Raman biophysical markers in skin cancer diagnosis

Abstract. Raman spectroscopy (RS) has demonstrated great potential for in vivo cancer screening; however, the biophysical changes that occur for specific diagnoses remain unclear. We recently developed an inverse biophysical skin cancer model to address this issue. Here, we presented the first demonstration of in vivo melanoma and nonmelanoma skin cancer (NMSC) detection based on this model. We fit the model to our previous clinical dataset and extracted the concentration of eight Raman active components in 100 lesions in 65 patients diagnosed with malignant melanoma (MM), dysplastic nevi (DN), basal cell carcinoma, squamous cell carcinoma, and actinic keratosis. We then used logistic regression and leave-one-lesion-out cross validation to determine the diagnostically relevant model components. Our results showed that the biophysical model captures the diagnostic power of the previously used statistical classification model while also providing the skin’s biophysical composition. In addition, collagen and triolein were the most relevant biomarkers to represent the spectral variances between MM and DN, and between NMSC and normal tissue. Our work demonstrates the ability of RS to reveal the biophysical basis for accurate diagnosis of different skin cancers, which may eventually lead to a reduction in the number of unnecessary excisional skin biopsies performed.

[1]  S. Ganesan,et al.  Near-infrared Raman spectroscopy for estimating biochemical changes associated with different pathological conditions of cervix. , 2018, Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy.

[2]  Laura Marcu,et al.  Label-free optical imaging technologies for rapid translation and use during intraoperative surgical and tumor margin assessment , 2017, Journal of biomedical optics.

[3]  Xu Feng,et al.  Raman active components of skin cancer. , 2017, Biomedical optics express.

[4]  Menglin Cheng,et al.  Label-Free Raman Spectroscopy Detects Stromal Adaptations in Premetastatic Lungs Primed by Breast Cancer. , 2017, Cancer research.

[5]  Arti R. Hole,et al.  In vivo subsite classification and diagnosis of oral cancers using Raman spectroscopy , 2016 .

[6]  A. Mahadevan-Jansen,et al.  Clinical instrumentation and applications of Raman spectroscopy. , 2016, Chemical Society reviews.

[7]  I. Gersonde,et al.  In vivo study for the discrimination of cancerous and normal skin using fibre probe‐based Raman spectroscopy , 2015, Experimental dermatology.

[8]  Ram I Mahato,et al.  Nanoparticle-mediated drug delivery for treating melanoma. , 2015, Nanomedicine.

[9]  H. Lui,et al.  Real-time Raman spectroscopy for automatic in vivo skin cancer detection: an independent validation , 2015, Analytical and Bioanalytical Chemistry.

[10]  Daisuke Miyamori,et al.  Raman spectroscopy of human skin: looking for a quantitative algorithm to reliably estimate human age , 2015, Journal of biomedical optics.

[11]  M. Pacheco,et al.  Discrimination of non‐melanoma skin lesions from non‐tumor human skin tissues in vivo using Raman spectroscopy and multivariate statistics , 2015, Lasers in surgery and medicine.

[12]  Mia K Markey,et al.  Clinical study of noninvasive in vivo melanoma and nonmelanoma skin cancers using multimodal spectral diagnosis , 2014, Journal of biomedical optics.

[13]  James W Tunnell,et al.  Design and characterization of a novel multimodal fiber-optic probe and spectroscopy system for skin cancer applications. , 2014, The Review of scientific instruments.

[14]  Renato Amaro Zângaro,et al.  Discriminating model for diagnosis of basal cell carcinoma and melanoma in vitro based on the Raman spectra of selected biochemicals , 2012, Journal of biomedical optics.

[15]  M. Pacheco,et al.  Discrimination of basal cell carcinoma and melanoma from normal skin biopsies in vitro through Raman spectroscopy and principal component analysis. , 2012, Photomedicine and laser surgery.

