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Melanoma pp 39-85 | Cite as

Methods of Melanoma Detection

  • Clara Curiel-Lewandrowski
  • Clara Stemwedel
  • Mihaela Balu
  • Suephy C. Chen
  • Laura K. Ferris
  • Pedram Gerami
  • Adele C. Green
  • Mariah M. Johnson
  • Lois J. Loescher
  • Josep Malvehy
  • Ashfaq A. Marghoob
  • Kathryn Martires
  • Giovanni Pellacani
  • Tracy Petrie
  • Susana Puig
  • Inga Saknite
  • Susan M. Swetter
  • Per Svedenhag
  • Eric R. Tkaczyk
  • Oliver J. Wisco
  • Sancy A. Leachman
Chapter

Abstract

The purpose of this chapter is to present a broad spectrum of approaches for melanoma detection. From the perspective of morbidity, mortality, quality of life, and cost, there is a compelling need to improve the early detection of melanoma. Fortunately, because melanoma is usually visible on the surface of the skin, it is well suited for the application of early detection approaches and noninvasive technologies. Here, we systematically review several state-of-the-art methods, from screening processes of populations and individuals to screening of suspicious lesions with novel technologies—including advanced imaging modalities, machine learning, and consumer-driven technologies such as mobile devices and applications—as well as the use of molecular assays for both diagnosis and prognosis. Technologies and techniques that capitalize on the cutaneous location of melanoma are rapidly evolving to reach the right populations, to identify the individuals in greatest need of screening and to apply the right approach at the right time to identify melanoma before it reaches metastatic competency. A combination of approaches that bridge medical science, responsible adoption of technology, public health challenges, and behavior modification will be necessary in order for progress in melanoma early detection—and resultant minimization of melanoma morbidity and mortality—to continue.

Keywords

Melanoma early detection Stratified approaches to melanoma screening Melanoma surveillance Dermoscopy Teledermatology Mobile apps Advanced imaging Machine learning Molecular assays 

References

  1. 1.
    Coit DG, Thompson JA, Andtbacka R, Anker CJ, Bichakjian CK, Carson WE 3rd, et al. Melanoma, version 4.2014. J Natl Compr Cancer Netw. 2014;12(5):621–9.CrossRefGoogle Scholar
  2. 2.
    Hodi FS, Chesney J, Pavlick AC, Robert C, Grossmann KF, McDermott DF, et al. Combined nivolumab and ipilimumab versus ipilimumab alone in patients with advanced melanoma: 2-year overall survival outcomes in a multicentre, randomised, controlled, phase 2 trial. Lancet Oncol. 2016;17(11):1558–68.  https://doi.org/10.1016/S1470-2045(16)30366-7.CrossRefPubMedPubMedCentralGoogle Scholar
  3. 3.
    Skin cancer by the numbers. In: Skin disease briefs: American Academy of Dermatology; 2017.Google Scholar
  4. 4.
    Rubio-Rodriguez D, De Diego BS, Perez M, Rubio-Terres C. Cost-effectiveness of drug treatments for advanced melanoma: a systematic literature review. PharmacoEconomics. 2017.  https://doi.org/10.1007/s40273-017-0517-1.
  5. 5.
    Oh A, Tran DM, McDowell LC, Keyvani D, Barcelon JA, Merino O, et al. Cost effectiveness of nivolumab-ipilimumab combination therapy compared with monotherapy for first-line treatment of metastatic melanoma in the United States. J Manag Care Spec Pharm. 2017;23(6):653–64.  https://doi.org/10.18553/jmcp.2017.23.6.653.CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    Silva A, Rauscher GH, Ferrans CE, Hoskins K, Rao R. Assessing the quality of race/ethnicity, tumor, and breast cancer treatment information in a non-SEER state registry. J Registry Manag. 2014;41(1):24–30.PubMedGoogle Scholar
  7. 7.
    Harris RB, Koch SM, Newton C, Silvis NG, Curiel-Lewandroski C, Giancola J, et al. Underreporting of melanoma in Arizona and strategies for increasing reporting: a public health partnership approach. Public Health Rep. 2015;130(6):737–44.  https://doi.org/10.1177/003335491513000624.CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Cockburn M, Swetter SM, Peng D, Keegan TH, Deapen D, Clarke CA. Melanoma underreporting: why does it happen, how big is the problem, and how do we fix it? J Am Acad Dermatol. 2008;59(6):1081–5.  https://doi.org/10.1016/j.jaad.2008.08.007.CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    Brunssen A, Waldmann A, Eisemann N, Katalinic A. Impact of skin cancer screening and secondary prevention campaigns on skin cancer incidence and mortality: a systematic review. J Am Acad Dermatol. 2017;76(1):129–39. e10.  https://doi.org/10.1016/j.jaad.2016.07.045.CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Johnson MM, Leachman SA, Aspinwall LG, Cranmer LD, Curiel-Lewandrowski C, Sondak VK, et al. Skin cancer screening: recommendations for data-driven screening guidelines and a review of the US preventive services task force controversy. Melanoma Manage. 2017;4(1):13–37.CrossRefGoogle Scholar
  11. 11.
    Byrnes P, Ackermann E, Williams ID, Mitchell GK, Askew D. Management of skin cancer in Australia--a comparison of general practice and skin cancer clinics. Aust Fam Physician. 2007;36(12):1073–5.PubMedGoogle Scholar
  12. 12.
    Swetter SM, Johnson TM, Miller DR, Layton CJ, Brooks KR, Geller AC. Melanoma in middle-aged and older men: a multi-institutional survey study of factors related to tumor thickness. Arch Dermatol. 2009;145(4):397–404.  https://doi.org/10.1001/archdermatol.2008.603.CrossRefPubMedGoogle Scholar
  13. 13.
    LeBlanc WG, Vidal L, Kirsner RS, Lee DJ, Caban-Martinez AJ, McCollister KE, et al. Reported skin cancer screening of US adult workers. J Am Acad Dermatol. 2008;59(1):55–63.  https://doi.org/10.1016/j.jaad.2008.03.013.CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    Miller KA, Langholz BM, Zadnick J, Hamilton AS, Cozen W, Mack TM, et al. Prevalence and predictors of recent skin examination in a population-based twin cohort. Cancer Epidemiol Biomark Prev. 2015;24(8):1190–8.  https://doi.org/10.1158/1055-9965.EPI-14-1389.CrossRefGoogle Scholar
  15. 15.
    Goulart JM, Quigley EA, Dusza S, Jewell ST, Alexander G, Asgari MM, et al. Skin cancer education for primary care physicians: a systematic review of published evaluated interventions. J Gen Intern Med. 2011;26(9):1027–35.  https://doi.org/10.1007/s11606-011-1692-y.CrossRefPubMedPubMedCentralGoogle Scholar
  16. 16.
    Martires KJ, Kurlander DE, Minwell GJ, Dahms EB, Bordeaux JS. Patterns of cancer screening in primary care from 2005 to 2010. Cancer. 2014;120(2):253–61.  https://doi.org/10.1002/cncr.28403.CrossRefPubMedGoogle Scholar
  17. 17.
    Oliveria SA, Heneghan MK, Cushman LF, Ughetta EA, Halpern AC. Skin cancer screening by dermatologists, family practitioners, and internists: barriers and facilitating factors. Arch Dermatol. 2011;147(1):39–44.  https://doi.org/10.1001/archdermatol.2010.414.CrossRefPubMedGoogle Scholar
  18. 18.
    Eide MJ, Asgari MM, Fletcher SW, Geller AC, Halpern AC, Shaikh WR, et al. Effects on skills and practice from a web-based skin cancer course for primary care providers. J Am Board Fam Med. 2013;26(6):648–57.  https://doi.org/10.3122/jabfm.2013.06.130108.CrossRefPubMedGoogle Scholar
  19. 19.
    Berwick M, Armstrong BK, Ben-Porat L, Fine J, Kricker A, Eberle C, et al. Sun exposure and mortality from melanoma. J Natl Cancer Inst. 2005;97(3):195–9.  https://doi.org/10.1093/jnci/dji019.CrossRefPubMedGoogle Scholar
  20. 20.
    Weinstock MA, Risica PM, Martin RA, Rakowski W, Dube C, Berwick M, et al. Melanoma early detection with thorough skin self-examination: the "check it out" randomized trial. Am J Prev Med. 2007;32(6):517–24.  https://doi.org/10.1016/j.amepre.2007.02.024.CrossRefPubMedPubMedCentralGoogle Scholar
  21. 21.
    Preventive Services Task Force US, Bibbins-Domingo K, Grossman DC, Curry SJ, Davidson KW, Ebell M, et al. Screening for skin cancer: US Preventive Services Task Force recommendation statement. JAMA. 2016;316(4):429–35.  https://doi.org/10.1001/jama.2016.8465.CrossRefGoogle Scholar
  22. 22.
    Wernli KJ, Henrikson NB, Morrison CC, Nguyen M, Pocobelli G, Whitlock EP. Screening for skin cancer in adults: An updated systematic evidence review for the U.S. Preventive Services Task Force. Rockville, MD: U.S. Preventive Services Task Force Evidence Syntheses, formerly Systematic Evidence Reviews; 2016.Google Scholar
  23. 23.
    Katalinic A, Waldmann A, Weinstock MA, Geller AC, Eisemann N, Greinert R, et al. Does skin cancer screening save lives? An observational study comparing trends in melanoma mortality in regions with and without screening. Cancer. 2012;118(21):5395–402.  https://doi.org/10.1002/cncr.27566.CrossRefPubMedPubMedCentralGoogle Scholar
  24. 24.
    Weinstock MA, Ferris LK, Saul MI, Geller AC, Risica PM, Siegel JA, et al. Downstream consequences of melanoma screening in a community practice setting: first results. Cancer. 2016;122(20):3152–6.  https://doi.org/10.1002/cncr.30177.CrossRefPubMedPubMedCentralGoogle Scholar
  25. 25.
    Goulart JM, Malvehy J, Puig S, Martin G, Marghoob AA. Dermoscopy in skin self-examination: a useful tool for select patients. Arch Dermatol. 2011;147(1):53–8.  https://doi.org/10.1001/archdermatol.2010.387.CrossRefPubMedGoogle Scholar
  26. 26.
