Biophysical basis of skin cancer margin assessment using Raman spectroscopy

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.

Characterization of Ex Vivo Nonmelanoma Skin Tissue Using Raman Spectroscopy

“…Previous studies have shown encouraging results for classifying nonmelanoma skin cancer versus normal tissue with Raman fiber probes [16][17][18][19][20][21]. Several studies have reported high sensitivity (63-100%) and specificity (83-91%) for discriminating the major types of nonmelanoma skin cancer, basal cell carcinoma (BCC), and squamous cell carcinoma (SCC) from normal skin in vivo [16,21,22] and ex vivo [23,24]. Though high diagnostic accuracy has been demonstrated in these studies, most used statistical algorithms to transform Raman spectra to a few significant dimensions, including principal component analysis [16,17,20,21], linear discriminant analysis [23], and maximum representation and discrimination feature [18,19].…”

Superpixel Raman spectroscopy for rapid skin cancer margin assessment

“…By selecting a balanced tumor score threshold, the specificity and sensitivity reached 94% and 82%, respectively. This ROC was then statistically compared to our recent study in 30 patients using R software to determine if the superpixel acquisition provides equivalent potential for BCC classification compared with the point‐by‐point scanning.…”

“…Bottom: SNR plot. The main bands of interest are assigned to collagen at 792, 942, 1056, 1269, 1454 and 1665 cm −1 …”

“…A previously established biophysical inverse model was fitted to the average tissue spectra by nonnegative least squares fitting. The model has been recently applied to in vivo skin cancer diagnosis , and ex vivo skin tumor margin assessment . The model describes the tissue spectra as a linear combination of the basis spectra of collagen, elastin, triolein, nucleus, keratin, ceramide and water.…”

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

“…Many Raman microscopes have been developed with the intention of guiding surgery by assisting pathologists in the visualization of tissue margins in excised tissue (e.g., breast, 30 skin, 31 and oral 32 cancer). These systems can cover areas up to several , but they are unsuitable for performing molecular imaging in vivo .…”