Dermoscopy compared with naked eye examination for the diagnosis of primary melanoma: A meta-analysis of studies performed in a clinical setting

The Sydney Melanoma Diagnostic Centre and The Department of Dermatology, Sydney Cancer Centre, Royal Prince Alfred Hospital, Camperdown, NSW 2050, Australia.
British Journal of Dermatology (Impact Factor: 4.1). 07/2008; 159(3):669-76. DOI: 10.1111/j.1365-2133.2008.08713.x
Source: PubMed

ABSTRACT Dermoscopy is a noninvasive technique that enables the clinician to perform direct microscopic examination of diagnostic features, not seen by the naked eye, in pigmented skin lesions. Diagnostic accuracy of dermoscopy has previously been assessed in meta-analyses including studies performed in experimental and clinical settings.
To assess the diagnostic accuracy of dermoscopy for the diagnosis of melanoma compared with naked eye examination by performing a meta-analysis exclusively on studies performed in a clinical setting.
We searched for publications from 1987 to January 2008 and found nine eligible studies. The selected studies compare diagnostic accuracy of dermoscopy with naked eye examination using a valid reference test on consecutive patients with a defined clinical presentation, performed in a clinical setting. Hierarchical summary receiver operator curve analysis was used to estimate the relative diagnostic accuracy for clinical examination with, and without, the use of dermoscopy.
We found the relative diagnostic odds ratio for melanoma, for dermoscopy compared with naked eye examination, to be 15.6 [95% confidence interval (CI) 2.9-83.7, P = 0.016]; removal of two outlier studies changed this to 9.0 (95% CI 1.5-54.6, P = 0.03).
Dermoscopy is more accurate than naked eye examination for the diagnosis of cutaneous melanoma in suspicious skin lesions when performed in the clinical setting.

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    • "Thus, they provide additional diagnostic criteria to the dermatolo- gist. Dermoscopy enables better diagnosis as compared to unaided eye [14] [15] [16] with an improvement in diagnostic sensitivity of 10–30% [17]. However, it has also been demonstrated that dermoscopy may actually lower the diagnostic accuracy in the hands of inexperienced dermatologists [10, 18–20], since this method requires great deal of experience to differentiate skin lesions [21]. "
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    ABSTRACT: Image-based computer aided diagnosis systems have significant potential for screening and early detection of malignant melanoma. We review the state of the art in these systems and examine current practices, problems, and prospects of image acquisition, pre-processing, segmentation, feature extraction and selection, and classification of dermoscopic images. This paper reports statistics and results from the most important implementations reported to date. We compared the performance of several classifiers specifically developed for skin lesion diagnosis and discussed the corresponding findings. Whenever available, indication of various conditions that affect the technique's performance is reported. We suggest a framework for comparative assessment of skin cancer diagnostic models and review the results based on these models. The deficiencies in some of the existing studies are highlighted and suggestions for future research are provided.
    International Journal of Biomedical Imaging 12/2013; 2013:323268. DOI:10.1155/2013/323268
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    • "Dermoscopy is a method that allows doctors to examine structures in the skin that are not visible to the naked eye. When practiced by experts, dermoscopy improves the diagnostic accuracy of pigmented skin lesions (PSL) [3] [4] [5]. "
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    ABSTRACT: Background It is often difficult to differentiate early melanomas from benign melanocytic nevi even by expert dermatologists, and the task is even more challenging for primary care physicians untrained in dermatology and dermoscopy. A computer system can provide an objective and quantitative evaluation of skin lesions, reducing subjectivity in the diagnosis. Objective Our objective is to make a low-cost computer aided diagnostic tool applicable in primary care based on a consumer grade camera with attached dermatoscope, and compare its performance to that of experienced dermatologists. Methods and Material We propose several new image-derived features computed from automatically segmented dermoscopic pictures. These are related to the asymmetry, color, border, geometry, and texture of skin lesions. The diagnostic accuracy of the system is compared with that of three dermatologists. Results With a data set of 206 skin lesions, 169 benign and 37 melanomas, the classifier was able to provide competitive sensitivity (86%) and specificity (52%) scores compared with the sensitivity (85%) and specificity (48%) of the most accurate dermatologist using only dermoscopic images. Conclusion We show that simple statistical classifiers can be trained to provide a recommendation on whether a pigmented skin lesion requires biopsy to exclude skin cancer with a performance that is comparable to and exceeds that of experienced dermatologists.
    Artificial Intelligence in Medicine 12/2013; DOI:10.1016/j.artmed.2013.11.006 · 1.36 Impact Factor
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    • "Dermatoscopy is a particularly helpful standard method of diagnosing the malignancy of skin lesions [1]. One of the major advantages of dermatoscopy is an increase in accuracy compared with naked-eye examination (up to 20% in the case of sensitivity and up to 10% in the case of specificity), thereby reducing the frequency of unnecessary surgical excisions of benign lesions [2] [3] [4]. "
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    ABSTRACT: This paper presents a novel approach to segmentation of dermoscopic images based on wavelet transform where the approximation coefficients have been shown to be efficient in segmentation. The three novel frameworks proposed in this paper, W-FCM, W-CPSFCM, and WK-Means, have been employed in segmentation using ROC curve analysis to demonstrate sufficiently good results. The novel W-CPSFCM algorithm permits the detection of a number of clusters in automatic mode without the intervention of a specialist.
    Computational and Mathematical Methods in Medicine 04/2012; 2012:578721. DOI:10.1155/2012/578721 · 1.02 Impact Factor
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