    Titus LJ, Clough-Gorr K, Mackenzie TA, Perry A, Spencer SK, Weiss J, et al. Recent skin self-examination and doctor visits in relation to melanoma risk and tumour depth. Br J Dermatol. 2013;168(3):571–6.  https://doi.org/10.1111/bjd.12003.CrossRefPubMedGoogle Scholar
  27. 27.
    Greaney ML, Puleo E, Geller AC, Hu SW, Werchniak AE, DeCristofaro S, et al. Patient follow-up after participating in a beach-based skin cancer screening program. Int J Environ Res Public Health. 2012;9(5):1836–45.  https://doi.org/10.3390/ijerph9051836.CrossRefPubMedPubMedCentralGoogle Scholar
  28. 28.
    Hamidi R, Peng D, Cockburn M. Efficacy of skin self-examination for the early detection of melanoma. Int J Dermatol. 2010;49(2):126–34.  https://doi.org/10.1111/j.1365-4632.2009.04268.x.CrossRefPubMedGoogle Scholar
  29. 29.
    Pollitt RA, Geller AC, Brooks DR, Johnson TM, Park ER, Swetter SM. Efficacy of skin self-examination practices for early melanoma detection. Cancer Epidemiol Biomark Prev. 2009;18(11):3018–23.  https://doi.org/10.1158/1055-9965.EPI-09-0310.CrossRefGoogle Scholar
  30. 30.
    American Massage Therapy Association. Massage therapy industry fact sheet 2017. 2017. https://www.amtamassage.org/infocenter/economic_industry-fact-sheet.html.
  31. 31.
    Nahin RL, Barnes PM, Stussman BJ, Bloom B. Costs of complementary and alternative medicine (CAM) and frequency of visits to CAM practitioners: United States, 2007. Natl Health Stat Rep. 2009(18):1–14.Google Scholar
  32. 32.
    Cherkin DC, Deyo RA, Sherman KJ, Hart LG, Street JH, Hrbek A, et al. Characteristics of visits to licensed acupuncturists, chiropractors, massage therapists, and naturopathic physicians. J Am Board Fam Pract. 2002;15(6):463–72.PubMedGoogle Scholar
  33. 33.
    Heiligers PJ, de Groot J, Koster D, van Dulmen S. Diagnoses and visit length in complementary and mainstream medicine. BMC Complement Altern Med. 2010;10:3.  https://doi.org/10.1186/1472-6882-10-3.CrossRefPubMedPubMedCentralGoogle Scholar
  34. 34.
    Campbell SM, Louie-Gao Q, Hession ML, Bailey E, Geller AC, Cummins D. Skin cancer education among massage therapists: a survey at the 2010 meeting of the American massage therapy association. J Cancer Educ. 2013;28(1):158–64.  https://doi.org/10.1007/s13187-012-0403-7.CrossRefPubMedGoogle Scholar
  35. 35.
    US Department of Labor, Bureau of Labor Statistics. Skincare Specialists. In: Occupational outlook handbook, 2016–17 edition. December 17, 2015. https://www.bls.gov/ooh/personal-care-and-service/skincare-specialists.htm. Accessed March 19, 2017.
  36. 36.
    Beauty Schools Marketing Group. Esthetician, Skin Care & Medical Esthetician job Description. In: Beauty Schools Directory. 2017. http://www.beautyschoolsdirectory.com/faq/esthetician.php. Accessed March 20, 2017.
  37. 37.
    US Department of Labor, Bureau of Labor Statistics. Barbers, hairdressers, and cosmetologists. In: Occupational outlook handbook, 2016–17 edition. December 17, 2015. https://www.bls.gov/ooh/personal-care-and-service/barbers-hairdressers-and-cosmetologists.htm. Accessed March 20, 2017.
  38. 38.
    Linnan LA, Kim AE, Wasilewski Y, Lee AM, Yang J, Solomon F. Working with licensed cosmetologists to promote health: results from the North Carolina BEAUTY and health pilot study. Prev Med. 2001;33(6):606–12.  https://doi.org/10.1006/pmed.2001.0933.CrossRefPubMedGoogle Scholar
  39. 39.
    Solomon FM, Linnan LA, Wasilewski Y, Lee AM, Katz ML, Yang J. Observational study in ten beauty salons: results informing development of the North Carolina BEAUTY and Health Project. Health Educ Behav. 2004;31(6):790–807.  https://doi.org/10.1177/1090198104264176.CrossRefPubMedGoogle Scholar
  40. 40.
    Bailey EE, Marghoob AA, Orengo IF, Testa MA, White VR, Geller AC. Skin cancer knowledge, attitudes, and behaviors in the salon: a survey of working hair professionals in Houston. Texas Arch Dermatol. 2011;147(10):1159–65.  https://doi.org/10.1001/archdermatol.2011.184.CrossRefPubMedGoogle Scholar
  41. 41.
    Statistics Brain. Tattoo statistics. August 13, 2016. http://www.statisticbrain.com/tattoo-statistics/. Accessed March 20, 2017.
  42. 42.
    Beltrone G. Sunscreen brand trains Tattoo Artists to look for signs of skin cancer. In: Adweek May 13, 2014. http://www.adweek.com/creativity/sunscreen-brand-trains-tattoo-artists-look-signs-skin-cancer-157639/. Accessed March 20, 2017.
  43. 43.
    Rosenbaum BE, Milam EC, Seo L, Leger MC. Skin care in the tattoo parlor: a survey of tattoo artists in New York City. Dermatology. 2016;232(4):484–9.  https://doi.org/10.1159/000446345.CrossRefPubMedGoogle Scholar
  44. 44.
    American Society for Dermatologic Surgery. Stylists against skin cancer: program overview. https://www.asds.net/SHADE/. Accessed July 27, 2017.
  45. 45.
    Neufeld BS, Anderson SK. Massage therapists and the detection of skin cancer in clients. Massage Today. 2013 2013/02.Google Scholar
  46. 46.
    La Plante C. Early detection of skin cancer by massage therapists can save lives. In: Merican massage therapy association, reflections - first line of defense. March 21, 2008. https://www.amtamassage.org/articles/3/MTJ/detail/1641. Accessed March 20, 2017.
  47. 47.
    Potrony M, Badenas C, Aguilera P, Puig-Butille JA, Carrera C, Malvehy J, et al. Update in genetic susceptibility in melanoma. Ann Transl Med. 2015;3(15):210.  https://doi.org/10.3978/j.issn.2305-5839.2015.08.11.CrossRefPubMedPubMedCentralGoogle Scholar
  48. 48.
    Soura E, Eliades PJ, Shannon K, Stratigos AJ, Tsao H. Hereditary melanoma: update on syndromes and management: genetics of familial atypical multiple mole melanoma syndrome. J Am Acad Dermatol. 2016;74(3):395–407.; quiz 8-10.  https://doi.org/10.1016/j.jaad.2015.08.038.CrossRefPubMedPubMedCentralGoogle Scholar
  49. 49.
    Florell SR, Boucher KM, Garibotti G, Astle J, Kerber R, Mineau G, et al. Population-based analysis of prognostic factors and survival in familial melanoma. J Clin Oncol. 2005;23(28):7168–77.  https://doi.org/10.1200/JCO.2005.11.999.CrossRefPubMedGoogle Scholar
  50. 50.
    Bishop DT, Demenais F, Goldstein AM, Bergman W, Bishop JN, Bressac-de Paillerets B, et al. Geographical variation in the penetrance of CDKN2A mutations for melanoma. J Natl Cancer Inst. 2002;94(12):894–903.CrossRefPubMedGoogle Scholar
  51. 51.
    Orlow I, Begg CB, Cotignola J, Roy P, Hummer AJ, Clas BA, et al. CDKN2A germline mutations in individuals with cutaneous malignant melanoma. J Invest Dermatol. 2007;127(5):1234–43.  https://doi.org/10.1038/sj.jid.5700689.CrossRefPubMedGoogle Scholar
  52. 52.
    Parker JF, Florell SR, Alexander A, DiSario JA, Shami PJ, Leachman SA. Pancreatic carcinoma surveillance in patients with familial melanoma. Arch Dermatol. 2003;139(8):1019–25.  https://doi.org/10.1001/archderm.139.8.1019.CrossRefPubMedGoogle Scholar
  53. 53.
    Vasen HF, Gruis NA, Frants RR, van Der Velden PA, Hille ET, Bergman W. Risk of developing pancreatic cancer in families with familial atypical multiple mole melanoma associated with a specific 19 deletion of p16 (p16-Leiden). Int J Cancer. 2000;87(6):809–11.CrossRefPubMedGoogle Scholar
  54. 54.
    Read J, Wadt KA, Hayward NK. Melanoma genetics. J Med Genet. 2016;53(1):1–14.  https://doi.org/10.1136/jmedgenet-2015-103150.CrossRefPubMedGoogle Scholar
  55. 55.
    Leachman SA, Lucero OM, Sampson JE, Cassidy P, Bruno W, Queirolo P, et al. Identification, genetic testing, and management of hereditary melanoma. Cancer Metastasis Rev. 2017;36(1):77–90.  https://doi.org/10.1007/s10555-017-9661-5.CrossRefPubMedPubMedCentralGoogle Scholar
  56. 56.
    Gandini S, Sera F, Cattaruzza MS, Pasquini P, Zanetti R, Masini C, et al. Meta-analysis of risk factors for cutaneous melanoma: III. Family history, actinic damage and phenotypic factors. Eur J Cancer. 2005;41(14):2040–59.  https://doi.org/10.1016/j.ejca.2005.03.034.CrossRefPubMedPubMedCentralGoogle Scholar
  57. 57.
    Weinstock MA, Brodsky GL. Bias in the assessment of family history of melanoma and its association with dysplastic nevi in a case-control study. J Clin Epidemiol. 1998;51(12):1299–303.CrossRefPubMedGoogle Scholar
  58. 58.
