Emerging technologies for the detection of melanoma: achieving better outcomes

Cila Herman, Cila Herman

Abstract

Every year around 2.5-3 million skin lesions are biopsied in the US, and a fraction of these - between 50,000 and 100,000 - are diagnosed as melanoma. Diagnostic instruments that allow early detection of melanoma are the key to improving survival rates and reducing the number of unnecessary biopsies, the associated morbidity, and the costs of care. Advances in technology over the past 2 decades have enabled the development of new, sophisticated test methods, which are currently undergoing laboratory and small-scale clinical testing. This review highlights and compares some of the emerging technologies that hold the promise of melanoma diagnosis at an early stage of the disease. The needs for detection at different levels (patient, primary care, specialized care) are discussed, and three broad classes of instruments are identified that are capable of satisfying these needs. Technical and clinical requirements on the diagnostic instruments are introduced to aid the comparison and evaluation of new technologies. White- and polarized-light imaging, spatial and spectroscopic multispectral methods, quantitative thermographic imaging, confocal microscopy, Optical Coherence Tomography (OCT), and Terahertz (THZ) imaging methods are highlighted in light of the criteria identified in the review. Based on the properties, possibilities, and limitations of individual methods, those best suited for a particular setting are identified. Challenges faced in development and wide-scale application of novel technologies are addressed.

Keywords: Infrared imaging; in vivo diagnostics; melanoma detection and diagnosis; quantitative imaging; thermography.

Figures

Figure 1
Figure 1
EM radiation spectrum shown in terms of wavelength and frequency, characteristic ranges used in medical diagnostics. Note: The black-body radiation spectrum for thermal and IR radiation is shown in the top portion of the image.
Figure 2
Figure 2
(A) Interaction of radiation with tissue (absorption, reflection), radiation source, and detector; (B) digital white light, and (C) dermoscopy images of a melanoma lesion, capturing the reflected portion of the incident EM radiation.
Figure 3
Figure 3
(A) White light photograph of the larger body surface area with a cluster of pigmented lesions, adhesive window serving as thermal marker and reference IR image of the region at ambient temperature; (B) the same area 2s into the thermal recovery and magnified section of the melanoma lesion and its surroundings; (C) temperature profiles of the lesion and the surrounding normal skin during the thermal recovery process.–
Figure 4
Figure 4
Image registration steps in melanoma detection: (A) white light image with characteristic points for image analysis (rectangular marker and lesion center), (B) edge and corner correspondence in IR image, (C) lesion contour registration in the IR images via transformation matrix.
Figure 5
Figure 5
Motion correction for compensation of the subject’s involuntary movement in IR images recorded at different time instants. Notes: The effects of motion correction can be observed in the corrected columns as dark regions around the edges of the images. White arrows in the uncorrected images represent the direction and magnitude of motion of the subject.
Figure 6
Figure 6
Impact of the motion correction on the reconstructed temperature profiles. Notes: Without motion correction (left), the noise is of the order of magnitude of the measured signal. With motion correction. the temperature of the melanoma lesion is higher than the temperature of the surrounding healthy tissue. This temperature difference can be used to quantify the malignant potential of the cancerous lesion.
Figure 7
Figure 7
(A) VivaScope imaging system for dermatology; (B) parallel skin layers imaged by the CSLM system; (C) CSLM images of different skin layers.

