Hyperspectral Imaging for Non-invasive Diagnostics of Melanocytic Lesions

Authors

  • John Paoli Department of Dermatology and Venereology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Guthenburg, Sweden; Department of Dermatology and Venereology, Region Västra Götaland, Sahlgrenska University Hospital, Gothenburg, Sweden
  • Ilkka Pölönen Faculty of Information Technology, University of Jyväskylä, Finland
  • Mari Salmivuori Department of Dermatology and Allergology, Päijät-Häme Social and Health Care Group, Lahti, Finland; Department of Dermatology and Allergology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
  • Janne Räsänen Department of Dermatology and Allergology, Päijät-Häme Social and Health Care Group, Lahti, Finland; Department of Dermatology, Tampere University Hospital and Faculty of Medicine and Medical technology, Tampere University, Tampere, Finland
  • Oscar Zaar Department of Dermatology and Venereology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Guthenburg, Sweden; Department of Dermatology and Venereology, Region Västra Götaland, Sahlgrenska University Hospital, Gothenburg, Sweden
  • Sam Polesie Department of Dermatology and Venereology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Guthenburg, Sweden; Department of Dermatology and Venereology, Region Västra Götaland, Sahlgrenska University Hospital, Gothenburg, Sweden
  • Sari Koskenmies Department of Dermatology and Allergology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
  • Sari Pitkänen Department of Dermatology and Allergology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
  • Meri Övermark Department of Dermatology and Allergology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
  • Kirsi Isoherranen Department of Dermatology and Allergology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
  • Susanna Juteau Department of Pathology, University of Helsinki and HUSLAB, Helsinki, Finland
  • Annamari Ranki Department of Dermatology and Allergology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
  • Mari Grönroos Department of Dermatology and Allergology, Päijät-Häme Social and Health Care Group, Lahti, FinlanDepartment of Dermatology and Allergology, Päijät-Häme Social and Health Care Group, Lahti, Finland
  • Noora Neittaanmäki Department of Dermatology and Venereology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Guthenburg, Sweden; Department of Laboratory Medicine, Institute of Biomedicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden; Department of Clinical Pathology, Region Västra Götaland, Sahlgrenska University Hospital, Gothenburg, Sweden

DOI:

https://doi.org/10.2340/actadv.v102.2045

Keywords:

hyperspectral imaging, non-invasive diagnostic, machine learning, malignant melanoma

Abstract

Malignant melanoma poses a clinical diagnostic problem, since a large number of benign lesions are excised to find a single melanoma. This study assessed the accuracy of a novel non-invasive diagnostic technology, hyperspectral imaging, for melanoma detection. Lesions were imaged prior to excision and histopathological analysis. A deep neural network algorithm was trained twice to distinguish between histopathologically verified malignant and benign melanocytic lesions and to classify the separate subgroups. Furthermore, 2 different approaches were used: a majority vote classification and a pixel-wise classification. The study included 325 lesions from 285 patients. Of these, 74 were invasive melanoma, 88 melanoma in situ, 115 dysplastic naevi, and 48 non-dysplastic naevi. The study included a training set of 358,800 pixels and a validation set of 7,313 pixels, which was then tested with a training set of 24,375 pixels. The majority vote classification achieved high overall sensitivity of 95% and a specificity of 92% (95% confidence interval (95% CI) 0.024–0.029) in differentiating malignant from benign lesions. In the pixel-wise classification, the overall sensitivity and specificity were both 82% (95% CI 0.005–0.005). When divided into 4 subgroups, the diagnostic accuracy was lower. Hyperspectral imaging provides high sensitivity and specificity in distinguishing between naevi and melanoma. This novel method still needs further validation.

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References

Gershenwald JE, Scolyer RA, Hess KR, Sondak VK, Long GV, Ross MI, et al. Melanoma staging: evidence-based changes in the American Joint Committee on Cancer eighth edition cancer staging manual. CA Cancer J Clin 2017; 67: 472-492.

https://doi.org/10.3322/caac.21409

Gordon LG, Rowell D. Health system costs of skin cancer and cost-effectiveness of skin cancer prevention and screening: a systematic review. Eur J Cancer Prev 2015; 24: 141-149.

https://doi.org/10.1097/CEJ.0000000000000056

Eriksson T, Tinghög G. Societal cost of skin cancer in Sweden in 2011. Acta Derm Venereol 2015; 95: 347-348.

https://doi.org/10.2340/00015555-1938

Alexandrescu DT. Melanoma costs: a dynamic model comparing estimated overall costs of various clinical stages. Dermatol Online J 2009; 15: 1.

https://doi.org/10.5070/D353F8Q915

Baade PD, Youl PH, Janda M, Whiteman DC, Del Mar CB, Aitken JF. Factors associated with the number of lesions excised for each skin cancer: a study of primary care physicians in Queensland, Australia. Arch Dermatol 2008; 144: 1468-1476.