    Gandini S, Sera F, Cattaruzza MS, Pasquini P, Abeni D, Boyle P, et al. Meta-analysis of risk factors for cutaneous melanoma: I. Common and atypical naevi. Eur J Cancer. 2005;41(1):28–44.  https://doi.org/10.1016/j.ejca.2004.10.015.CrossRefPubMedPubMedCentralGoogle Scholar
  59. 59.
    El Ghissassi F, Baan R, Straif K, Grosse Y, Secretan B, Bouvard V, et al. A review of human carcinogens—Part D: Radiation. Lancet Oncol. 2009;10(8):751–2.CrossRefPubMedGoogle Scholar
  60. 60.
    Cust AE, Armstrong BK, Goumas C, Jenkins MA, Schmid H, Hopper JL, et al. Sunbed use during adolescence and early adulthood is associated with increased risk of early-onset melanoma. Int J Cancer. 2011;128(10):2425–35.  https://doi.org/10.1002/ijc.25576.CrossRefPubMedPubMedCentralGoogle Scholar
  61. 61.
    Lazovich D, Isaksson Vogel R, Weinstock MA, Nelson HH, Ahmed RL, Berwick M. Association between indoor tanning and melanoma in younger men and women. JAMA Dermatol. 2016;152(3):268–75.  https://doi.org/10.1001/jamadermatol.2015.2938.CrossRefPubMedPubMedCentralGoogle Scholar
  62. 62.
    Gandini S, Sera F, Cattaruzza MS, Pasquini P, Picconi O, Boyle P, et al. Meta-analysis of risk factors for cutaneous melanoma: II. Sun exposure. Eur J Cancer. 2005;41(1):45–60.  https://doi.org/10.1016/j.ejca.2004.10.016.CrossRefPubMedPubMedCentralGoogle Scholar
  63. 63.
    Geller AC, Miller DR, Swetter SM, Demierre MF, Gilchrest BA. A call for the development and implementation of a targeted national melanoma screening program. Arch Dermatol. 2006;142(4):504–7.  https://doi.org/10.1001/archderm.142.4.504.CrossRefPubMedGoogle Scholar
  64. 64.
    Oliveria SA, Selvam N, Mehregan D, Marchetti MA, Divan HA, Dasgeb B, et al. Biopsies of nevi in children and adolescents in the United States, 2009 through 2013. JAMA Dermatol. 2015;151(4):447–8.  https://doi.org/10.1001/jamadermatol.2014.4576.CrossRefPubMedGoogle Scholar
  65. 65.
    Russell LB, Gold MR, Siegel JE, Daniels N, Weinstein MC. The role of cost-effectiveness analysis in health and medicine. Panel on Cost-Effectiveness in Health and Medicine. JAMA. 1996;276(14):1172–7.CrossRefPubMedGoogle Scholar
  66. 66.
    Girgis A, Clarke P, Burton RC, Sanson-Fisher RW. Screening for melanoma by primary health care physicians: a cost-effectiveness analysis. J Med Screen. 1996;3(1):47–53.  https://doi.org/10.1177/096914139600300112.CrossRefPubMedGoogle Scholar
  67. 67.
    Freedberg KA, Geller AC, Miller DR, Lew RA, Koh HK. Screening for malignant melanoma: a cost-effectiveness analysis. J Am Acad Dermatol. 1999;41(5 Pt 1):738–45.CrossRefPubMedGoogle Scholar
  68. 68.
    Losina E, Walensky RP, Geller A, Beddingfield FC 3rd, Wolf LL, Gilchrest BA, et al. Visual screening for malignant melanoma: a cost-effectiveness analysis. Arch Dermatol. 2007;143(1):21–8.  https://doi.org/10.1001/archderm.143.1.21.CrossRefPubMedPubMedCentralGoogle Scholar
  69. 69.
    Salopek TG, Slade JM, Marghoob AA, Rigel DS, Kopf AW, Bart RS, et al. Management of cutaneous malignant melanoma by dermatologists of the American Academy of Dermatology. II. Definitive surgery for malignant melanoma. J Am Acad Dermatol. 1995;33(3):451–61.CrossRefPubMedGoogle Scholar
  70. 70.
    Robinson JK, Halpern AC. Cost-effective melanoma screening. JAMA Dermatol. 2016;152(1):19–21.  https://doi.org/10.1001/jamadermatol.2015.2681.CrossRefPubMedPubMedCentralGoogle Scholar
  71. 71.
    Hoorens I, Vossaert K, Pil L, Boone B, De Schepper S, Ongenae K, et al. Total-body examination vs lesion-directed skin cancer screening. JAMA Dermatol. 2016;152(1):27–34.  https://doi.org/10.1001/jamadermatol.2015.2680.CrossRefPubMedGoogle Scholar
  72. 72.
    Aitken JF, Elwood M, Baade PD, Youl P, English D. Clinical whole-body skin examination reduces the incidence of thick melanomas. Int J Cancer. 2010;126(2):450–8.  https://doi.org/10.1002/ijc.24747.CrossRefPubMedPubMedCentralGoogle Scholar
  73. 73.
    Whiteman DC, Green AC, Olsen CM. The growing burden of invasive melanoma: projections of incidence rates and numbers of new cases in six susceptible populations through 2031. J Invest Dermatol. 2016;136(6):1161–71.  https://doi.org/10.1016/j.jid.2016.01.035.CrossRefPubMedGoogle Scholar
  74. 74.
    Baade P, Coory M. Trends in melanoma mortality in Australia: 1950-2002 and their implications for melanoma control. Aust N Z J Public Health. 2005;29(4):383–6.CrossRefPubMedGoogle Scholar
  75. 75.
    Iannacone MR, Green AC. Towards skin cancer prevention and early detection: evolution of skin cancer awareness campaigns in Australia. Melanoma Manage. 2014;1(1):75–84.  https://doi.org/10.2217/mmt.14.6.CrossRefGoogle Scholar
  76. 76.
    Criscione VD, Weinstock MA. Melanoma thickness trends in the United States, 1988–2006. J Invest Dermatol. 2010;130(3):793–7.  https://doi.org/10.1038/jid.2009.328.CrossRefPubMedGoogle Scholar
  77. 77.
    Autier P, Koechlin A, Boniol M. The forthcoming inexorable decline of cutaneous melanoma mortality in light-skinned populations. Eur J Cancer. 2015;51(7):869–78.  https://doi.org/10.1016/j.ejca.2015.01.056.CrossRefPubMedGoogle Scholar
  78. 78.
    Olsen CM, Neale RE, Green AC, Webb PM, The QSkin Study, The Epigene Study, et al. Independent validation of six melanoma risk prediction models. J Invest Dermatol. 2015;135(5):1377–84.  https://doi.org/10.1038/jid.2014.533.CrossRefPubMedGoogle Scholar
  79. 79.
    Vuong K, Armstrong BK, Weiderpass E, Lund E, Adami HO, Veierod MB, et al. Development and external validation of a melanoma risk prediction model based on self-assessed risk factors. JAMA Dermatol. 2016.  https://doi.org/10.1001/jamadermatol.2016.0939.
  80. 80.
    Robinson JK, Wayne JD, Martini MC, Hultgren BA, Mallett KA, Turrisi R. Early detection of new melanomas by patients with melanoma and their partners using a structured skin self-examination skills training intervention: a randomized clinical trial. JAMA Dermatol. 2016;152(9):979–85.  https://doi.org/10.1001/jamadermatol.2016.1985.CrossRefPubMedPubMedCentralGoogle Scholar
  81. 81.
    Liu W, Dowling JP, Murray WK, McArthur GA, Thompson JF, Wolfe R, et al. Rate of growth in melanomas: characteristics and associations of rapidly growing melanomas. Arch Dermatol. 2006;142(12):1551–8.  https://doi.org/10.1001/archderm.142.12.1551.CrossRefPubMedGoogle Scholar
  82. 82.
    Wu X, Oliveria SA, Yagerman S, Chen L, DeFazio J, Braun R, et al. Feasibility and efficacy of patient-initiated mobile teledermoscopy for short-term monitoring of clinically atypical nevi. JAMA Dermatol. 2015;151(5):489–96.  https://doi.org/10.1001/jamadermatol.2014.3837.CrossRefPubMedGoogle Scholar
  83. 83.
    Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017;542(7639):115–8.  https://doi.org/10.1038/nature21056.CrossRefPubMedGoogle Scholar
  84. 84.
    Webster DE, Suver C, Doerr M, Mounts E, Domenico L, Petrie T, et al. The Mole Mapper Study, mobile phone skin imaging and melanoma risk data collected using ResearchKit. Sci Data. 2017;4:170005.  https://doi.org/10.1038/sdata.2017.5.CrossRefPubMedPubMedCentralGoogle Scholar
  85. 85.
    Salerni G, Carrera C, Lovatto L, Marti-Laborda RM, Isern G, Palou J, et al. Characterization of 1152 lesions excised over 10 years using total-body photography and digital dermatoscopy in the surveillance of patients at high risk for melanoma. J Am Acad Dermatol. 2012;67(5):836–45.  https://doi.org/10.1016/j.jaad.2012.01.028.CrossRefPubMedGoogle Scholar
  86. 86.
    Feit NE, Dusza SW, Marghoob AA. Melanomas detected with the aid of total cutaneous photography. Br J Dermatol. 2004;150(4):706–14.  https://doi.org/10.1111/j.0007-0963.2004.05892.x.CrossRefPubMedGoogle Scholar
  87. 87.
    Banky JP, Kelly JW, English DR, Yeatman JM, Dowling JP. Incidence of new and changed nevi and melanomas detected using baseline images and dermoscopy in patients at high risk for melanoma. Arch Dermatol. 2005;141(8):998–1006.  https://doi.org/10.1001/archderm.141.8.998.CrossRefPubMedGoogle Scholar
  88. 88.
    Perier-Muzet M, Thomas L, Poulalhon N, Debarbieux S, Bringuier PP, Duru G, et al. Melanoma patients under vemurafenib: prospective follow-up of melanocytic lesions by digital dermoscopy. J Invest Dermatol. 2014;134(5):1351–8.  https://doi.org/10.1038/jid.2013.462.CrossRefPubMedGoogle Scholar
  89. 89.