References

    1. National Cancer Institute. SEER Stat Fact Sheets: Melanoma of the Skin. 2012. [Accessed August 3, 2012]. Available from: .
    1. Skin Cancer Foundation. Skin Cancer Facts. 2012. [Accessed August 3, 2012]. Available from: .
    1. Jemal A, Siegel R, Ward E, Hao Y, Xu J, Thun MJ. Cancer statistics, 2009. CA Cancer J Clin. 2009;59(4):225–249.
    1. Elder D. Tumor progression, early diagnosis and prognosis of melanoma. Acta Oncol. 1999;38(5):535–547.
    1. Fecher LA, Cummings SD, Keefe MJ, Alani RM. Toward a molecular classification of melanoma. J Clin Oncol. 2007;25(12):1606–1620.
    1. Wartman D, Weinstock M. Are we overemphasizing sun avoidance in protection from melanoma? Cancer Epidemiol Biomarkers Prev. 2008;17(3):469–470.
    1. Geller AC, Swetter SM, Brooks K, Demierre MF, Yaroch AL. Screening, early detection, and trends for melanoma: current status (2000–2006) and future directions. J Am Acad Dermatol. 2007;57(4):555–572. quiz 573–576.
    1. Friedman RJ, Rigel DS, Kopf AW. Early detection of malignant melanoma: the role of physician examination and self-examination of the skin. CA Cancer J Clin. 1985;35(3):130–151.
    1. Abbasi NR, Shaw HM, Rigel DS, et al. Early diagnosis of cutaneous melanoma: revisiting the ABCD criteria. JAMA. 2004;292(22):2771–2776.
    1. Thomas L, Tranchand P, Berard F, Secchi T, Colin C, Moulin G. Semiological value of ABCDE criteria in the diagnosis of cutaneous pigmented tumors. Dermatology. 1998;197(1):11–17.
    1. Wang SQ, Rabinovitz H, Kopf AW, Oliviero M. Current technologies for the in vivo diagnosis of cutaneous melanomas. Clin Dermatol. 2004;22(3):217–222.
    1. Psaty EL, Halpern AC. Current and emerging technologies in melanoma diagnosis: the state of the art. Clin Dermatol. 2009;27(1):35–45.
    1. Patel JK, Konda S, Perez OA, Amini S, Elgart G, Berman B. Newer technologies/techniques and tools in the diagnosis of melanoma. Eur J Dermatol. 2008;18(6):617–631.
    1. Andreassi M, Andreassi L. Utility and limits of noninvasive methods in dermatology. Expert Rev Dermatol. 2007;2(3):249–255.