https://doi.org/10.1001/archderm.144.11.1468

Petty AJ, Ackerson B, Garza R, Peterson M, Liu B, Green C, et al. Meta-analysis of number needed to treat for diagnosis of melanoma by clinical setting. J Am Acad Dermatol 2020; 82: 1158-1165.

https://doi.org/10.1016/j.jaad.2019.12.063

Terushkin V, Ng E, Stein JA, Katz S, Cohen DE, Meehan S, et al. A prospective study evaluating the utility of a 2-mm biopsy margin for complete removal of histologically atypical (dysplastic) nevi. J Am Acad Dermatol 2017; 77: 1096-1099.

https://doi.org/10.1016/j.jaad.2017.07.016

Neittaanmäki N, Salmivuori M, Pölönen I, Jeskanen L, Ranki A, Saksela O, et al. Hyperspectral imaging in detecting dermal invasion in lentigo maligna melanoma. Br J Dermatol 2017; 177: 1742-1744.

https://doi.org/10.1111/bjd.15267

Räsänen J, Salmivuori M, Pölönen I, Grönroos M, Neittaanmäki N. Hyperspectral imaging reveals spectral differences and can distinguish malignant melanoma from pigmented basal cell carcinomas: a pilot study. Acta Derm Venereol 2021; 101: adv00405.

https://doi.org/10.2340/00015555-3755

Neittaanmäki-Perttu N, Grönroos M, Jeskanen L, Pölönen I, Ranki A, Saksela O, Snellman E. Delineating margins of lentigo maligna using a hyperspectral imaging system. Acta Derm Venereol 2015; 95: 549-552.

https://doi.org/10.2340/00015555-2010

Salmivuori M, Neittaanmäki N, Pölönen I, Jeskanen L, Snellman E, Grönroos M. Hyperspectral imaging system in the delineation of ill-defined basal cell carcinomas: a pilot study. J Eur Acad Dermatol Venereol 2019; 33: 71-78.

https://doi.org/10.1111/jdv.15102

Saari H, Pölönen I, Salo H, Honkavaara E, Hakala T, Holmlund C, et al. Miniaturized hyperspectral imager calibration and UAV flight campaigns. International Society for Optics and Photonics. Sensors, Systems, and Next-Generation Satellites XVII 2013; 8889: 88891.

https://doi.org/10.1117/12.2028972

Barun VV, Ivanov AP, VolotovskayaAV. Absorption spectra and light penetration depth of normal and pathologically altered human skin. J Appl Spectrosc 2007; 74: 430-439.

https://doi.org/10.1007/s10812-007-0071-2

Morales G, Sheppard JW, Scherrer B, Shaw JA. Reduced-cost hyperspectral convolutional neural networks. J Appl Remote Sens 2020; 14: 036519.

https://doi.org/10.1117/1.JRS.14.036519

Audebert N, Le Saux B, Lefèvre S. Deep learning for classification of hyperspectral data: a comparative review. IEEE Geoscience and Remote Sensing Magazine 2009; 7: 159-173.

https://doi.org/10.1109/MGRS.2019.2912563

Lunga D, Prasad S, Crawford MM, Ersoy O. Manifold-learning-based feature extraction for classification of hyperspectral data: A review of advances in manifold learning. IEEE Signal Processing Magazine 2013; 31: 55-66.

https://doi.org/10.1109/MSP.2013.2279894

Ghamisi P, Plaza J, Chen Y, Li J, Plaza, AJ. Advanced spectral classifiers for hyperspectral images: a review. IEEE Geoscience and Remote Sensing Magazine 2017; 5: 8-32.

https://doi.org/10.1109/MGRS.2016.2616418

Legendre P. Spatial autocorrelation: trouble or new paradigm? Ecology 1993; 74: 1659-1673.

https://doi.org/10.2307/1939924

Menardi, G, Torelli N. Training and assessing classification rules with imbalanced data. Data Mining and Knowledge Discovery 2014; 28: 92-122.

https://doi.org/10.1007/s10618-012-0295-5

Goodfellow I, Bengio Y, Courville A, Bengio Y. Deep learning. Cambridge: MIT Press; 2016.

