Literature DB >> 30775086

Biophysical basis of skin cancer margin assessment using Raman spectroscopy.

Xu Feng1, Matthew C Fox2, Jason S Reichenberg2, Fabiana C P S Lopes2, Katherine R Sebastian2, Mia K Markey1,3, James W Tunnell1.   

Abstract

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.

Entities:  

Year:  2018        PMID: 30775086      PMCID: PMC6363200          DOI: 10.1364/BOE.10.000104

Source DB:  PubMed          Journal:  Biomed Opt Express        ISSN: 2156-7085            Impact factor:   3.732


  4 in total

1.  Co-localized line-field confocal optical coherence tomography and confocal Raman microspectroscopy for three-dimensional high-resolution morphological and molecular characterization of skin tissues ex vivo.

Authors:  Léna Waszczuk; Jonas Ogien; Jean-Luc Perrot; Arnaud Dubois
Journal:  Biomed Opt Express       Date:  2022-03-25       Impact factor: 3.562

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

Authors:  François Daoust; Tien Nguyen; Patrick Orsini; Jacques Bismuth; Marie-Maude de Denus-Baillargeon; Israel Veilleux; Alexandre Wetter; Philippe Mckoy; Isabelle Dicaire; Maroun Massabki; Kevin Petrecca; Frédéric Leblond
Journal:  J Biomed Opt       Date:  2021-02       Impact factor: 3.170

3.  Label-Free Differentiation of Cancer and Non-Cancer Cells Based on Machine-Learning-Algorithm-Assisted Fast Raman Imaging.

Authors:  Qing He; Wen Yang; Weiquan Luo; Stefan Wilhelm; Binbin Weng
Journal:  Biosensors (Basel)       Date:  2022-04-15

4.  Deep learning on reflectance confocal microscopy improves Raman spectral diagnosis of basal cell carcinoma.

Authors:  Mengkun Chen; Xu Feng; Matthew C Fox; Jason S Reichenberg; Fabiana C P S Lopes; Katherine R Sebastian; Mia K Markey; James W Tunnell
Journal:  J Biomed Opt       Date:  2022-06       Impact factor: 3.758

  4 in total

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