    1. http://www.melafind.com

    1. http://www.melasciences.com

    1. Gutkowicz-Krusin D, Elbaum M, Jacobs A, et al. Precision of automatic measurements of pigmented skin lesion parameters with a MelaFind(TM) multispectral digital dermoscope. Melanoma Res. 2000;10(6):563–570.
    1. Elbaum M, Kopf AW, Rabinovitz HS, et al. Automatic differentiation of melanoma from melanocytic nevi with multispectral digital dermoscopy: a feasibility study. J Am Acad Dermatol. 2001;44(2):207–218.
    1. Elbaum M. Computer-aided melanoma diagnosis. Dermatol Clin. 2002;20(4):735–747. x–xi.
    1. Elbaum M. Automated diagnosis: illustrated by the MelaFind system. In: Marghoob AA, Kopf AW, Braun R, editors. An Atlas of Dermoscopy. Abingdon: Taylor and Francis; 2004. pp. 325–341.
    1. Friedman RJ, Gutkowicz-Krusin D, Farber MJ, et al. The diagnostic performance of expert dermoscopists vs a computer-vision system on small-diameter melanomas. Arch Dermatol. 2008;144(4):476–482.
    1. Monheit G, Cognetta AB, Ferris L, et al. The performance of MelaFind: a prospective multicenter study. Arch Dermatol. 2011;147(2):188–194.
    1. Rigel DS, Roy M, Yoo J, Cockerell CJ, Robinson JK, White R. Impact of guidance from a computer-aided multispectral digital skin lesion analysis device on decision to biopsy lesions clinically suggestive of melanoma. Arch Dermatol. 2012;148(4):541–543.
    1. Krafft C, Sergo V. Biomedical applications of Raman and infrared spectroscopy to diagnose tissues. Spectroscopy. 2006;20(5–6):195–218.
    1. Garidel P. Insights in the biochemical composition of skin as investigated by micro infrared spectroscopic imaging. Phys Chem Chem Phys. 2003;5:2673–2679.
    1. Lucassen GW, Caspers PJ, Puppels GJ. In vivo infrared and Raman spectroscopy of stratum corneum. Proc SPIE. 1998;3257:52–60.
    1. Mendelsohn R, Flach CR, Moore DJ. Determination of molecular conformation and permeation in skin via IR spectroscopy, microscopy and imaging. Biochim Biophys Acta. 2006;1758(7):923–933.
    1. Mendelsohn R, Rerek ME, Moore DJ. Infrared spectroscopy and microscopic imaging of stratum corneum models and skin. Phys Chem Chem Phys. 2000;2:4651–4657.
    1. Mendelsohn R, Chen HC, Rerek ME, Moore DJ. Infrared microscopic imaging maps the spatial distribution of exogenous molecules in skin. J Biomed Opt. 2003;8(2):185–190.
    1. Xiao C, Moore DJ, Flach CR, Mendelsohn R. Permeation of dimyristoylphosphatidylcholine into skin – structural and spatial information from IR and Raman microscopic imaging. Vib Spectrosc. 2005;38(1–2):151–158.
    1. Xiao C, Moore DJ, Rerek ME, Flach CR, Mendelsohn R. Feasibility of tracking phospholipid permeation from IR and Raman microscopic imaging. J Invest Dermatol. 2005;124(3):622–632.
    1. Zhang G, Moore DJ, Mendelsohn R, Flach CR. Vibrational microspectroscopy and imaging of molecular composition and structure during human corneocyte maturation. J Invest Dermatol. 2006;126(5):1088–1094.
    1. Bommannan D, Potts RO, Guy RH. Examination of stratum corneum barrier function in vivo by infrared spectroscopy. J Invest Dermatol. 1990;95(4):403–408.
    1. Bhargava R, Levin IW. Gram-Schmidt orthogonalization for rapid reconstruction of Fourier transform infrared spectroscopic imaging data. Appl Spectrosc. 2004;58(8):995–1000.
    1. Xiao C, Flach CR, Marcott C, Mendelsohn R. Uncertainties in depth determination and comparison of multivariate with univariate analysis in confocal Raman studies of a laminated polymer and skin. Appl Spectrosc. 2004;58(4):382–389.
    1. Caspers PJ, Lucassen GW, Carter EA, Bruining HA, Puppels GJ. In vivo confocal Raman microspectroscopy of the skin: noninvasive determination of molecular concentration profiles. J Invest Dermatol. 2001;116(3):434–442.
    1. Caspers PJ, Lucassen GW, Puppels GJ. Combined in vivo confocal Raman spectroscopy and confocal microscopy of human skin. Biophys J. 2003;85(1):572–580.
    1. Percot A, Lafleur M. Direct observation of domains in model stratum corneum lipid mixtures by Raman microspectroscopy. Biophys J. 2001;81(4):2144–2153.
    1. Tfalyli A, Piot O, Durlach A, Bernard P, Manfait M. Discriminating nevus and melanoma on paraffin-embedded skin biopsies using FTIR microspectroscopy. Biochim Biophys Acta. 2005;1724(3):262–269.
    1. Lasch P, Naumann D. FT-IR microspectroscopic imaging of human carcinoma thin sections based on pattern recognition techniques. Cell Mol Biol (Noisy-le-grand) 1998;44(1):189–202.
    1. Gniadecka M, Philipsen PA, Sigurdsson S, et al. Melanoma diagnosis by Raman spectroscopy and neural networks: structure alteration in proteins and lipids in intact cancer tissue. J Invest Dermatol. 2004;122(2):443–449.
    1. Nijssen A, Bakker Schut TC, Heule F, et al. Discriminating basal cell carcinoma from its surrounding tissue by Raman spectroscopy. J Invest Dermatol. 2002;119(1):64–69.