    Green WH, Wang SQ, Cognetta AB Jr. Total-body cutaneous examination, total-body photography, and dermoscopy in the care of a patient with xeroderma pigmentosum and multiple melanomas. Arch Dermatol. 2009;145(8):910–5.  https://doi.org/10.1001/archdermatol.2009.87.CrossRefPubMedGoogle Scholar
  90. 90.
    Truong A, Strazzulla L, March J, Boucher KM, Nelson KC, Kim CC, et al. Reduction in nevus biopsies in patients monitored by total body photography. J Am Acad Dermatol. 2016;75(1):135–43. e5.  https://doi.org/10.1016/j.jaad.2016.02.1152.CrossRefPubMedGoogle Scholar
  91. 91.
    Leachman SA, Cassidy PB, Chen SC, Curiel C, Geller A, Gareau D, et al. Methods of melanoma detection. Cancer Treat Res. 2016;167:51–105.  https://doi.org/10.1007/978-3-319-22539-5_3.CrossRefPubMedGoogle Scholar
  92. 92.
    Marghoob AA, International Skin Imaging Collaboration Melanoma Project Working Group. Standards in dermatologic imaging. JAMA Dermatol. 2015;151(8):819–21.  https://doi.org/10.1001/jamadermatol.2015.32.CrossRefPubMedGoogle Scholar
  93. 93.
    Katragadda C, Finnane A, Soyer HP, Marghoob AA, Halpern A, Malvehy J, et al. Technique standards for skin lesion imaging: a Delphi consensus statement. JAMA Dermatol. 2016.  https://doi.org/10.1001/jamadermatol.2016.3949.
  94. 94.
    Finnane A, Curiel-Lewandrowski C, Wimberley G, Caffery L, Katragadda C, Halpern A, et al. Proposed technical guidelines for the acquisition of clinical images of skin-related conditions. JAMA Dermatol. 2017;153(5):453–7.  https://doi.org/10.1001/jamadermatol.2016.6214.CrossRefPubMedGoogle Scholar
  95. 95.
    Korotkov K, Quintana J, Puig S, Malvehy J, Garcia R. A new total body scanning system for automatic change detection in multiple pigmented skin lesions. IEEE Trans Med Imaging. 2015;34(1):317–38.  https://doi.org/10.1109/TMI.2014.2357715.CrossRefPubMedGoogle Scholar
  96. 96.
    Bogo F, Romero J, Peserico E, Black MJ. Automated detection of new or evolving melanocytic lesions using a 3D body model. Med Image Comput Comput Assist Interv. 2014;17(Pt 1):593–600.PubMedGoogle Scholar
  97. 97.
    Lovatto L, Carrera C, Salerni G, Alos L, Malvehy J, Puig S. In vivo reflectance confocal microscopy of equivocal melanocytic lesions detected by digital dermoscopy follow-up. J Eur Acad Dermatol Venereol. 2015;29(10):1918–25.  https://doi.org/10.1111/jdv.13067.CrossRefPubMedGoogle Scholar
  98. 98.
    Ceder H, Hylen AS, Larko AW, Paoli J. Evaluation of electrical impedance spectroscopy as an adjunct to dermoscopy in short-term monitoring of atypical melanocytic lesions. Dermatol Pract Concept. 2016;6(4):1–6.  https://doi.org/10.5826/dpc.0604a01.CrossRefPubMedPubMedCentralGoogle Scholar
  99. 99.
    American Academy of Dermatology Ad Hoc Task Force for the ABCDEs of Melanoma, Tsao H, Olazagasti JM, Cordoro KM, Brewer JD, Taylor SC, et al. Early detection of melanoma: reviewing the ABCDEs. J Am Acad Dermatol. 2015;72(4):717–23.  https://doi.org/10.1016/j.jaad.2015.01.025.CrossRefGoogle Scholar
  100. 100.
    Moloney FJ, Guitera P, Coates E, Haass NK, Ho K, Khoury R, et al. Detection of primary melanoma in individuals at extreme high risk: a prospective 5-year follow-up study. JAMA Dermatol. 2014;150(8):819–27.  https://doi.org/10.1001/jamadermatol.2014.514.CrossRefPubMedGoogle Scholar
  101. 101.
    Gaudy-Marqueste C, Wazaefi Y, Bruneu Y, Triller R, Thomas L, Pellacani G, et al. Ugly duckling sign as a major factor of efficiency in melanoma detection. JAMA Dermatol. 2017.  https://doi.org/10.1001/jamadermatol.2016.5500.
  102. 102.
    Vestergaard ME, Macaskill P, Holt PE, Menzies SW. Dermoscopy compared with naked eye examination for the diagnosis of primary melanoma: a meta-analysis of studies performed in a clinical setting. Br J Dermatol. 2008;159(3):669–76.  https://doi.org/10.1111/j.1365-2133.2008.08713.x.CrossRefPubMedGoogle Scholar
  103. 103.
    Carli P, De Giorgi V, Crocetti E, Mannone F, Massi D, Chiarugi A, et al. Improvement of malignant/benign ratio in excised melanocytic lesions in the “dermoscopy era”: a retrospective study 1997–2001. Br J Dermatol. 2004;150(4):687–92.  https://doi.org/10.1111/j.0007-0963.2004.05860.x.CrossRefPubMedPubMedCentralGoogle Scholar
  104. 104.
    Haenssle HA, Hoffmann S, Holzkamp R, Samhaber K, Lockmann A, Fliesser M, et al. Melanoma thickness: the role of patients' characteristics, risk indicators and patterns of diagnosis. J Eur Acad Dermatol Venereol. 2015;29(1):102–8.  https://doi.org/10.1111/jdv.12471.CrossRefPubMedGoogle Scholar
  105. 105.
    Bafounta ML, Beauchet A, Aegerter P, Saiag P. Is dermoscopy (epiluminescence microscopy) useful for the diagnosis of melanoma? Results of a meta-analysis using techniques adapted to the evaluation of diagnostic tests. Arch Dermatol. 2001;137(10):1343–50.CrossRefPubMedGoogle Scholar
  106. 106.
    Kittler H, Pehamberger H, Wolff K, Binder M. Diagnostic accuracy of dermoscopy. Lancet Oncol. 2002;3(3):159–65.CrossRefPubMedGoogle Scholar
  107. 107.
    Terushkin V, Warycha M, Levy M, Kopf AW, Cohen DE, Polsky D. Analysis of the benign to malignant ratio of lesions biopsied by a general dermatologist before and after the adoption of dermoscopy. Arch Dermatol. 2010;146(3):343–4.  https://doi.org/10.1001/archdermatol.2010.12.CrossRefPubMedGoogle Scholar
  108. 108.
    Argenziano G, Puig S, Zalaudek I, Sera F, Corona R, Alsina M, et al. Dermoscopy improves accuracy of primary care physicians to triage lesions suggestive of skin cancer. J Clin Oncol. 2006;24(12):1877–82.  https://doi.org/10.1200/JCO.2005.05.0864.CrossRefPubMedGoogle Scholar
  109. 109.
    Binder M, Puespoeck-Schwarz M, Steiner A, Kittler H, Muellner M, Wolff K, et al. Epiluminescence microscopy of small pigmented skin lesions: short-term formal training improves the diagnostic performance of dermatologists. J Am Acad Dermatol. 1997;36(2 Pt 1):197–202.CrossRefPubMedGoogle Scholar
  110. 110.
    Chen LL, Liebman TN, Soriano RP, Dusza SW, Halpern AC, Marghoob AA. One-year follow-up of dermoscopy education on the ability of medical students to detect skin cancer. Dermatology. 2013;226(3):267–73.  https://doi.org/10.1159/000350571.CrossRefPubMedGoogle Scholar
  111. 111.
    Rogers T, Marino ML, Dusza SW, Bajaj S, Usatine RP, Marchetti MA, et al. A clinical aid for detecting skin cancer: The Triage Amalgamated Dermoscopic Algorithm (TADA). J Am Board Fam Med. 2016;29(6):694–701.  https://doi.org/10.3122/jabfm.2016.06.160079.CrossRefPubMedPubMedCentralGoogle Scholar
  112. 112.
    Salerni G, Teran T, Puig S, Malvehy J, Zalaudek I, Argenziano G, et al. Meta-analysis of digital dermoscopy follow-up of melanocytic skin lesions: a study on behalf of the International Dermoscopy Society. J Eur Acad Dermatol Venereol. 2013;27(7):805–14.  https://doi.org/10.1111/jdv.12032.CrossRefPubMedPubMedCentralGoogle Scholar
  113. 113.
    Rose SE, Argenziano G, Marghoob AA. Melanomas difficult to diagnose via dermoscopy. G Ital Dermatol Venereol. 2010;145(1):111–26.PubMedGoogle Scholar
  114. 114.
    Carrera C, Marchetti MA, Dusza SW, Argenziano G, Braun RP, Halpern AC, et al. Validity and reliability of dermoscopic criteria used to differentiate nevi from melanoma: a web-based international Dermoscopy Society Study. JAMA Dermatol. 2016;152(7):798–806.  https://doi.org/10.1001/jamadermatol.2016.0624.CrossRefPubMedPubMedCentralGoogle Scholar
  115. 115.
    Ali A-RA, Deserno TM. A systematic review of automated melanoma detection in dermatoscopic images and its ground truth data. In: Proc. SPIE 8318, medical imaging 2012: Image perception, observer performance, and technology assessment, 83181I. Feburary 29, 2012.  https://doi.org/10.1117/12.912389.
  116. 116.
    He KZ, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. arXivorg. December 10, 2015. doi:arXiv:1512.03385.Google Scholar
  117. 117.
    Collaboration ISI. ISIC Archive: International skin imaging collaboration: melanoma project. https://isic-archive.com/. Accessed March 20, 2017.
  118. 118.
    Codella NN, Nguyen QB, Pankanti S, Gutman D, Helba B, Halpern A, Smith JR. Deep Learning Ensembles for Melanoma Recognition in Dermoscopy Images. arXivorg. October 18, 2016. doi:arXiv:1610.04662.Google Scholar
  119. 119.
