Discriminating model for diagnosis of basal cell carcinoma and melanomain vitrobased on the Raman spectra of selected biochemicals

Abstract

Raman spectroscopy has been employed to identify differences in the biochemical constitution of malignant [basal cell carcinoma (BCC) and melanoma (MEL)] cells compared to normal skin tissues, with the goal of skin cancer diagnosis. We collected Raman spectra from compounds such as proteins, lipids, and nucleic acids, which are expected to be represented in human skin spectra, and developed a linear least-squares fitting model to estimate the contributions of these compounds to the tissue spectra. We used a set of 145 spectra from biopsy fragments of normal (30 spectra), BCC (96 spectra), and MEL (19 spectra) skin tissues, collected using a near-infrared Raman spectrometer (830 nm, 50 to 200 mW, and 20 s exposure time) coupled to a Raman probe. We applied the best-fitting model to the spectra of biochemicals and tissues, hypothesizing that the relative spectral contribution of each compound to the tissue Raman spectrum changes according to the disease. We verified that actin, collagen, elastin, and triolein were the most important biochemicals representing the spectral features of skin tissues. A classification model applied to the relative contribution of collagen III, elastin, and melanin using Euclidean distance as a discriminator could differentiate normal from BCC and MEL.

Raman active components of skin cancer

“…For instance, Caspers et al used ceramide to model epidermal lipid in human stratum corneum layer [40]. Silveira et al included elastin to model skin dermal protein [9]. Keratin was important for in vivo skin to consider the impact of epidermis [22,40] but not necessary for excisional skin fragments because the measurement was on the dermis side [9].…”

“…The advantage of using synthetic/purified chemicals as model components is that they can be easily measured without the need for Raman micro-imaging. This concept has been applied successfully to a previous Raman model of excisional skin biopsies [9] and has given us some prior knowledge about skin composition. Other groups used in situ constituents to build biophysical models of other disease processes, such as coronary atherosclerosis [19], breast cancer [20], and brain tumor [21].…”

“…Recent advances in near-infrared lasers, optical filters, fiber optics and CCD cameras have greatly improved its sensitivity in detecting the chemical composition of biological tissues. In the most recent decade, the Raman optical fiber probe has allowed for fast and accurate cancer diagnostics, including ex vivo study of breast [4,5], prostate [6], lung [7] and skin [8,9], and in vivo study of breast [10], cervical [11], and skin [2,12]. Recent clinical studies from our group [12] and others [2] have demonstrated that RS has high diagnostic accuracy in discriminating skin melanoma from nonmelanoma pigmented lesions.…”

“…The building blocks of our model are Raman active components extracted from skin in situ. Previous biophysical models used model components either measured directly from synthetic/purified chemicals [9,[15][16][17][18], or extracted from tissue sections in situ [19][20][21]. The advantage of using synthetic/purified chemicals as model components is that they can be easily measured without the need for Raman micro-imaging.…”

“…Previous Raman models of human skin were either built for ex vivo tissue specimens [9] or in vivo normal skin [22]; however, Raman biophysical models have not been applied to in vivo skin cancer screening to interpret biophysical changes between pathologies.…”