Discrimination between basal cell carcinoma and hair follicles in skin tissue sections by Raman micro-spectroscopy

Larraona-Puy, Marta; Ghiţă, Constantin; Zoladek, Alina; Perkins, William; Varma, Sandeep; Leach, I. H.; Koloydenko, Alexey; Williams, Hywel C; Notingher, Ioan

doi:10.1016/j.molstruc.2010.10.046

Cited by 25 publications

(18 citation statements)

References 32 publications

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“…HF is the sheath of cells and connective tissue that surrounds the root of a hair. HF and HS were separated because they had heterogenous biochemical composition [17]. While muscle tissue can be present in Mohs sections and has been studied in other Raman studies [6,20], it generally appears in a small minority of cases; thus, we have excluded it from this study.…”

Section: Clustering Analysismentioning

confidence: 99%

“…Prior studies mostly utilized statistical algorithms to extract the spectral differences between BCC and normal tissue, such as principal component analysis [6,14], linear discriminant analysis [17] and maximum representation and discrimination feature [15]. Although such methods provided high diagnostic accuracy, they did not elucidate the nature and biochemical processes responsible for the spectral differences.…”

Section: Introductionmentioning

confidence: 99%

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Biophysical basis of skin cancer margin assessment using Raman spectroscopy

Feng

Fox

Reichenberg

et al. 2018

Biomed. Opt. Express

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Achieving adequate margins during tumor margin resection is critical to minimize the recurrence rate and maximize positive patient outcomes during skin cancer surgery. Although Mohs micrographic surgery is by far the most effective method to treat nonmelanoma skin cancer, it can be limited by its inherent required infrastructure, including time-consuming and expensive on-site histopathology. Previous studies have demonstrated that Raman spectroscopy can accurately detect basal cell carcinoma (BCC) from surrounding normal tissue; however, the biophysical basis of the detection remained unclear. Therefore, we aim to explore the relevant Raman biomarkers to guide BCC margin resection. Raman imaging was performed on skin tissue samples from 30 patients undergoing Mohs surgery. High correlations were found between the histopathology and Raman images for BCC and primary normal structures (including epidermis, dermis, inflamed dermis, hair follicle, hair shaft, sebaceous gland and fat). A previously developed model was used to extract the biochemical changes associated with malignancy. Our results showed that BCC had a significantly different concentration of nucleus, keratin, collagen, triolein and ceramide compared to normal structures. The nucleus accounted for most of the discriminant power (90% sensitivity, 92% specificity-balanced approach). Our findings suggest that Raman spectroscopy is a promising surgical guidance tool for identifying tumors in the resection margins.

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Section: Clustering Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Biophysical basis of skin cancer margin assessment using Raman spectroscopy

Feng

Fox

Reichenberg

et al. 2018

Biomed. Opt. Express

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“…These characteristic Raman peaks interpret not only information about biological components of the cell but also their quality, quantity, symmetry, and orientation the compound. Recently, a combination of Raman microspectroscopy and machine learning techniques have been used successfully in the identification of various cancers such as lung, breast, cervix, stomach, brain, skin, oral, leukemia, and bladder cancer [7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24]. Initial RS studies used near-infrared and visible lasers to identify biochemical profiles of human breast biopsies.…”

Section: Introductionmentioning

confidence: 99%

Advancing cancer diagnostics with artificial intelligence and spectroscopy: identifying chemical changes associated with breast cancer

Talari

Rehman

2019

Expert Review of Molecular Diagnostics

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Background: Artificial intelligence (AI) and machine learning (ML) approaches in combination with Raman spectroscopy (RS) to obtain accurate medical diagnosis and decision-making is a way forward for understanding not only the chemical pathway to the progression of disease, but also for tailor-made personalized medicine. These processes remove unwanted affects in the spectra such as noise, fluorescence and normalization, and help in the optimization of spectral data by employing chemometrics. Methods: In this study, breast cancer tissues have been analyzed by RS in conjunction with principal component (PCA) and linear discriminate (LDA) analyses. Tissue microarray (TMA) breast biopsies were investigated using RS and chemometric methods and classified breast biopsies into luminal A, luminal B, HER2, and triple negative subtypes. Results: Supervised and unsupervised algorithms were applied on biopsy data to explore intra and inter data set biochemical changes associated with lipids, collagen, and nucleic acid content. LDA predicted specificity accuracy of luminal A, luminal B, HER2, and triple negative subtypes were 70%, 100%, 90%, and 96.7%, respectively. Conclusion: It is envisaged that a combination of RS with AI and ML may create a precise and accurate real-time methodology for cancer diagnosis and monitoring. ARTICLE HISTORY

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“…Not only was the LDA model able to classify BCC from healthy tissue with a high sensitivity and specificity, it was also employed in constructing 2-dimensinonal biochemical images which correlated strongly with histopathology staining. In a more recent study, Larraona-Puy et al demonstrated that by using a similar LDA "a priori" model, BCC and hair follicles in skin tissue could be differentiated [47]. Once more, images were constructed and classification was accomplished using this same LDA model.…”

Section: Skinmentioning

confidence: 99%

Raman Spectroscopy for Clinical Oncology

Fenn

Xanthopoulos

Pyrgiotakis

et al. 2011

Advances in Optical Technologies

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Cancer is one of the leading causes of death throughout the world. Advancements in early and improved diagnosis could help prevent a significant number of these deaths. Raman spectroscopy is a vibrational spectroscopic technique which has received considerable attention recently with regards to applications in clinical oncology. Raman spectroscopy has the potential not only to improve diagnosis of cancer but also to advance the treatment of cancer. A number of studies have investigated Raman spectroscopy for its potential to improve diagnosis and treatment of a wide variety of cancers. In this paper the most recent advances in dispersive Raman spectroscopy, which have demonstrated promising leads to real world application for clinical oncology are reviewed. The application of Raman spectroscopy to breast, brain, skin, cervical, gastrointestinal, oral, and lung cancers is reviewed as well as a special focus on the data analysis techniques, which have been employed in the studies.

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Discrimination between basal cell carcinoma and hair follicles in skin tissue sections by Raman micro-spectroscopy

Cited by 25 publications

References 32 publications

Biophysical basis of skin cancer margin assessment using Raman spectroscopy

Biophysical basis of skin cancer margin assessment using Raman spectroscopy

Advancing cancer diagnostics with artificial intelligence and spectroscopy: identifying chemical changes associated with breast cancer

Raman Spectroscopy for Clinical Oncology

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