Nonscanning large-area Raman imaging for ex vivo/in vivo skin cancer discrimination

Schmälzlin, Elmar; Moralejo, B.; Gersonde, Ingo; Schleusener, Johannes; Darvin, Maxim E.; Thiede, Gisela; Roth, Martin

doi:10.1117/1.jbo.23.10.105001

Cited by 15 publications

(12 citation statements)

References 43 publications

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“…The fluorescence, introduced by the high lipofuscin content could possibly be avoided when a longer wavelength illumination source for Raman spectroscopy is chosen. 76 The relatively long acquisition time of spontaneous Raman scattering when measuring a large area could be overcome either by non-scanning Raman techniques 77 or by stimulated Raman scattering (SRS) imaging. 18…”

Section: Resultsmentioning

confidence: 99%

The search for a unique Raman signature of amyloid-beta plaques in human brain tissue from Alzheimer's disease patients

Lochocki

Morrema

Ariese

et al. 2020

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Section: Resultsmentioning

confidence: 99%

The search for a unique Raman signature of amyloid-beta plaques in human brain tissue from Alzheimer's disease patients

Lochocki

Morrema

Ariese

et al. 2020

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“…Optical biopsy based on RS and AF application demonstrates a high level of accuracy (exceeding 90%) in the diagnosis of skin cancer . Moreover, Schmälzlin et al offered application of the Raman imaging system to study cancer biopsy samples . Many research teams have been trying to increase the efficiency of diagnostics by using more sensitive and expensive equipment (high spectral resolution, deeply cooled detectors, etc.).…”

Section: Introductionmentioning

confidence: 99%

Portable spectroscopic system for in vivo skin neoplasms diagnostics by Raman and autofluorescence analysis

Khristoforova

Bratchenko

Myakinin

et al. 2019

Journal of Biophotonics

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The present paper studies the applicability of a portable cost‐effective spectroscopic system for the optical screening of skin tumors. in vivo studies of Raman scattering and autofluorescence (AF) of skin tumors with the 785 nm excitation laser in the near‐infrared region included malignant melanoma, basal cell carcinoma and various types of benign neoplasms. The efficiency of the portable system was evaluated by comparison with a highly sensitive spectroscopic system and with the diagnosis accuracy of a human oncologist. Partial least square analysis of Raman and AF spectra was performed; specificity and sensitivity of various skin oncological pathologies detection varied from 78.9% to 100%. Hundred percent accuracy of benign and malignant skin tumors differentiation is possible only with a combined analysis of Raman and AF signals.

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“…38 However, there remains a lack of Raman imaging systems with large FOV for in vivo human clinical use. The only Raman imaging system with a large measurement area (1 cm 2 ) that was developed and tested for a human in vivo application is reported by Schmälzlin et al (2018) 35 for skin cancer although no cancer margin detection was demonstrated.…”

Section: Introductionmentioning

confidence: 99%

Handheld macroscopic Raman spectroscopy imaging instrument for machine-learning-based molecular tissue margins characterization

et al. 2021

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Significance: Raman spectroscopy has been developed for surgical guidance applications interrogating live tissue during tumor resection procedures to detect molecular contrast consistent with cancer pathophysiological changes. To date, the vibrational spectroscopy systems developed for medical applications include single-point measurement probes and intraoperative microscopes. There is a need to develop systems with larger fields of view (FOVs) for rapid intraoperative cancer margin detection during surgery. Aim: We design a handheld macroscopic Raman imaging system for in vivo tissue margin characterization and test its performance in a model system. Approach: The system is made of a sterilizable line scanner employing a coherent fiber bundle for relaying excitation light from a 785-nm laser to the tissue. A second coherent fiber bundle is used for hyperspectral detection of the fingerprint Raman signal over an area of 1 cm 2. Machine learning classifiers were trained and validated on porcine adipose and muscle tissue. Results: Porcine adipose versus muscle margin detection was validated ex vivo with an accuracy of 99% over the FOV of 95 mm 2 in ∼3 min using a support vector machine. Conclusions: This system is the first large FOV Raman imaging system designed to be integrated in the workflow of surgical cancer resection. It will be further improved with the aim of discriminating brain cancer in a clinically acceptable timeframe during glioma surgery.

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Nonscanning large-area Raman imaging for ex vivo/in vivo skin cancer discrimination

Cited by 15 publications

References 43 publications

The search for a unique Raman signature of amyloid-beta plaques in human brain tissue from Alzheimer's disease patients

The search for a unique Raman signature of amyloid-beta plaques in human brain tissue from Alzheimer's disease patients

Portable spectroscopic system for in vivo skin neoplasms diagnostics by Raman and autofluorescence analysis

Handheld macroscopic Raman spectroscopy imaging instrument for machine-learning-based molecular tissue margins characterization

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