    Moreno-Ramirez D, Ferrandiz L, Nieto-Garcia A, Carrasco R, Moreno-Alvarez P, Galdeano R, et al. Store-and-forward teledermatology in skin cancer triage: experience and evaluation of 2009 teleconsultations. Arch Dermatol. 2007;143(4):479–84.  https://doi.org/10.1001/archderm.143.4.479.CrossRefPubMedGoogle Scholar
  120. 120.
    Ferrandiz L, Ruiz-de-Casas A, Martin-Gutierrez FJ, Peral-Rubio F, Mendez-Abad C, Rios-Martin JJ, et al. Effect of teledermatology on the prognosis of patients with cutaneous melanoma. Arch Dermatol. 2012;148(9):1025–8.  https://doi.org/10.1001/archdermatol.2012.778.CrossRefPubMedGoogle Scholar
  121. 121.
    Griffiths WA. Improving melanoma diagnosis in primary care--A tele-dermatoscopy project. J Telemed Telecare. 2010;16(4):185–6.  https://doi.org/10.1258/jtt.2010.004005.CrossRefPubMedGoogle Scholar
  122. 122.
    Arzberger E, Curiel-Lewandrowski C, Blum A, Chubisov D, Oakley A, Rademaker M, et al. Teledermoscopy in high-risk melanoma patients: a comparative study of face-to-face and teledermatology visits. Acta Derm Venereol. 2016;96(6):779–83.  https://doi.org/10.2340/00015555-2344.CrossRefPubMedGoogle Scholar
  123. 123.
    Warshaw EM, Gravely AA, Nelson DB. Reliability of store and forward teledermatology for skin neoplasms. J Am Acad Dermatol. 2015;72(3):426–35.  https://doi.org/10.1016/j.jaad.2014.11.001.CrossRefPubMedGoogle Scholar
  124. 124.
    Janda M, Loescher LJ, Banan P, Horsham C, Soyer HP. Lesion selection by melanoma high-risk consumers during skin self-examination using mobile teledermoscopy. JAMA Dermatol. 2014;150(6):656–8.  https://doi.org/10.1001/jamadermatol.2013.7743.CrossRefPubMedGoogle Scholar
  125. 125.
    Gendreau JL, Gemelas J, Wang M, Capulong D, Lau C, Bratten DM, et al. Unimaged melanomas in store-and-forward teledermatology. Telemed J E Health. 2016.  https://doi.org/10.1089/tmj.2016.0170.
  126. 126.
    Kassianos AP, Emery JD, Murchie P, Walter FM. Smartphone applications for melanoma detection by community, patient and generalist clinician users: a review. Br J Dermatol. 2015;172(6):1507–18.  https://doi.org/10.1111/bjd.13665.CrossRefPubMedGoogle Scholar
  127. 127.
    Hamilton AD, Brady RR. Medical professional involvement in smartphone "apps" in dermatology. Br J Dermatol. 2012;167(1):220–1.  https://doi.org/10.1111/j.1365-2133.2012.10844.x.CrossRefPubMedGoogle Scholar
  128. 128.
    Marek AJ, Chu EY, Ming ME, Kovarik CL. Assessment of smartphone applications for total body digital photography-guided skin exams by patients. J Am Acad Dermatol. 2016;75(5):1063–4. e1.  https://doi.org/10.1016/j.jaad.2016.06.005.CrossRefPubMedPubMedCentralGoogle Scholar
  129. 129.
    Resneck JS Jr, Abrouk M, Steuer M, Tam A, Yen A, Lee I, et al. Choice, transparency, coordination, and quality among direct-to-consumer telemedicine websites and apps treating skin disease. JAMA Dermatol. 2016;152(7):768–75.  https://doi.org/10.1001/jamadermatol.2016.1774.CrossRefPubMedGoogle Scholar
  130. 130.
    Wolf JA, Moreau JF, Akilov O, Patton T, English JC 3rd, Ho J, et al. Diagnostic inaccuracy of smartphone applications for melanoma detection. JAMA Dermatol. 2013;149(4):422–6.  https://doi.org/10.1001/jamadermatol.2013.2382.CrossRefPubMedPubMedCentralGoogle Scholar
  131. 131.
    Maier T, Kulichova D, Schotten K, Astrid R, Ruzicka T, Berking C, et al. Accuracy of a smartphone application using fractal image analysis of pigmented moles compared to clinical diagnosis and histological result. J Eur Acad Dermatol Venereol. 2015;29(4):663–7.  https://doi.org/10.1111/jdv.12648.CrossRefPubMedGoogle Scholar
  132. 132.
    U.S. Department of Health and Human Services Food and Drug Administration. Mobile medical applications: Guidance for industry and Food and Drug Administration staff. February 9, 2015. https://www.fda.gov/downloads/MedicalDevices/.../UCM263366.pdf. Accessed December 8, 2016.
  133. 133.
    Anastasiou A, Giokas K, Koutsouris D. Monitoring of compliance on an individual treatment through mobile innovations. Conf Proc IEEE Eng Med Biol Soc. 2015;2015:7320–3.  https://doi.org/10.1109/EMBC.2015.7320082.CrossRefPubMedGoogle Scholar
  134. 134.
    Neittaanmaki N, Salmivuori M, Polonen I, Jeskanen L, Ranki A, Saksela O, et al. Hyperspectral imaging in detecting dermal invasion in lentigo maligna melanoma. Br J Dermatol. 2016.  https://doi.org/10.1111/bjd.15267.
  135. 135.
    Robles FE, Chowdhury S, Wax A. Assessing hemoglobin concentration using spectroscopic optical coherence tomography for feasibility of tissue diagnostics. Biomed Opt Express. 2010;1(1):310–7.  https://doi.org/10.1364/boe.1.000310/.CrossRefPubMedPubMedCentralGoogle Scholar
  136. 136.
    Gareau D, Jacques S, Krueger J. Monte Carlo modeling of pigmented lesions. In: Proc. SPIE 8926, photonic therapeutics and diagnostics X, 89260V. March 4, 2014.  https://doi.org/10.1117/12.2040473.
  137. 137.
    U.S. Food and Drug Administration. SIASCOPE II 510(k) Premarket Notifications. 2003. http://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfPMN/pmn.cfm?ID=K023729. Accessed December 29, 2016.
  138. 138.
    U.S. Food and Drug Administration. MELAFIND Premarket Approval (PMA). 2011. http://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpma/pma.cfm?id=p090012. Accessed December 29, 1976.
  139. 139.
    Claridge E, Cotton S, Hall P, Moncrieff M. From colour to tissue histology: physics-based interpretation of images of pigmented skin lesions. Med Image Anal. 2003;7(4):489–502.CrossRefPubMedGoogle Scholar
  140. 140.
    Terstappen K, Suurkula M, Hallberg H, Ericson MB, Wennberg AM. Poor correlation between spectrophotometric intracutaneous analysis and histopathology in melanoma and nonmelanoma lesions. J Biomed Opt. 2013;18(6):061223.  https://doi.org/10.1117/1.JBO.18.6.061223.CrossRefPubMedGoogle Scholar
  141. 141.
    Moncrieff M, Cotton S, Claridge E, Hall P. Spectrophotometric intracutaneous analysis: a new technique for imaging pigmented skin lesions. Br J Dermatol. 2002;146(3):448–57.CrossRefPubMedGoogle Scholar
  142. 142.
    Emery JD, Hunter J, Hall PN, Watson AJ, Moncrieff M, Walter FM. Accuracy of SIAscopy for pigmented skin lesions encountered in primary care: development and validation of a new diagnostic algorithm. BMC Dermatol. 2010;10:9.  https://doi.org/10.1186/1471-5945-10-9.CrossRefPubMedPubMedCentralGoogle Scholar
  143. 143.
    Tomatis S, Carrara M, Bono A, Bartoli C, Lualdi M, Tragni G, et al. Automated melanoma detection with a novel multispectral imaging system: results of a prospective study. Phys Med Biol. 2005;50(8):1675–87.  https://doi.org/10.1088/0031-9155/50/8/004.CrossRefPubMedGoogle Scholar
  144. 144.
    Haniffa MA, Lloyd JJ, Lawrence CM. The use of a spectrophotometric intracutaneous analysis device in the real-time diagnosis of melanoma in the setting of a melanoma screening clinic. Br J Dermatol. 2007;156(6):1350–2.  https://doi.org/10.1111/j.1365-2133.2007.07932.x.CrossRefPubMedGoogle Scholar
  145. 145.
    Walter FM, Morris HC, Humphrys E, Hall PN, Prevost AT, Burrows N, et al. Effect of adding a diagnostic aid to best practice to manage suspicious pigmented lesions in primary care: randomised controlled trial. BMJ. 2012;345:e4110.  https://doi.org/10.1136/bmj.e4110.CrossRefPubMedPubMedCentralGoogle Scholar
  146. 146.
    Braun RP, Gutkowicz-Krusin D, Rabinovitz H, Cognetta A, Hofmann-Wellenhof R, Ahlgrimm-Siess V, et al. Agreement of dermatopathologists in the evaluation of clinically difficult melanocytic lesions: how golden is the “gold standard”? Dermatology. 2012;224(1):51–8.  https://doi.org/10.1159/000336886.CrossRefPubMedGoogle Scholar
  147. 147.
    Elbaum M, Kopf AW, Rabinovitz HS, Langley RG, Kamino H, Mihm MC Jr, et al. Automatic differentiation of melanoma from melanocytic nevi with multispectral digital dermoscopy: a feasibility study. J Am Acad Dermatol. 2001;44(2):207–18.  https://doi.org/10.1067/mjd.2001.110395.CrossRefPubMedGoogle Scholar
  148. 148.
    Monheit G, Cognetta AB, Ferris L, Rabinovitz H, Gross K, Martini M, et al. The performance of MelaFind: a prospective multicenter study. Arch Dermatol. 2011;147(2):188–94.  https://doi.org/10.1001/archdermatol.2010.302.CrossRefPubMedPubMedCentralGoogle Scholar
  149. 149.
    Cukras AR. On the comparison of diagnosis and management of melanoma between dermatologists and MelaFind. JAMA Dermatol. 2013;149(5):622–3.  https://doi.org/10.1001/jamadermatol.2013.3405.CrossRefPubMedGoogle Scholar
  150. 150.
