The CASH (color, architecture, symmetry, and homogeneity) algorithm for dermoscopy

doi:10.1016/j.jaad.2006.09.003

Journal of the American Academy of Dermatology

Volume 56, Issue 1, January 2007, Pages 45-52

https://doi.org/10.1016/j.jaad.2006.09.003 Get rights and content

Background

The color, architecture, symmetry, and homogeneity (CASH) algorithm for dermoscopy includes a feature not used in prior algorithms, namely, architecture. Architectural order/disorder is derived from current concepts regarding the biology of benign versus malignant melanocytic neoplasms.

Objective

We sought to evaluate the accuracy of the CASH algorithm.

Methods

A total CASH score (TCS) was calculated for dermoscopic images of 325 melanocytic neoplasms. Sensitivity, specificity, diagnostic accuracy, and receiver operating characteristic curve analyses were performed by comparing the TCS with the histopathologic diagnoses for all lesions.

Results

The mean TCS was 12.28 for melanoma, 7.62 for dysplastic nevi, and 5.24 for nondysplastic nevi. These differences were statistically significant (P < .001). A TCS of 8 or more yielded a sensitivity of 98% and specificity of 68% for the diagnosis of melanoma.

Limitations

This is a single-evaluator pilot study. Additional studies are needed to verify the CASH algorithm.

Conclusions

The CASH algorithm can distinguish melanoma from melanocytic nevi with sensitivity and specificity comparable with other algorithms. Further study is warranted to determine its intraobserver and interobserver correlations.

Section snippets

Methods

Most dermoscopic algorithms begin with a two-step procedure. In step one, the PLS are classified as melanocytic or nonmelanocytic. Accordingly, only melanocytic neoplasms were included in this study set.

For this study, the digital dermoscopic images of melanocytic neoplasms were selected from the following databases: (1) New York University Department of Dermatology Pigmented Lesion Clinic; (2) Consensus Net Meeting (with the permission of Drs Argenziano and Soyer); (3) Consortium of

Results

A total of 325 melanocytic neoplasms were studied. Included in the data set were 131 MMs, 70 DN, and 124 MN (Table I). The mean TCS for the 3 populations of melanocytic neoplasms were as follows: MM 12.28 (SD 2.47); DN 7.62 (SD 2.49); and MN 5.24 (SD 2.47). The mean TCS for MM was significantly greater than for DN (P < .001) and for MN (P < .0001) (Fig 3). Combining the TCS for DN and MN gave a mean TCS of 7.62 (SD 2.67), which was significantly different from the MM mean TCS of 12.28 (P <

Discussion

This cross-sectional pilot study demonstrates that the CASH algorithm can distinguish benign from malignant melanocytic neoplasms with a level of sensitivity, specificity, and diagnostic accuracy comparable with other verified dermoscopic algorithms (Table IV).

Pattern analysis, or modifications thereof, is probably the most widely used algorithm by dermoscopists.⁷ However, this method requires considerable dermoscopic experience. The CASH algorithm is a simplified version of pattern analysis,

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Citation Excerpt :
These systems simulate expert-designed diagnostic methods, to quantify the malignancy of the lesions based on the appearance of specific structures. Some of these diagnostic methods are pattern analysis Pehamberger, Steiner, and Wolff [3]; the ABCD rule Stolz [4]; the 7-point checklist Argenziano, Fabbrocini, Carli, De Giorgi, Sammarco, and Delfino [5]; the Menzies method Menzies, Ingvar, Crotty, and McCarthy [6]; the CASH method Henning, Dusza, Wang, Marghoob, Rabinovitz, Polsky, and Kopf [7] and the Chaos & Clues method Kittler [8]. In most of these protocols, the symmetry of lesions plays a key role, although not all of them have the same definition of symmetry.
Skin cancer has become a public health problem due to its increasing incidence. However, the malignancy risk of the lesions can be reduced if diagnosed at an early stage. To do so, it is essential to identify particular characteristics such as the symmetry of lesions. In this work, we present a novel approach for skin lesion symmetry classification of dermoscopic images based on deep learning techniques. We use a CNN model, which classifies the symmetry of a skin lesion as either “fully asymmetric”, “symmetric with respect to one axis”, or “symmetric with respect to two axes”. Moreover, we introduce a new dataset of labels for 615 skin lesions. During the experimentation framework, we also evaluate whether it is beneficial to rely on transfer learning from pre-trained CNNs or traditional learning-based methods. As a result, we present a new simple, robust and fast classification pipeline that outperforms methods based on traditional approaches or pre-trained networks, with a weighted-average F1-score of 64.5%.
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Supported by the Stavros S. Niarchos Foundation, The Rahr Family Foundation.

Conflicts of interest: None identified.

Presented at the First Congress of the International Dermoscopy Society, Naples, Italy, on April 25-29, 2006.

The opinions expressed here are the private views of the authors and do not represent the official position of the Department of the Army.

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ReportThe CASH (color, architecture, symmetry, and homogeneity) algorithm for dermoscopy

Background

Objective

Methods

Results

Limitations

Conclusions

Section snippets

Methods

Results

Discussion

J Am Acad Dermatol

J Am Acad Dermatol

Dermoscopy of pigmented skin lesions

J Am Acad Dermatol

Differentiation of atypical moles (dysplastic nevi) from early melanomas by dermoscopy

Dermatol Clin

Frequency and morphologic characteristics of invasive melanomas lacking specific surface microscopic features

Arch Dermatol

Epiluminescence microscopy for the diagnosis of doubtful melanocytic skin lesions: comparison of the ABCD rule of dermatoscopy and a new 7-point checklist based on pattern analysis

Arch Dermatol

Dermoscopy of pigmented skin lesions: results of a consensus meeting via the Internet

J Am Acad Dermatol

Report
The CASH (color, architecture, symmetry, and homogeneity) algorithm for dermoscopy