The CASH (color, Architecture, Symmetry, and Homogeneity) Algorithm for Dermoscopy

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.