Raman spectroscopy to identify and grade prostatic adenocarcinoma in vitro

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

Classification of colonic tissues using near-infrared Raman spectroscopy and support vector machines

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