In Vivo Detection of Dysplastic Tissue by Raman Spectroscopy

Schut, Tom C. Bakker; Witjes, Max J. H.; Sterenborg, Henricus J. C. M.; Speelman, Otto C.; Roodenburg, Jan; Marple, Eric; Bruining, H. A.; Puppels, Gerwin J.

doi:10.1021/ac000780u

Cited by 179 publications

(121 citation statements)

References 27 publications

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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%

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

Widjaja

Zheng²,

Huang³

2008

Int J Oncol

117

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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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Widjaja

Zheng²,

Huang³

2008

Int J Oncol

117

137

View full text Add to dashboard Cite

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“…9 -12 The accurate detection of BCC has attracted much attention from clinical dermatologists since it is difficult to distinguish BCC tissue from surrounding noncancerous tissue. [13][14][15][16] The routine diagnostic technique used for the detection of BCC is pathological examination of biopsy samples. This involves removal of tissue from suspected areas, which is then sliced and stained to enable the pathologist to identify morphological abnormalities.…”

Section: Introductionmentioning

confidence: 99%

Direct observation of spectral differences between normal and basal cell carcinoma (BCC) tissues using confocal Raman microscopy

et al. 2005

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“…Initial work in rats models in-vitro showed potential for in vivo applications. 74 Safety and feasibility of using Raman probes in the oesophagus was shown using an Enviva Visionex probe. 400 spectra in 20 patients were measured using 5 second acquisition times, although discrimination of pathology was poor 67 due to large sampling depths.…”

Section: Endoscopic Diagnostic Applicationsmentioning

confidence: 99%

Developing fibre optic Raman probes for applications in clinical spectroscopy

et al. 2016

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Raman spectroscopy has been shown by various groups over the last two decades to have significant capability in discriminating disease states in bodily fluids, cells and tissues. Recent development in instrumentation, optics and manufacturing approaches has facilitated the design and demonstration of various novel in vivo probes, which have applicability for myriad of applications. This review focusses on key considerations and recommendations for application specific clinical Raman probe design and construction. Raman probes can be utilised as clinical tools able to provide rapid, non-invasive, real-time molecular analysis of disease specific changes in tissues. Clearly the target tissue location, the significiance of spectral changes with disease and the possible access routes to the region of interest will vary for each clinical application considered. This review provides insight into design and construction considerations, including suitable probe designs and manufacturing materials compatible with Raman spectroscopy.

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In Vivo Detection of Dysplastic Tissue by Raman Spectroscopy

Cited by 179 publications

References 27 publications

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

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

Direct observation of spectral differences between normal and basal cell carcinoma (BCC) tissues using confocal Raman microscopy

Developing fibre optic Raman probes for applications in clinical spectroscopy

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