In vivo nonmelanoma skin cancer diagnosis using Raman microspectroscopy

Lieber, Chad A.; Majumder, Shovan K.; Ellis, Darrel L.; Billheimer, Dean; Mahadevan‐Jansen, Anita

doi:10.1002/lsm.20653

Cited by 197 publications

(194 citation statements)

References 31 publications

Supporting

Mentioning

188

Contrasting

Unclassified

Order By: Relevance

“…While both melanoma and nonmelanoma skin cancers were included in this study, the direct comparison between BCC or SCC and MM is much less clinically relevant [43]. Thus, we compared nonmelanoma (BCC, SCC, AK) versus normal skin and MM versus PL separately.…”

Section: Discussionmentioning

confidence: 99%

Raman active components of skin cancer

Feng¹,

Moy²,

Nguyen³

et al. 2017

Biomed. Opt. Express

View full text Add to dashboard Cite

Raman spectroscopy (RS) has shown great potential in noninvasive cancer screening. Statistically based algorithms, such as principal component analysis, are commonly employed to provide tissue classification; however, they are difficult to relate to the chemical and morphological basis of the spectroscopic features and underlying disease. As a result, we propose the first Raman biophysical model applied to in vivo skin cancer screening data. We expand upon previous models by utilizing in situ skin constituents as the building blocks, and validate the model using previous clinical screening data collected from a Raman optical fiber probe. We built an 830nm confocal Raman microscope integrated with a confocal laser-scanning microscope. Raman imaging was performed on skin sections spanning various disease states, and multivariate curve resolution (MCR) analysis was used to resolve the Raman spectra of individual in situ skin constituents. The basis spectra of the most relevant skin constituents were combined linearly to fit in vivo human skin spectra. Our results suggest collagen, elastin, keratin, cell nucleus, triolein, ceramide, melanin and water are the most important model components. We make available for download (see supplemental information) a database of Raman spectra for these eight components for others to use as a reference. Our model reveals the biochemical and structural makeup of normal, nonmelanoma and melanoma skin cancers, and precancers and paves the way for future development of this approach to noninvasive skin cancer diagnosis.

show abstract

Section: Discussionmentioning

confidence: 99%

Raman active components of skin cancer

Feng¹,

Moy²,

Nguyen³

et al. 2017

Biomed. Opt. Express

View full text Add to dashboard Cite

show abstract

“…Recently, quantitative diagnosis based solely on the spectral information of tissue with simultaneous sensitivity and specificity higher than 90% was demonstrated for many cancer types, including skin (11)(12)(13)(14)(15), esophagus (16), prostate (17)(18)(19)(20)(21), breast (22), and lung (23, 24) (here, sensitivity represents the percentage of correctly detected tumor samples relative to the total number of tumor samples, and specificity is the percentage of correctly identified nontumor samples relative to the total number of nontumor samples). Nevertheless, achieving objective diagnosis while maintaining a high spatial resolution (20-50 μm) required tissue sectioning and long data acquisition times: typically 30-40 min/mm 2 for infrared microscopy and 5-20 h/mm 2 for Raman microscopy (13,15,20,23,25).…”

mentioning

confidence: 99%

Diagnosis of tumors during tissue-conserving surgery with integrated autofluorescence and Raman scattering microscopy

Kong

Rowlands

Varma

et al. 2013

Proc. Natl. Acad. Sci. U.S.A.

225

223

View full text Add to dashboard Cite

Tissue-conserving surgery is used increasingly in cancer treatment. However, one of the main challenges in this type of surgery is the detection of tumor margins. Histopathology based on tissue sectioning and staining has been the gold standard for cancer diagnosis for more than a century. However, its use during tissue-conserving surgery is limited by time-consuming tissue preparation steps (1-2 h) and the diagnostic variability inherent in subjective image interpretation. Here, we demonstrate an integrated optical technique based on tissue autofluorescence imaging (high sensitivity and high speed but low specificity) and Raman scattering (high sensitivity and high specificity but low speed) that can overcome these limitations. Automated segmentation of autofluorescence images was used to select and prioritize the sampling points for Raman spectroscopy, which then was used to establish the diagnosis based on a spectral classification model (100% sensitivity, 92% specificity per spectrum). This automated sampling strategy allowed objective diagnosis of basal cell carcinoma in skin tissue samples excised during Mohs micrographic surgery faster than frozen section histopathology, and one or two orders of magnitude faster than previous techniques based on infrared or Raman microscopy. We also show that this technique can diagnose the presence or absence of tumors in unsectioned tissue layers, thus eliminating the need for tissue sectioning. This study demonstrates the potential of this technique to provide a rapid and objective intraoperative method to spare healthy tissue and reduce unnecessary surgery by determining whether tumor cells have been removed.

show abstract

“…100,101,102 In the investigation of tumour margins during surgery, it is also important to discriminate any buried tumour tissue beneath the surface of an apparently normal tissue margin. 65,103 29 Huang et al 53 mm Lensed tip beveled tip flat face tip Jaillon et al 95 Hattori et al 28 Komachi et al 91 cm Large laser spot…”

Section: Subsurface Probes and Applicationsmentioning

confidence: 99%

“…Taking measurements in a clinic or during surgery in a completely dark environment is not always possible, so one way to overcome this is by using LED lights, which typically have a much smaller contribution to the near infrared spectral background. 65,102 …”

Section: Ambient Lightmentioning

confidence: 99%

Developing fibre optic Raman probes for applications in clinical spectroscopy

et al. 2016

View full text Add to dashboard Cite

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.

show abstract

In vivo nonmelanoma skin cancer diagnosis using Raman microspectroscopy

Cited by 197 publications

References 31 publications

Raman active components of skin cancer

Raman active components of skin cancer

Diagnosis of tumors during tissue-conserving surgery with integrated autofluorescence and Raman scattering microscopy

Developing fibre optic Raman probes for applications in clinical spectroscopy

Contact Info

Product

Resources

About