[16]  Wei Zheng,et al.  Simultaneous fingerprint and high-wavenumber confocal Raman spectroscopy enhances early detection of cervical precancer in vivo. , 2012, Analytical chemistry.

[17]  H. Lui,et al.  Real-time Raman spectroscopy for in vivo skin cancer diagnosis. , 2012, Cancer research.

[18]  Michael J Sherratt,et al.  Molecular aspects of skin ageing. , 2011, Maturitas.

[19]  Fiona M Watt,et al.  Cell-extracellular matrix interactions in normal and diseased skin. , 2011, Cold Spring Harbor perspectives in biology.

[20]  Xavier Robin,et al.  pROC: an open-source package for R and S+ to analyze and compare ROC curves , 2011, BMC Bioinformatics.

[21]  Mads S. Bergholt,et al.  Characterizing variability in in vivo Raman spectra of different anatomical locations in the upper gastrointestinal tract toward cancer detection. , 2011, Journal of biomedical optics.

[22]  R. Phelps,et al.  Histopathological Variants of Cutaneous Squamous Cell Carcinoma: A Review , 2010, Journal of skin cancer.

[23]  Narasimhan Rajaram,et al.  Pilot clinical study for quantitative spectral diagnosis of non‐melanoma skin cancer , 2010, Lasers in surgery and medicine.

[24]  L. Sherby.  Journey , 2009 .

[25]  Wei Xia,et al.  Matrix-degrading metalloproteinases in photoaging. , 2009, The journal of investigative dermatology. Symposium proceedings.

[26]  Khek Yu Ho,et al.  Near-infrared Raman spectroscopy for gastric precancer diagnosis , 2009 .

[27]  Apostolos Pappas,et al.  Epidermal surface lipids , 2009, Dermato-endocrinology.

[28]  Anita Mahadevan-Jansen,et al.  In vivo nonmelanoma skin cancer diagnosis using Raman microspectroscopy , 2008, Lasers in surgery and medicine.

[29]  Nicholas Stone,et al.  The use of Raman spectroscopy to provide an estimation of the gross biochemistry associated with urological pathologies , 2007, Analytical and bioanalytical chemistry.

[30]  R. Dasari,et al.  Diagnosing breast cancer by using Raman spectroscopy. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[31]  L. K. Hansen,et al.  Melanoma diagnosis by Raman spectroscopy and neural networks: structure alterations in proteins and lipids in intact cancer tissue. , 2004, The Journal of investigative dermatology.

[32]  Michael S Feld,et al.  Optical fiber probe for biomedical Raman spectroscopy. , 2004, Applied optics.

[33]  Chris Del Mar,et al.  Factors influencing the number needed to excise: excision rates of pigmented lesions by general practitioners , 2004, The Medical journal of Australia.

[34]  A. Mahadevan-Jansen,et al.  Automated Method for Subtraction of Fluorescence from Biological Raman Spectra , 2003, Applied spectroscopy.

[35]  J. Habbema,et al.  Internal validation of predictive models: efficiency of some procedures for logistic regression analysis. , 2001, Journal of clinical epidemiology.

[36]  Howell G. M. Edwards,et al.  Fourier transform Raman and infrared vibrational study of human skin: Assignment of spectral bands , 1992 .

[37]  S Zucker,et al.  Purification and characterization of a connective-tissue-degrading metalloproteinase from the cytosol of metastatic melanoma cells. , 1987, The Biochemical journal.

[38]  F. E. Harrell.  Regression modeling strategies : with applications to linear models, logistic and ordinal regression, and survival analysis , 2015 .

[39]  Sophia Rabe-Hesketh,et al.  Generalized linear mixed-effects models , 2009 .

[40]  Clay J Cockerell,et al.  The actinic (solar) keratosis: a 21st-century perspective. , 2003, Archives of dermatology.

[41]  H. Bruining,et al.  In vitro and in vivo Raman spectroscopy of human skin. , 1998, Biospectroscopy.

[42]  E. Boston.  Katherine. , 1970, Nursing mirror and midwives journal.