A1 Journal article (refereed)
Hyperspectral Imaging for Non-invasive Diagnostics of Melanocytic Lesions (2022)


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, Article adv00815. https://doi.org/10.2340/actadv.v102.2045


JYU authors or editors


Publication details

All authors or editorsPaoli, John; Pölönen, Ilkka; Salmivuori, Mari; Räsänen, Janne; Zaar, Oscar; Polesie, Sam; Koskenmies, Sari; Pitkänen, Sari; Övermark, Meri; Isoherranen, Kirsi; et al.

Journal or seriesActa Dermato-Venereologica

ISSN0001-5555

eISSN1651-2057

Publication year2022

Publication date14/11/2022

Volume102

Article numberadv00815

PublisherMedical Journals Sweden AB

Publication countrySweden

Publication languageEnglish

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

Publication open accessOpenly available

Publication channel open accessOpen Access channel

Publication is parallel published (JYX)https://jyx.jyu.fi/handle/123456789/84594


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.


Keywordsskin cancermelanomadiagnosticshyperspectral imagingmachine learning

Free keywordshyperspectral imaging; non-invasive diagnostic; machine learning; malignant melanoma


Contributing organizations


Ministry reportingYes

Reporting Year2022

JUFO rating2


Last updated on 2024-15-06 at 00:46