    March J, Hand M, Grossman D. Practical application of new technologies for melanoma diagnosis: Part I. Noninvasive approaches. J Am Acad Dermatol. 2015;72(6):929–41.; quiz 41-2.  https://doi.org/10.1016/j.jaad.2015.02.1138.CrossRefPubMedGoogle Scholar
  151. 151.
    Gutkowicz-Krusin D, Elbaum M, Jacobs A, Keem S, Kopf AW, Kamino H, et al. Precision of automatic measurements of pigmented skin lesion parameters with a MelaFind(TM) multispectral digital dermoscope. Melanoma Res. 2000;10(6):563–70.CrossRefPubMedGoogle Scholar
  152. 152.
    Kapsokalyvas D, Bruscino N, Alfieri D, de Giorgi V, Cannarozzo G, Cicchi R, et al. Spectral morphological analysis of skin lesions with a polarization multispectral dermoscope. Opt Express. 2013;21(4):4826–40.  https://doi.org/10.1364/OE.21.004826.CrossRefPubMedGoogle Scholar
  153. 153.
    Vasefi F, MacKinnon N, Saager RB, Durkin AJ, Chave R, Lindsley EH, et al. Polarization-sensitive hyperspectral imaging in vivo: a multimode dermoscope for skin analysis. Sci Rep. 2014;4:4924.  https://doi.org/10.1038/srep04924.CrossRefPubMedPubMedCentralGoogle Scholar
  154. 154.
    Spigulis J, Uldis Rubins EK, Rubenis O. SkImager: a concept device for in-vivo skin assessment by multimodal imaging. P Est Acad Sci. 2014;63(3):301–8.Google Scholar
  155. 155.
    Delpueyo X, Vilaseca M, Royo S, Ares M, Rey-Barroso L, Sanabria F, et al. Multispectral imaging system based on light-emitting diodes for the detection of melanomas and basal cell carcinomas: a pilot study. J Biomed Opt. 2017;22(6):65006.  https://doi.org/10.1117/1.JBO.22.6.065006.CrossRefPubMedGoogle Scholar
  156. 156.
    Kim S, Cho D, Kim J, Kim M, Youn S, Jang JE, et al. Smartphone-based multispectral imaging: system development and potential for mobile skin diagnosis. Biomed Opt Express. 2016;7(12):5294–307.  https://doi.org/10.1364/BOE.7.005294.CrossRefPubMedPubMedCentralGoogle Scholar
  157. 157.
    Vasefi F, McKinnon N, Farkas DL. Hyperspectral and multispectral imaging in dematology. In: Hamblin M, Avici P, Gupta G, editors. Imaging in dermatology: Elsevier Academic Press; 2016. p. 187–201.CrossRefGoogle Scholar
  158. 158.
    Alenin AS, Morrison L, Curiel C, Tyo JS. Hyperspectral measurement of the scattering of polarized light by skin. In: Proc. SPIE 8160, polarization science and remote sensing V, 816014. September 10 2011.  https://doi.org/10.1117/12.895552.
  159. 159.
    Gareau DS, Correa da Rosa J, Yagerman S, Carucci JA, Gulati N, Hueto F et al. Digital imaging biomarkers feed machine learning for melanoma screening. Exp Dermatol. 2016. doi: https://doi.org/10.1111/exd.13250.CrossRefPubMedPubMedCentralGoogle Scholar
  160. 160.
    Martin J, Krueger J, Gareau D, editors. Hyperspectral imaging for melanoma screening. In: Proc. SPIE 8926, photonic therapeutics and diagnostics X, 892611. March 4, 2014.  https://doi.org/10.1117/12.2040396.
  161. 161.
    Kuzmina I, Diebele I, Jakovels D, Spigulis J, Valeine L, Kapostinsh J, et al. Towards noncontact skin melanoma selection by multispectral imaging analysis. J Biomed Opt. 2011;16(6):060502.  https://doi.org/10.1117/1.3584846.CrossRefPubMedGoogle Scholar
  162. 162.
    Rajadhyaksha M, Grossman M, Esterowitz D, Webb RH, Anderson RR. In vivo confocal scanning laser microscopy of human skin: melanin provides strong contrast. J Invest Dermatol. 1995;104(6):946–52.CrossRefPubMedGoogle Scholar
  163. 163.
    Longo C, Rajadhyaksha M, Ragazzi M, Nehal K, Gardini S, Moscarella E, et al. Evaluating ex vivo fluorescence confocal microscopy images of basal cell carcinomas in Mohs excised tissue. Br J Dermatol. 2014;171(3):561–70.  https://doi.org/10.1111/bjd.13070.CrossRefPubMedGoogle Scholar
  164. 164.
    Guitera P, Menzies SW, Longo C, Cesinaro AM, Scolyer RA, Pellacani G. In vivo confocal microscopy for diagnosis of melanoma and basal cell carcinoma using a two-step method: analysis of 710 consecutive clinically equivocal cases. J Invest Dermatol. 2012;132(10):2386–94.  https://doi.org/10.1038/jid.2012.172.CrossRefPubMedGoogle Scholar
  165. 165.
    Pellacani G, Scope A, Farnetani F, Casaretta G, Zalaudek I, Moscarella E, et al. Towards an in vivo morphologic classification of melanocytic nevi. J Eur Acad Dermatol Venereol. 2014;28(7):864–72.  https://doi.org/10.1111/jdv.12181.CrossRefPubMedGoogle Scholar
  166. 166.
    Pellacani G, Farnetani F, Gonzalez S, Longo C, Cesinaro AM, Casari A, et al. In vivo confocal microscopy for detection and grading of dysplastic nevi: a pilot study. J Am Acad Dermatol. 2012;66(3):e109–21.  https://doi.org/10.1016/j.jaad.2011.05.017.CrossRefPubMedGoogle Scholar
  167. 167.
    Pellacani G, Guitera P, Longo C, Avramidis M, Seidenari S, Menzies S. The impact of in vivo reflectance confocal microscopy for the diagnostic accuracy of melanoma and equivocal melanocytic lesions. J Invest Dermatol. 2007;127(12):2759–65.  https://doi.org/10.1038/sj.jid.5700993.CrossRefPubMedGoogle Scholar
  168. 168.
    Pellacani G, De Pace B, Reggiani C, Cesinaro AM, Argenziano G, Zalaudek I, et al. Distinct melanoma types based on reflectance confocal microscopy. Exp Dermatol. 2014;23(6):414–8.  https://doi.org/10.1111/exd.12417.CrossRefPubMedGoogle Scholar
  169. 169.
    Alarcon I, Carrera C, Palou J, Alos L, Malvehy J, Puig S. Impact of in vivo reflectance confocal microscopy on the number needed to treat melanoma in doubtful lesions. Br J Dermatol. 2014;170(4):802–8.  https://doi.org/10.1111/bjd.12678.CrossRefPubMedPubMedCentralGoogle Scholar
  170. 170.
    Borsari S, Pampena R, Lallas A, Kyrgidis A, Moscarella E, Benati E, et al. Clinical indications for use of reflectance confocal microscopy for skin cancer diagnosis. JAMA Dermatol. 2016;152(10):1093–8.  https://doi.org/10.1001/jamadermatol.2016.1188.CrossRefPubMedGoogle Scholar
  171. 171.
    Stevenson AD, Mickan S, Mallett S, Ayya M. Systematic review of diagnostic accuracy of reflectance confocal microscopy for melanoma diagnosis in patients with clinically equivocal skin lesions. Dermatol Pract Concept. 2013;3(4):19–27.  https://doi.org/10.5826/dpc.0304a05.CrossRefPubMedPubMedCentralGoogle Scholar
  172. 172.
    Pellacani G, Pepe P, Casari A, Longo C. Reflectance confocal microscopy as a second-level examination in skin oncology improves diagnostic accuracy and saves unnecessary excisions: a longitudinal prospective study. Br J Dermatol. 2014;171(5):1044–51.  https://doi.org/10.1111/bjd.13148.CrossRefPubMedGoogle Scholar
  173. 173.
    Pellacani G, Witkowski A, Cesinaro AM, Losi A, Colombo GL, Campagna A, et al. Cost-benefit of reflectance confocal microscopy in the diagnostic performance of melanoma. J Eur Acad Dermatol Venereol. 2016;30(3):413–9.  https://doi.org/10.1111/jdv.13408.CrossRefPubMedGoogle Scholar
  174. 174.
    Farnetani F, Scope A, Braun RP, Gonzalez S, Guitera P, Malvehy J, et al. Skin cancer diagnosis with reflectance confocal microscopy: reproducibility of feature recognition and accuracy of diagnosis. JAMA Dermatol. 2015;151(10):1075–80.  https://doi.org/10.1001/jamadermatol.2015.0810.CrossRefPubMedGoogle Scholar
  175. 175.
    Rao BK, Mateus R, Wassef C, Pellacani G. In vivo confocal microscopy in clinical practice: comparison of bedside diagnostic accuracy of a trained physician and distant diagnosis of an expert reader. J Am Acad Dermatol. 2013;69(6):e295–300.  https://doi.org/10.1016/j.jaad.2013.07.022.CrossRefPubMedGoogle Scholar
  176. 176.
    Malvehy J, Hauschild A, Curiel-Lewandrowski C, Mohr P, Hofmann-Wellenhof R, Motley R, et al. Clinical performance of the Nevisense system in cutaneous melanoma detection: an international, multicentre, prospective and blinded clinical trial on efficacy and safety. Br J Dermatol. 2014;171(5):1099–107.  https://doi.org/10.1111/bjd.13121.CrossRefPubMedPubMedCentralGoogle Scholar
  177. 177.
    Fukushima K. Neocognitron--a self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. NHK 放送科学基礎研究所報告. 1981;15:p106–15.Google Scholar
  178. 178.
    Hinton GE, Sejnowski TJ. Learning and releaming in Boltzmann machines. Parallel Distrilmted Process. 1986;1Google Scholar
  179. 179.