    1. Huang Z, Lui H, Chen XK, Alajlan A, McLean DI, Zeng H. Raman spectroscopy of in vivo cutaneous melanin. J Biomed Opt. 2004;9(6):1198–1205.
    1. Cristofolini M, Piscioli F, Valdagni C, Della Selva A. Correlations between thermography and morphology of primary cutaneous malignant melanomas. Acta Thermogr. 1976;1(1):3–11.
    1. Hartmann M, Kunze J, Friedel S. Telethermography in the diagnostic and management of malignant melanomas. J Dermatol Surg Oncol. 1981;7(3):213–218.
    1. Di Carlo A. Thermography and the possibilities for its applications in clinical and experimental dermatology. Clin Dermatol. 1995;13(4):329–336.
    1. Diakides NA. 1998 Infrared imaging: an emerging technology in medicine. IEEE Eng Med Biol Mag. 1998;17(4):17–18.
    1. Fauci M, Breiter R, Cabanski W, et al. Medical infrared imaging – differentiating facts from fiction, and the impact of high precision quantum well infrared photodetector camera systems, and other factors, in its reemergence. Infrared Phys. 2001;42(3–5):337–344.
    1. Gulyaev YV, Markov AG, Koreneva LG, Zakharov PV. Dynamical infrared thermography in humans. IEEE Eng Med Biol Mag. 1995;14:766–771.
    1. Ring EFJ, Ammer K. Infrared thermal imaging in medicine. Physiol Meas. 2012;33(3):R33–R46.
    1. Herman C, Pirtini Cetingul M. Quantitative visualization and detection of skin cancer using dynamic thermal imaging. J Vis Exp. 2011;(51):e2679.
    1. Pirtini Cetingul M, Herman C. Heat transfer model of skin tissue for the detection of lesions: sensitivity analysis. Phys Med Biol. 2010;55(19):5933–5951.
    1. Pirtini Cetingul M, Herman C. Quantification of the thermal signature of a melanoma lesion. Int J Therm Sci. 2011;50(4):421–431.
    1. Pirtini Cetingul M, Herman C. The assessment of melanoma risk using the dynamic infrared imaging technique. J Therm Sci Eng Appl. 2011;3(3):031006-1–031006-9.
    1. Pirtini Cetingul M, Alani RM, Herman C. Quantitative evaluation of skin lesions using transient thermal imaging. Proceedings of the International Heat Transfer Conference, IHTC14; August 8–13, 2010; Washington, DC, USA.
    1. Pirtini Cetingul M, Alani RM, Herman C. Detection of skin cancer using skin transient thermal imaging. Proceedings of the ASME 2010 Summer Bioengineering Conference, SBC2010; June 15–19, 2010; Naples, Florida, USA.
    1. Pirtini Cetingul M, Cetingul HE, Herman C. Analysis of transient thermal images to distinguish melanoma from dysplastic nevi. Proceedings of the SPIE Medical Imaging Conference; February 12–17, 2011; Lake Buena Vista, FL, USA.
    1. Harris C, Stephens M. A combined corner and edge detector. Proceedings of the 4th Alvey Vision Conference; 1988.
    1. Hartley R, Zisserman A. Multiple View Geometry in Computer Vision. Cambridge: Cambridge University Press; 2003. pp. 32–33.
    1. Grady L. Random walks for image segmentation, pattern analysis and machine intelligence. IEEE Trans Pattern Anal Mach Intell. 2006;28(11):1768.
    1. Odobez JM, Bouthemy P. Robust multiresolution estimation of parametric motion models. J Vis Commun Image Represent. 1995;6(4):348–365.
    1. Pawley JB. Handbook of Biological Confocal Microscopy. 2nd ed. New York: Plenum Press; 1995.
    1. Webb RH. Confocal optical microscopy. Rep Prog Phys. 1996;59(3):427–471.
    1. Rajadhyaksha M, Zavislan JM. Confocal reflectance microscopy of unstained tissue in vivo. Retin Lipid-Soluble Vitam Clin Pract. 1998;14(1):26–30.
    1. Rajadhyaksha M, Grossman M, Esterowitz D, Webb RH, Anderson RR. In vivo confocal scanning laser microscopy of human skin: melanin provides a good contrast. J Invest Dermatol. 1995;104(6):946–952.
    1. Busam KJ, Hester K, Charles C, et al. Detection of clinically amelanotic malignant melanoma and assessment of its margins by in vivo confocal scanning laser microscopy. Arch Dermatol. 2001;137(7):923–929.
    1. Charles CA, Marghoob AA, Busam KJ, Clark-Loeser L, Halpern AC. Melanoma or pigmented basal cell carcinoma: a clinical-pathologic correlation with dermoscopy, in vivo confocal scanning laser microscopy, and routine histology. Skin Res Technol. 2002;8(4):282–287.
    1. Marghoob AA, Charles C, Busam KJ, et al. In vivo confocal scanning laser microscopy of a series of congenital melanocytic nevi suggestive of having developed malignant melanoma. Arch Dermatol. 2005;141(11):1401–1412.
    1. Scope A, Benvenuto-Andrae C, Agero AL, et al. In vivo reflectance confocal microscopy imaging of melanocytic skin lesions: Consensus terminology glossary and illustrative images. J Am Acad Dermatol. 2007;57(4):644–658.
    1. Aghassi D, Anderson RR, Gonzales S. Confocal laser microscopic imaging of actinic keratosis in vivo: a preliminary report. J Am Acad Dermatol. 2000;43(1):42–48.
    1. Busam KJ, Hester K, Charles C, et al. Detection of clinically amelanotic malignant melanoma and assessment of its margins by in vivo confocal scanning laser microscopy. Arch Dermatol. 2001;137(7):923–929.

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