Raman fiber-optical method for colon cancer detection: Cross-validation and outlier identification approach

Petersen, Dennis R.; Naveed, P.; Ragheb, Ahmad S.; Niedieker, Daniel; El-Mashtoly, Samir F.; Brechmann, Thorsten; Kötting, Carsten; Schmiegel, Wolff; Freier, Erik; Pox, Christian; Gerwert, Klaus

doi:10.1016/j.saa.2017.03.054

Cited by 34 publications

(24 citation statements)

References 38 publications

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“…9,13-15 Analogous peaks to IP with differing spectral patterns have been found in colon and lung cancer. 14,15 It is likely that neoplastic tissue shares certain specific peaks but maintains differences in spectral patterns and intensities. 13,14 That being said, no difference in spectral peaks was noted in the IP specimens without dysplasia from the 1 patient with mild dysplasia and may be due to sampling error during RS measurements.…”

Section: Discussionmentioning

confidence: 86%

“…16 Despite the high interobserver reliability, HRME for IP requires training to identify nuclear crowding with sparse cytoplasm patterns. [15][16][17] Several hurdles remain to implement RS in clinical practice: the size of the equipment, time to analysis of spectra, and lack of characterization of other sinonasal pathologies. The current setup in a research laboratory and associated cost for the Raman microscope (roughly $50,000 USD) remain active barriers.…”

Section: Discussionmentioning

confidence: 99%

“…As others have described, RS can have a significant role in margin assessment intraoperatively. 12,15 Although frozen section remains the current gold standard for intraoperative assessment of margin status, limitations exist, including time to result as well as sampling and interpretation errors. 3,4 Alternative optical diagnostic tools, such as high-resolution microendoscopy (HRME) of the nasal cavity, use systemic or topical contrast agents that stain nuclear membranes of cells and allow for visualization of cellular structure and patterns.…”

Section: Discussionmentioning

confidence: 99%

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Raman Spectroscopy for Inverted Papilloma: A Proof‐of‐Concept Study

Mascarella

Al-Rasheed

Fnais

et al. 2018

Otolaryngol.--head neck surg.

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Inverted papillomas are tumors of the sinonasal tract with a propensity to recur. Raman spectroscopy can potentially identify inverted papillomas from other tissue based on biochemical signatures. A pilot study comparing Raman spectroscopy to histopathology for 3 types of sinonasal tissue was performed. Spectral data of biopsies from patients with normal sinonasal mucosa, chronic rhinosinusitis, and inverted papillomas are compared to histopathology using principal component analysis and linear discriminant analysis after data preprocessing. A total of 18 normal, 15 chronic rhinosinusitis, and 18 inverted papilloma specimens were evaluated. The model distinguished normal sinonasal mucosa, chronic rhinosinusitis, and inverted papilloma tissue with an overall accuracy of 90.2% (95% confidence interval, 0.86-0.94). In conclusion, Raman spectroscopy can distinguish inverted papilloma, normal sinonasal mucosa, and chronically rhinosinusitis tissue with acceptable accuracy.

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Section: Discussionmentioning

confidence: 86%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Raman Spectroscopy for Inverted Papilloma: A Proof‐of‐Concept Study

Mascarella

Al-Rasheed

Fnais

et al. 2018

Otolaryngol.--head neck surg.

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“…It appears that the potential advantages resulting from this have not been fully exploited to date, and spatial bagging constitutes one step in this direction. For example, spatial bagging may be of relevance in the context of fibre optical Raman probes, where spatial bagging could be applicable by collecting several spectra around one investigated spot.…”

Section: Resultsmentioning

confidence: 99%

Integrating spatial, morphological, and textural information for improved cell type differentiation using Raman microscopy

Krauß

Yosef

Lechtonen

et al. 2017

Journal of Chemometrics

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Raman microscopy is a well-established tool for distinguishing different cell types in cell biological or cytopathological applications, since it can provide maps that show the specific distribution of biochemical components in the cell, with high lateral and spatial resolution. Currently, established data analysis approaches for differentiating cells of different types mostly rely on conventional chemometrics approaches, which tend to not systematically utilise the advantages provided by Raman microscopic data sets. To address this, we propose 2 approaches that explicitly exploit the large number of spectra as well as the morphological and textural information that are available in Raman microscopic data sets. Spatial bagging as our first approach is based on a statistical analysis of majority vote over classification results obtained from individual pixel spectra. Based on the Condorcet's Jury Theorem, this approach raises the accuracy of a relatively weak classifier for individual spectra to nearly perfect accuracy at the level of characterising whole cells. Our second approach extracts morphological and textural (morpho-textural) features from Raman microscopic images to differentiate cell types. While using few wavenumbers of the Raman spectrum only, our results indicate on a quantitative basis that Raman microscopic images carry more morphological and textural information than haematoxylin and eosin (H&E) stained images as the current gold standard in cytopathology. Our 2 approaches promise improved protocols for the fast acquisition of Raman imaging data, for instance, for the morphological analysis of coherent anti-Stokes Raman spectroscopy microscopic imaging data or for improving the accuracy of fibre optical probe systems by resampling spectra and utilising spatial bagging.

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“…The authors further demonstrated that confocal Raman microspectroscopy could be used to differentiate four histological types of gastric adenocarcinomas, including papillary adenocarcinoma, tubular adenocarcinoma, mucinous adenocarcinoma, and signet ring cell adenocarcinoma [ 20 ]. Petersen et al carried out Raman fiber-optical measurements of colon biopsy samples taken during colonoscopy and showed a diagnostic accuracy over 70% [ 21 ].…”

Section: Spontaneous Raman Scattering For Cancer Diagnosismentioning

confidence: 99%

Raman Spectroscopy and Imaging for Cancer Diagnosis

Cui

Zhang

Yue

2018

Journal of Healthcare Engineering

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Raman scattering has long been used to analyze chemical compositions in biological systems. Owing to its high chemical specificity and noninvasive detection capability, Raman scattering has been widely employed in cancer screening, diagnosis, and intraoperative surgical guidance in the past ten years. In order to overcome the weak signal of spontaneous Raman scattering, coherent Raman scattering and surface-enhanced Raman scattering have been developed and recently applied in the field of cancer research. This review focuses on innovative studies of the use of Raman scattering in cancer diagnosis and their potential to transition from bench to bedside.

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Raman fiber-optical method for colon cancer detection: Cross-validation and outlier identification approach

Cited by 34 publications

References 38 publications

Raman Spectroscopy for Inverted Papilloma: A Proof‐of‐Concept Study

Raman Spectroscopy for Inverted Papilloma: A Proof‐of‐Concept Study

Integrating spatial, morphological, and textural information for improved cell type differentiation using Raman microscopy

Raman Spectroscopy and Imaging for Cancer Diagnosis

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