    Rumelhart DE, Hinton GE, Williams RJ. Learning representations by back-propagating errors. Nature. 1986;323(6088):533–6.CrossRefGoogle Scholar
  180. 180.
    Forsyth DPJ, Computer vision: a modern approach. Prentice Hall Professional Technical Reference; 2002.Google Scholar
  181. 181.
    Hintz-Madsen M, Hansen LK, Larsen J, Olesen E, Drzewiecki KT, editors. Detection of malignant melanoma using neural classifiers. In: Proceedings of international conference on engineerings applications on neural networks; 1996.Google Scholar
  182. 182.
    Binder M, Kittler H, Seeber A, Steiner A, Pehamberger H, Wolff K. Epiluminescence microscopy-based classification of pigmented skin lesions using computerized image analysis and an artificial neural network. Melanoma Res. 1998;8(3):261–6.CrossRefPubMedGoogle Scholar
  183. 183.
    Maglogiannis I, Pavlopoulos S, Koutsouris D. An integrated computer supported acquisition, handling, and characterization system for pigmented skin lesions in dermatological images. IEEE Trans Inf Technol Biomed. 2005;9(1):86–98.CrossRefPubMedGoogle Scholar
  184. 184.
    Brewer AC, Endly DC, Henley J, Amir M, Sampson BP, Moreau JF, et al. Mobile applications in dermatology. JAMA Dermatol. 2013;149(11):1300–4.  https://doi.org/10.1001/jamadermatol.2013.5517.CrossRefPubMedGoogle Scholar
  185. 185.
    Goodfellow I, Bengio Y, Courville A. Deep learning: MIT Press; 2016.Google Scholar
  186. 186.
    LeCun Y, Bottou L, Bengio Y, Haffner P. Gradient-based learning applied to document recognition. Proc IEEE. 1998;86(11):2278–324.CrossRefGoogle Scholar
  187. 187.
    ImageNet. 2016. http://www.image-net.org/.
  188. 188.
    Lin T-Y, Maire M, Belongie S, Hays J, Perona P, Ramanan D, et al., editors. Microsoft coco: Common objects in context. In: European conference on computer vision: Springer; 2014.Google Scholar
  189. 189.
    Krizhevsky A, Sutskever I, Hinton GE, editors. Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems; 2012.Google Scholar
  190. 190.
    Large Scale Visual Recognition Challenge 2016 (ILSVRC2016). 2016. http://image-net.org/challenges/LSVRC/2016/results. Accessed March 21, 2017.
  191. 191.
    O'Regan GM, Kemperman PM, Sandilands A, Chen H, Campbell LE, Kroboth K, et al. Raman profiles of the stratum corneum define 3 filaggrin genotype-determined atopic dermatitis endophenotypes. J Allergy Clin Immunol. 2010;126(3):574–80. e1.  https://doi.org/10.1016/j.jaci.2010.04.038.CrossRefPubMedPubMedCentralGoogle Scholar
  192. 192.
    Lui H, Zhao J, McLean D, Zeng H. Real-time Raman spectroscopy for in vivo skin cancer diagnosis. Cancer Res. 2012;72(10):2491–500.  https://doi.org/10.1158/0008-5472.CAN-11-4061.CrossRefPubMedGoogle Scholar
  193. 193.
    Liao CS, Slipchenko MN, Wang P, Li J, Lee SY, Oglesbee RA, et al. Microsecond scale vibrational spectroscopic imaging by multiplex stimulated Raman scattering microscopy. Light Sci Appl. 2015;4.  https://doi.org/10.1038/lsa.2015.38.CrossRefGoogle Scholar
  194. 194.
    Legesse FB, Medyukhina A, Heuke S, Popp J. Texture analysis and classification in coherent anti-stokes Raman scattering (CARS) microscopy images for automated detection of skin cancer. Comput Med Imaging Graph. 2015;43:36–43.  https://doi.org/10.1016/j.compmedimag.2015.02.010.CrossRefPubMedGoogle Scholar
  195. 195.
    Yue S, Cheng JX. Deciphering single cell metabolism by coherent Raman scattering microscopy. Curr Opin Chem Biol. 2016;33:46–57.  https://doi.org/10.1016/j.cbpa.2016.05.016.CrossRefPubMedPubMedCentralGoogle Scholar
  196. 196.
    Dimitrow E, Ziemer M, Koehler MJ, Norgauer J, Konig K, Elsner P, et al. Sensitivity and specificity of multiphoton laser tomography for in vivo and ex vivo diagnosis of malignant melanoma. J Invest Dermatol. 2009;129(7):1752–8.  https://doi.org/10.1038/jid.2008.439.CrossRefPubMedGoogle Scholar
  197. 197.
    Balu M, Kelly KM, Zachary CB, Harris RM, Krasieva TB, Konig K, et al. Distinguishing between benign and malignant melanocytic nevi by in vivo multiphoton microscopy. Cancer Res. 2014;74(10):2688–97.  https://doi.org/10.1158/0008-5472.CAN-13-2582.CrossRefPubMedPubMedCentralGoogle Scholar
  198. 198.
    Dimitrow E, Riemann I, Ehlers A, Koehler MJ, Norgauer J, Elsner P, et al. Spectral fluorescence lifetime detection and selective melanin imaging by multiphoton laser tomography for melanoma diagnosis. Exp Dermatol. 2009;18(6):509–15.  https://doi.org/10.1111/j.1600-0625.2008.00815.x.CrossRefPubMedGoogle Scholar
  199. 199.
    Matthews TE, Piletic IR, Selim MA, Simpson MJ, Warren WS. Pump-probe imaging differentiates melanoma from melanocytic nevi. Sci Transl Med. 2011;3(71):71ra15.  https://doi.org/10.1126/scitranslmed.3001604.CrossRefPubMedPubMedCentralGoogle Scholar
  200. 200.
    Zhang C, Maslov K, Wang LV. Subwavelength-resolution label-free photoacoustic microscopy of optical absorption in vivo. Opt Lett. 2010;35(19):3195–7.  https://doi.org/10.1364/OL.35.003195.CrossRefPubMedPubMedCentralGoogle Scholar
  201. 201.
    Hu S, Wang LV. Optical-resolution photoacoustic microscopy: auscultation of biological systems at the cellular level. Biophys J. 2013;105(4):841–7.  https://doi.org/10.1016/j.bpj.2013.07.017.CrossRefPubMedPubMedCentralGoogle Scholar
  202. 202.
    Stoffels I, Morscher S, Helfrich I, Hillen U, Leyh J, Burton NC, et al. Metastatic status of sentinel lymph nodes in melanoma determined noninvasively with multispectral optoacoustic imaging. Sci Transl Med. 2015;7(317):317ra199.  https://doi.org/10.1126/scitranslmed.aad1278.CrossRefPubMedGoogle Scholar
  203. 203.
    Zhou Y, Tripathi SV, Rosman I, Ma J, Hai P, Linette GP, et al. Noninvasive determination of melanoma depth using a handheld photoacoustic probe. J Invest Dermatol. 2017.  https://doi.org/10.1016/j.jid.2017.01.016.
  204. 204.
    Ng JC, Swain S, Dowling JP, Wolfe R, Simpson P, Kelly JW. The impact of partial biopsy on histopathologic diagnosis of cutaneous melanoma: experience of an Australian tertiary referral service. Arch Dermatol. 2010;146(3):234–9.  https://doi.org/10.1001/archdermatol.2010.14.CrossRefPubMedGoogle Scholar
  205. 205.
    Galanzha EI, Shashkov EV, Spring PM, Suen JY, Zharov VP. In vivo, noninvasive, label-free detection and eradication of circulating metastatic melanoma cells using two-color photoacoustic flow cytometry with a diode laser. Cancer Res. 2009;69(20):7926–34.  https://doi.org/10.1158/0008-5472.CAN-08-4900.CrossRefPubMedPubMedCentralGoogle Scholar
  206. 206.
    Federici A, Dubois A. Full-field optical coherence microscopy with optimized ultrahigh spatial resolution. Opt Lett. 2015;40(22):5347–50.  https://doi.org/10.1364/OL.40.005347.CrossRefPubMedGoogle Scholar
  207. 207.
    Tsai C-C, Wang Y-T, Ho T-S, Lin M-Y, Tjiu J-W, Hsu K-Y, et al. Mirau-based full-field time-domain optical coherence tomography using Ce3+ : YAG crystal fiber light source. In: Proc. SPIE 8802, optical coherence tomography and coherence techniques VI, 880209. June 18, 2013.  https://doi.org/10.1117/12.2032478.
  208. 208.
    Lee KS, Zhao H, Ibrahim SF, Meemon N, Khoudeir L, Rolland JP. Three-dimensional imaging of normal skin and nonmelanoma skin cancer with cellular resolution using Gabor domain optical coherence microscopy. J Biomed Opt. 2012;17(12):126006.  https://doi.org/10.1117/1.JBO.17.12.126006.CrossRefPubMedPubMedCentralGoogle Scholar
  209. 209.
    Murali S, Meemon P, Lee KS, Kuhn WP, Thompson KP, Rolland JP. Assessment of a liquid lens enabled in vivo optical coherence microscope. Appl Opt. 2010;49(16):D145–56.  https://doi.org/10.1364/AO.49.00D145.CrossRefPubMedGoogle Scholar
  210. 210.
    Song S, Xu J, Wang RK. Long-range and wide field of view optical coherence tomography for in vivo 3D imaging of large volume object based on akinetic programmable swept source. Biomed Opt Express. 2016;7(11):4734–48.  https://doi.org/10.1364/BOE.7.004734.CrossRefPubMedPubMedCentralGoogle Scholar
  211. 211.
    Kong K, Rowlands CJ, Varma S, Perkins W, Leach IH, Koloydenko AA, et al. Diagnosis of tumors during tissue-conserving surgery with integrated autofluorescence and Raman scattering microscopy. Proc Natl Acad Sci U S A. 2013;110(38):15189–94.  https://doi.org/10.1073/pnas.1311289110.CrossRefPubMedPubMedCentralGoogle Scholar
  212. 212.
    Wang Z. Multiwavelength reflectance confocal microscopy for immune cell identification. Rochester: University of Rochester; 2008.Google Scholar
  213. 213.
