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Skin Res Technol. 2013 May;19(2):75-83. doi: 10.1111/srt.12008. Epub 2013 Jan 27.

Electrical impedance spectroscopy as a potential adjunct diagnostic tool for cutaneous melanoma.

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1
Department of Dermatology, Elbe-Klinikum Buxtehude, Buxtehude, Germany.

Abstract

BACKGROUND:

Previous studies have shown statistically significant differences in electrical impedance between various cutaneous lesions. Electrical impedance spectroscopy (EIS) may therefore be able to aid clinicians in differentiating between benign and malignant skin lesions.

OBJECTIVES:

The aim of the study was to develop a classification algorithm to distinguish between melanoma and benign lesions of the skin with a sensitivity of at least 98% and a specificity approximately 20 per cent higher than the diagnostic accuracy of dermatologists.

PATIENTS/METHODS:

A total of 1300 lesions were collected in a multicentre, prospective, non-randomized clinical trial from 19 centres around Europe. All lesions were excised and subsequently evaluated independently by a panel of three expert dermatopathologists. From the data two classification algorithms were developed and verified.

RESULTS:

For the first classification algorithm, approximately 40% of the data were used for calibration and 60% for testing. The observed sensitivity for melanoma was 98.1% (101/103), non-melanoma skin cancer 100% (25/25) and dysplastic nevus with severe atypia 84.2% (32/38). The overall observed specificity was 23.6% (66/280). For the second classification algorithm, approximately 55% of the data were used for calibration. The observed sensitivity for melanoma was 99.4% (161/162), for non-melanoma skin cancer was 98.0% (49/50) and dysplastic nevus with severe atypia was 93.8% (60/64). The overall observed specificity was 24.5% (116/474).

CONCLUSION:

EIS has the potential to be an adjunct diagnostic tool to help clinicians differentiate between benign and malignant (melanocytic and non-melanocytic) skin lesions. Further studies are needed to confirm the validity of the automatic assessment algorithm.

PMID:
23350668
DOI:
10.1111/srt.12008
[Indexed for MEDLINE]
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