Combined Raman spectroscopy and autofluoresence imaging method for<i>in vivo</i>skin tumor diagnosis

Zakharov, Valery P.; Bratchenko, Ivan А.; Myakinin, Oleg O.; Artemyev, Dmitry N.; Khristoforova, Yulia А.; Kozlov, Sergey V.; Moryatov, Alexander A.

doi:10.1117/12.2061667

Cited by 10 publications

(5 citation statements)

References 4 publications

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“…Bergholt et al combined RS with near-infrared autofluorescence in vivo for endoscopic detection of gastric cancer, resulting in 98% sensitivity and 92% specificity that were better than RS alone (Bergholt et al 2011). Zakharov et al also used autofluorescence imaging combined with RS for in vivo detection of malignant skin melanoma, achieving 89% sensitivity and 87% specificity (Zakharov et al 2014b). Combined RS and optical coherence tomography (OCT) has been demonstrated ex vivo in rodents and in vivo in human skin, with 30 s acquisition time for RS (figure 5) (Patil et al 2011).…”

Section: Multimodal Imaging With Raman Spectroscopymentioning

confidence: 99%

A review of Raman spectroscopy advances with an emphasis on clinical translation challenges in oncology

et al. 2016

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There is an urgent need for improved techniques for disease detection. Optical spectroscopy and imaging technologies have potential for non- or minimally-invasive use in a wide range of clinical applications. The focus here, in vivo Raman spectroscopy (RS), measures inelastic light scattering based on interaction with the vibrational and rotational modes of common molecular bonds in cells and tissue. The Raman 'signature' can be used to assess physiological status and can also be altered by disease. This information can supplement existing diagnostic (e.g. radiological imaging) techniques for disease screening and diagnosis, in interventional guidance for identifying disease margins, and in monitoring treatment responses. Using fiberoptic-based light delivery and collection, RS is most easily performed on accessible tissue surfaces, either on the skin, in hollow organs or intra-operatively. The strength of RS lies in the high biochemical information content of the spectra, that characteristically show an array of very narrow peaks associated with specific chemical bonds. This results in high sensitivity and specificity, for example to distinguish malignant or premalignant from normal tissues. A critical issue is that the Raman signal is often very weak, limiting clinical use to point-by-point measurements. However, non-linear techniques using pulsed-laser sources have been developed to enable in vivo Raman imaging. Changes in Raman spectra with disease are often subtle and spectrally distributed, requiring full spectral scanning, together with the use of tissue classification algorithms that must be trained on large numbers of independent measurements. Recent advances in instrumentation and spectral analysis have substantially improved the clinical feasibility of RS, so that it is now being investigated with increased success in a wide range of cancer types and locations, as well as for non-oncological conditions. This review covers recent advances and continuing challenges, with emphasis on clinical translation.

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Section: Multimodal Imaging With Raman Spectroscopymentioning

confidence: 99%

A review of Raman spectroscopy advances with an emphasis on clinical translation challenges in oncology

et al. 2016

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“…Therefore, the development of novel methods of Raman scattering analysis for tumor diagnosis and the use of other spectral techniques to increase the tumors diagnosis accuracy are relevant. The combined diagnostic method helps to scan large tissue areas using autofluorescence (AF) or backscattering analysis of the scanning tissue with subsequent RS examination of suspicious tissue areas 7,8 . The applicability of the AF methods for skin cancer analysis in visible and near infrared (NIR) regions has been demonstrated in many studies 9,10 .…”

Section: Introductionmentioning

confidence: 99%

Combined autofluorescence and Raman spectroscopy method for skin tumor detection in visible and near infrared regions

et al. 2015

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The combined application of Raman and autofluorescence spectroscopy in visible and near infrared regions for the analysis of malignant neoplasms of human skin was demonstrated. Ex vivo experiments were performed for 130 skin tissue samples: 28 malignant melanomas, 19 basal cell carcinomas, 15 benign tumors, 9 nevi and 59 normal tissues. Proposed method of Raman spectra analysis allows for malignant melanoma differentiating from other skin tissues with accuracy of 84% (sensitivity of 97%, specificity of 72%). Autofluorescence analysis in near infrared and visible regions helped us to increase the diagnostic accuracy by 5-10%. Registration of autofluorescence in near infrared region is realized in one optical unit with Raman spectroscopy. Thus, the proposed method of combined skin tissues study makes possible simultaneous large skin area study with autofluorescence spectra analysis and precise neoplasm type determination with Raman spectroscopy.

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“…These results were followed in 2014 by another group, Shaikh et al, which was able to diagnose in vivo cervix cancer tissue with an accuracy superior to 95% [7]. Additional in vivo human diagnostic applications using RS can be found for multiple pathologies and especially multiple cancer types, such as gastrointestinal cancers [8][9][10][11][12][13][14] and malignant ulcers [15], lung cancers [16,17], skin cancers [18][19][20][21], oral cancers [22,23] and brain cancers [4,[24][25][26][27][28]. The number of patients in these studies varies greatly, ranging from 2 [17] to 848 [20], but in most cases does not exceed 100.…”

Section: Introductionmentioning

confidence: 99%

Data consistency and classification model transferability across biomedical Raman spectroscopy systems

Picot

Daoust

Sheehy

et al. 2020

Transl Biophotonics

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Surgical guidance applications using Raman spectroscopy are being developed at a rapid pace in oncology to ensure safe and complete tumor resection during surgery. Clinical translation of these approaches relies on the acquisition of large spectral and histopathological data sets to train classification models. Data calibration must ensure compatibility across Raman systems and predictive model transferability to allow multi‐centric studies to be conducted. This paper addresses issues relating to Raman measurement standardization by first comparing Raman spectral measurements made on an optical phantom and acquired with nine distinct point probe systems and one wide‐field imaging instrument. Data standardization method led to normalized root‐mean‐square deviations between instruments of 2%. A classification model discriminating between white and gray matter was trained with one point probe system. When used to classify independent data sets acquired with the other systems, model predictions led to >95% accuracy, preliminarily demonstrating model transferability across different biomedical Raman spectroscopy instruments.

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Combined Raman spectroscopy and autofluoresence imaging method forin vivoskin tumor diagnosis

Cited by 10 publications

References 4 publications

A review of Raman spectroscopy advances with an emphasis on clinical translation challenges in oncology

A review of Raman spectroscopy advances with an emphasis on clinical translation challenges in oncology

Combined autofluorescence and Raman spectroscopy method for skin tumor detection in visible and near infrared regions

Data consistency and classification model transferability across biomedical Raman spectroscopy systems

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