    Iftimia N, Peterson G, Chang EW, Maguluri G, Fox W, Rajadhyaksha M. Combined reflectance confocal microscopy-optical coherence tomography for delineation of basal cell carcinoma margins: an ex vivo study. J Biomed Opt. 2016;21(1):16006.  https://doi.org/10.1117/1.JBO.21.1.016006.CrossRefPubMedGoogle Scholar
  214. 214.
    Zhang EZ, Povazay B, Laufer J, Alex A, Hofer B, Pedley B, et al. Multimodal photoacoustic and optical coherence tomography scanner using an all optical detection scheme for 3D morphological skin imaging. Biomed Opt Express. 2011;2(8):2202–15.  https://doi.org/10.1364/BOE.2.002202.CrossRefPubMedPubMedCentralGoogle Scholar
  215. 215.
    Gerami P, Yao Z, Polsky D, Jansen B, Busam K, Ho J, et al. Development and validation of a noninvasive 2-gene molecular assay for cutaneous melanoma. J Am Acad Dermatol. 2017;76(1):114–20. e2.  https://doi.org/10.1016/j.jaad.2016.07.038.CrossRefPubMedGoogle Scholar
  216. 216.
    Clarke LE, Warf MB, Flake DD. 2nd, Hartman AR, Tahan S, Shea CR et al. clinical validation of a gene expression signature that differentiates benign nevi from malignant melanoma. J Cutan Pathol. 2015;42(4):244–52.  https://doi.org/10.1111/cup.12475.CrossRefPubMedGoogle Scholar
  217. 217.
    Clarke LE, Flake DD 2nd, Busam K, Cockerell C, Helm K, McNiff J, et al. An independent validation of a gene expression signature to differentiate malignant melanoma from benign melanocytic nevi. Cancer. 2017;123(4):617–28.  https://doi.org/10.1002/cncr.30385.CrossRefPubMedGoogle Scholar
  218. 218.
    Gerami P, Cook RW, Wilkinson J, Russell MC, Dhillon N, Amaria RN, et al. Development of a prognostic genetic signature to predict the metastatic risk associated with cutaneous melanoma. Clin Cancer Res. 2015;21(1):175–83.  https://doi.org/10.1158/1078-0432.CCR-13-3316.CrossRefPubMedGoogle Scholar
  219. 219.
    Zager JS, Messina SJ, Sondak VK, Ferris L, Cook RW, Middlebrook B, et al. Performance of a 31-gene expression profile in a previously unreported cohort of 334 cutaneous melanoma patients. J Clin Oncol. 2016;(34, Suppl):9581.Google Scholar
  220. 220.
    Khoja L, Lorigan P, Dive C, Keilholz U, Fusi A. Circulating tumour cells as tumour biomarkers in melanoma: detection methods and clinical relevance. Ann Oncol. 2015;26(1):33–9.  https://doi.org/10.1093/annonc/mdu207.CrossRefPubMedGoogle Scholar
  221. 221.
    Scoggins CR, Ross MI, Reintgen DS, Noyes RD, Goydos JS, Beitsch PD, et al. Prospective multi-institutional study of reverse transcriptase polymerase chain reaction for molecular staging of melanoma. J Clin Oncol. 2006;24(18):2849–57.  https://doi.org/10.1200/JCO.2005.03.2342.CrossRefPubMedPubMedCentralGoogle Scholar
  222. 222.
    Ashida A, Sakaizawa K, Mikoshiba A, Uhara H, Okuyama R. Quantitative analysis of the BRAF V600E mutation in circulating tumor-derived DNA in melanoma patients using competitive allele-specific TaqMan PCR. Int J Clin Oncol. 2016;21(5):981–8.  https://doi.org/10.1007/s10147-016-0976-y.CrossRefPubMedGoogle Scholar
  223. 223.
    Schreuer M, Meersseman G, Van Den Herrewegen S, Jansen Y, Chevolet I, Bott A, et al. Quantitative assessment of BRAF V600 mutant circulating cell-free tumor DNA as a tool for therapeutic monitoring in metastatic melanoma patients treated with BRAF/MEK inhibitors. J Transl Med. 2016;14:95.  https://doi.org/10.1186/s12967-016-0852-6.CrossRefPubMedPubMedCentralGoogle Scholar
  224. 224.
    Byrum SD, Larson SK, Avaritt NL, Moreland LE, Mackintosh SG, Cheung WL, et al. Quantitative proteomics identifies activation of hallmark pathways of cancer in patient melanoma. J Proteomics Bioinform. 2013;6(3):43–50.  https://doi.org/10.4172/jpb.1000260.CrossRefPubMedGoogle Scholar
  225. 225.
    Ribero S, Longo C, Glass D, Nathan P, Bataille V. What is new in melanoma genetics and treatment. Dermatology. 2016.  https://doi.org/10.1159/000445767.
  226. 226.
    Breast Cancer Linkage Consortium. Cancer risks in BRCA2 mutation carriers. J Natl Cancer Inst. 1999;91(15):1310–6.CrossRefGoogle Scholar
  227. 227.
    Bubien V, Bonnet F, Brouste V, Hoppe S, Barouk-Simonet E, David A, et al. High cumulative risks of cancer in patients with PTEN hamartoma tumour syndrome. J Med Genet. 2013;50(4):255–63.  https://doi.org/10.1136/jmedgenet-2012-101339.CrossRefPubMedGoogle Scholar
  228. 228.
    Fitzpatrick TB. The validity and practicality of sun-reactive skin types I through VI. Arch Dermatol. 1988;124(6):869–71.CrossRefPubMedGoogle Scholar
  229. 229.
    Howlader N, Noone AM, Krapcho M, Miller D, Bishop K, Altekruse SF, et al., editors. SEER Cancer Statistics Review (CSR) 1975–2013. Bethsda, MD: National Cancer Institute; 2016. Accessed March 20, 2017Google Scholar
  230. 230.
    Tkaczyk E. Innovations and developments in dermatologic non-invasive optical imaging and potential clinical applications. Acta Derm Venereol. 2017.  https://doi.org/10.2340/00015555-2717.
  231. 231.
    Demirli R, Otto P,Viswanathan R, Patwardhan S, Larkey J. RBX® Technology Overview. n.d. http://www.canfieldsci.com/FileLibrary/RBX%20tech%20overview-LoRz1.pdf. Accessed January 18, 2017.
  232. 232.
    . Learn about MelaFind and Melanoma. Strata Skin Sciences. http://www.melafind.com/melafind/. Accessed January 2, 2017.
  233. 233.
    Verisante Technology, Inc. Aura http://www.verisante.com/aura/medical_professional/. Accessed January 16, 2017.
  234. 234.
    Choi J, Choo J, Chung H, Gweon DG, Park J, Kim HJ, et al. Direct observation of spectral differences between normal and basal cell carcinoma (BCC) tissues using confocal Raman microscopy. Biopolymers. 2005;77(5):264–72.  https://doi.org/10.1002/bip.20236.CrossRefPubMedGoogle Scholar
  235. 235.
    MPTflex Multiphoton Laser Tomography. JenLab GmbH. http://www.jenlab.de/MPTflex.114.0.html. Accessed January 16, 2017.

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Clara Curiel-Lewandrowski
    • 1
  • Clara Stemwedel
    • 2
  • Mihaela Balu
    • 3
  • Suephy C. Chen
    • 4
  • Laura K. Ferris
    • 5
  • Pedram Gerami
    • 6
    • 7
  • Adele C. Green
    • 8
    • 9
  • Mariah M. Johnson
    • 2
  • Lois J. Loescher
    • 10
  • Josep Malvehy
    • 11
  • Ashfaq A. Marghoob
    • 12
  • Kathryn Martires
    • 13
  • Giovanni Pellacani
    • 14
  • Tracy Petrie
    • 2
  • Susana Puig
    • 11
  • Inga Saknite
    • 15
  • Susan M. Swetter
    • 16
    • 17
    • 18
  • Per Svedenhag
    • 19
  • Eric R. Tkaczyk
    • 20
  • Oliver J. Wisco
    • 2
  • Sancy A. Leachman
    • 2
  1. 1.Department of DermatologyThe University of ArizonaTucsonUSA
  2. 2.Department of DermatologyOregon Health & Science UniversityPortlandUSA
  3. 3.Department of Surgery, Beckman Laser Institute and Medical ClinicUniversity of CaliforniaIrvineUSA
  4. 4.Department of DermatologyEmory University and Atlanta VA Medical CenterAtlantaUSA
  5. 5.Department of DermatologyUniversity of Pittsburgh Medical CenterPittsburghUSA
  6. 6.Skin Cancer Institute of Northwestern Medicine (SCIN-Med)ChicagoUSA
  7. 7.Melanoma Program of SCIN-Med, Department of DermatologyNorthwestern HospitalChicagoUSA
  8. 8.Population HealthHerstonAustralia
  9. 9.QIMR Berghofer Medical Research Institute and CRUK Manchester InstituteUniversity of ManchesterManchesterUK
  10. 10.The University of Arizona, College of NursingTucsonUSA
  11. 11.Department of DermatologyHospital Clinic BarcelonaBarcelonaSpain
  12. 12.Division of Dermatology, Department of MedicineMemorial Sloan Kettering Cancer CenterNew YorkUSA
  13. 13.Stanford University School of Medicine, Department of DermatologyPalo AltoUSA
  14. 14.Department of DermatologyUniversity of Modena and Reggio EmiliaModenaItaly
  15. 15.Vanderbilt University Medical Center, Department of MedicineVanderbilt Health, One Hundred OaksNashvilleUSA
  16. 16.Stanford University Medical Center and Cancer InstituteStanfordUSA
  17. 17.VA Palo Alto Health Care SystemPalo AltoUSA
  18. 18.Department of Dermatology/Cutaneous OncologyStanford Dermatology/Cutaneous OncologyStanfordUSA
  19. 19.SciBaseStockholmSweden
  20. 20.Cutaneous Imaging Clinic, Vanderbilt University Medical Center, Department of MedicineOne Hundred Oaks—DermatologyNashvilleUSA

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