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. 2012 Jul 31;2(3):203a08.
doi: 10.5826/dpc.0203a08. Print 2012 Jul.

Accuracy of the first step of the dermatoscopic 2-step algorithm for pigmented skin lesions

Philipp Tschandl  1 , Cliff Rosendahl, Harald Kittler
Affiliations

Accuracy of the first step of the dermatoscopic 2-step algorithm for pigmented skin lesions

Philipp Tschandl et al. Dermatol Pract Concept. .

Abstract

Objectives: To evaluate the frequency of misclassifications of equivocal pigmented lesions according to the first step of the dermatoscopic 2-step algorithm.

Patients and methods: 707 consecutive cases from 553 patients of central Europe and Australia were included in the study. Dermatoscopic images were evaluated in a blinded fashion for the presence of features described in the 2-step algorithm to determine their melanocytic or non-melanocytic origin. Mucosal, genital and non-pigmented lesions were excluded.

Results: The sensitivity of the first step was 97.1% for patients from Australia and 96.8% for patients from central Europe. The specificity was 33.6% for Australian patients and 67.9% for European patients. The most common reasons for misclassification were the presence of a pigmented network in a non-melanocytic lesion (n=68, 25.2%) and the absence of dermatoscopic features of melanocytic and non-melanocytic lesions in 69 (25.6%) non-melanocytic lesions.

Conclusion: The first step of the dermatoscopic 2-step algorithm, if applied consistently, has high sensitivity but low specificity. Many non-melanocytic lesions, especially solar lentigines and seborrheic keratoses, are wrongly classified as melanocytic. The worse performance of the first step algorithm in Australian patients is probably due to a higher rate of solar lentigines in patients with severely sun-damaged skin.

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Figures

Figure 1
Figure 1
Distribution of lesions regarding body site and geographic origin. Given frequencies are calculated within each geographic group. [Copyright: ©2012 Tschandl et al.]
Figure 2
Figure 2
Pigmented network leading to a wrong classification. (A) Seborrheic keratosis. (B) Solar lentigo. (C) Seborrheic keratosis. (D) Solar lentigo. [Copyright: ©2012 Tschandl et al.]
Figure 2
Figure 2
Pigmented network leading to a wrong classification. (A) Seborrheic keratosis. (B) Solar lentigo. (C) Seborrheic keratosis. (D) Solar lentigo. [Copyright: ©2012 Tschandl et al.]
Figure 2
Figure 2
Pigmented network leading to a wrong classification. (A) Seborrheic keratosis. (B) Solar lentigo. (C) Seborrheic keratosis. (D) Solar lentigo. [Copyright: ©2012 Tschandl et al.]
Figure 2
Figure 2
Pigmented network leading to a wrong classification. (A) Seborrheic keratosis. (B) Solar lentigo. (C) Seborrheic keratosis. (D) Solar lentigo. [Copyright: ©2012 Tschandl et al.]
Figure 3
Figure 3
An absence of any specific melanocytic or non-melanocytic (“melanocytic by default”) features, leading to a wrong classification. (A & B) Pigmented Bowen’s disease. (C & D) Seborrheic keratosis. [Copyright: ©2012 Tschandl et al.]
Figure 3
Figure 3
An absence of any specific melanocytic or non-melanocytic (“melanocytic by default”) features, leading to a wrong classification. (A & B) Pigmented Bowen’s disease. (C & D) Seborrheic keratosis. [Copyright: ©2012 Tschandl et al.]
Figure 3
Figure 3
An absence of any specific melanocytic or non-melanocytic (“melanocytic by default”) features, leading to a wrong classification. (A & B) Pigmented Bowen’s disease. (C & D) Seborrheic keratosis. [Copyright: ©2012 Tschandl et al.]
Figure 3
Figure 3
An absence of any specific melanocytic or non-melanocytic (“melanocytic by default”) features, leading to a wrong classification. (A & B) Pigmented Bowen’s disease. (C & D) Seborrheic keratosis. [Copyright: ©2012 Tschandl et al.]
Figure 4
Figure 4
Difference between 2-step algorithm (A) versus “chaos and clues” algorithm (B). [Copyright: ©2012 Tschandl et al.]
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