Heal CF, Raasch BA, Buettner PG, Weedon D. Accuracy of clinical diagnosis of skin lesions. Br J Dermatol 2008; 159: 661-668.

https://doi.org/10.1111/j.1365-2133.2008.08715.x

Langley RG, Walsh N, Sutherland AE, Propperova I, Delaney L, Morris SF, et al. The diagnostic accuracy of in vivo confocal scanning laser microscopy compared to dermoscopy of benign and malignant melanocytic lesions: a prospective study. Dermatology 2007; 215: 365-372.

https://doi.org/10.1159/000109087

Ferris LK, Harris RJ. New diagnostic aids for melanoma. Dermatol Clin 2012; 30: 535-545.

https://doi.org/10.1016/j.det.2012.04.012

Tkaczyk E. Innovations and developments in dermatologic non-invasive optical imaging and potential clinical applications. Acta Derm Venereol 2017; Suppl 218: 5-13.

https://doi.org/10.2340/00015555-2717

Gambichler T, Plura I, Schmid-Wendtner M, Valavanis K, Kulichova D, Stücker M, et al. High-definition optical coherence tomography of melanocytic skin lesions. J Biophotonics 2015; 8: 681-686.

https://doi.org/10.1002/jbio.201400085

Dimitrow E, Ziemer M, Koehler MJ, Norgauer J, König 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: 1752-1758.

https://doi.org/10.1038/jid.2008.439

Mohr P, Birgersson U, Berking C, Henderson C, Trefzer U, Kemeny L, et al. Electrical impedance spectroscopy as a potential adjunct diagnostic tool for cutaneous melanoma. Skin Res Technol 2013; 19: 75-83.

https://doi.org/10.1111/srt.12008

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: 1099-1107.

https://doi.org/10.1111/bjd.13121

Friedman RJ, Gutkowicz-Krusin D, Farber MJ, Warycha M, Schneider-Kels L, Papastathis N, et al. The diagnostic performance of expert dermoscopists vs a computer-vision system on small-diameter melanomas. Arch Dermatol 2008; 144: 476-482.

https://doi.org/10.1001/archderm.144.4.476

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: 188-194.

https://doi.org/10.1001/archdermatol.2010.302

Glud M, Gniadecki R, Drzewiecki KT. Spectrophotometric intracutaneous analysis versus dermoscopy for the diagnosis of pigmented skin lesions: prospective, double-blind study in a secondary reference centre. Melanoma Res 2009; 19: 176-179.

https://doi.org/10.1097/CMR.0b013e328322fe5f

Moncrieff M, Cotton S, Claridge E, Hall P. Spectrophotometric intracutaneous analysis: a new technique for imaging pigmented skin lesions. Br J Dermatol 2002; 146: 448-457.

https://doi.org/10.1046/j.1365-2133.2002.04569.x

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: 1675-1687.

https://doi.org/10.1088/0031-9155/50/8/004

Marghoob AA, Swindle LD, Moricz CZ, Sanchez Negron FA, Slue B, Halpern AC, et al. Instruments and new technologies for the in vivo diagnosis of melanoma. J Am Acad Dermatol 2003; 49: 777-797.

https://doi.org/10.1016/S0190-9622(03)02470-8

Fink C, Haenssle HA. Non-invasive tools for the diagnosis of cutaneous melanoma. Skin Res Technol 2017; 23: 261-271.

https://doi.org/10.1111/srt.12350

Meng X, Chen J, Zhang Z, Li K, Li J, Yu Z, et al. Non-invasive optical methods for melanoma diagnosis. Photodiagnosis Photodyn Ther 2021; 34: 102266.

https://doi.org/10.1016/j.pdpdt.2021.102266

Li Q, He X, Wang Y, Liu H, Xu D, Guo F. Review of spectral imaging technology in biomedical engineering: achievements and challenges. J Biomed Opt 2013; 18: 100901.

https://doi.org/10.1117/1.JBO.18.10.100901

March J, Hand M, Grossman D. Practical application of new technologies for melanoma diagnosis: Part I. Noninvasive approaches. J Am Acad Dermatol 2015; 72: 929-941.

https://doi.org/10.1016/j.jaad.2015.02.1138

Christensen GB, Nagaoka T, Kiyohara Y, Johansson I, Ingvar C, Nakamura A, et al. Clinical performance of a novel hyperspectral imaging device for cutaneous melanoma and pigmented skin lesions in Caucasian skin. Skin Res Technol 2021; 27: 803-809.

https://doi.org/10.1111/srt.13023

Wang Q, Sun L, Wang Y, Zhou M, Hu M, Chen J, et al. Identification of melanoma from hyperspectral pathology image using 3D convolutional networks. IEEE Trans Med Imaging 2021; 40: 218-227.

https://doi.org/10.1109/TMI.2020.3024923

Wang J, Li Q, Zhou M, Sun L, Hu M, Lyu Y, et al. Identification and measurement on cutaneous melanoma superficial spreading depth using microscopic hyperspectral imaging technology. J Infrared Millim Waves 2020; 39: 749-759.

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Published

2022-11-14

How to Cite

Paoli, J., Pölönen, I., Salmivuori, M., Räsänen, J., Zaar, O., Polesie, S., Koskenmies, S., Pitkänen, S., Övermark, M., Isoherranen, K., Juteau, S., Ranki, A., Grönroos, M., & Neittaanmäki, N. (2022). Hyperspectral Imaging for Non-invasive Diagnostics of Melanocytic Lesions. Acta Dermato-Venereologica, 102, adv00815. https://doi.org/10.2340/actadv.v102.2045