# Dermoscopy of External Ear Melanocytic Lesions: Performance of Selected Dermoscopic Screening Algorithms and Proposal of a New Predictive Model for Malignancy (AuriCheck Dermoscopic Algorithm)

## TL;DR

This study introduces a new dermoscopic model for identifying melanoma in external ear lesions, showing high accuracy and describing new visual features.

## Contribution

Proposes a new predictive dermoscopic model and identifies previously unreported features for external ear melanocytic lesions.

## Key findings

- The proposed model achieved high sensitivity and specificity (around 90%) for external ear melanoma detection.
- New dermoscopic features 'red circles' and 'annular-globular pattern' were identified in malignant lesions.
- The model outperformed existing screening algorithms in balanced accuracy on independent and validation sets.

## Abstract

The data on dermoscopic presentation of external ear melanocytic lesions (EEMLs) are limited. We aimed to summarize the dermoscopic features of EEMLs, assess the performance of selected dermoscopic screening algorithms and propose a dermoscopic predictive model dedicated to this location. Previously undescribed dermoscopic structure (red circles) and pattern (annular-globular) were identified. The proposed predictive dermoscopic model has demonstrated both high sensitivity and specificity (hovering around 90%) for the assessment of EEMLs. This is the first large-scale study on the dermoscopic features of EEMLs. The proposed dermoscopic model was superior compared to known dermoscopic screening algorithms. The description of novel dermoscopic structure, pattern and the predictive model may aid early external ear melanoma diagnosis.

Background/Objectives: External ear melanocytic lesions (EEMLs) are frequently encountered in clinical practice. EEMLs belong to dermoscopic special site locations and their dermoscopic presentation is poorly characterized. The aims of the study were to determine the dermoscopic features and patterns of EEMLs, to evaluate the performance of selected dermoscopic screening algorithms for EEML and to propose a dedicated site-specific dermoscopic predictive model for the assessment of EEMLs. Methods: A retrospective study of EEMLs, which were either excised or had ≥2 years of stable clinical and digital dermoscopy follow-up, was performed. Each lesion was evaluated according to the predefined criteria and selected dermoscopic screening algorithms. Based on the diagnostic potential of each dermoscopic feature, the predictive model was developed and validated utilizing a test set derived from samples obtained from the HAM10000 database. Results: A total of 145 lesions from 136 patients (63 females; 46.3%) were analysed. The mean age of patients was 45.6 years. The most frequent dermoscopic findings of malignant lesions were irregular pigmentation (50/56; 89.3%) and asymmetry of the pattern (49/56; 87.5%), followed by asymmetry of colour (48/56; 85.7%), whereas the most common dermoscopic features of benign lesions were the presence of brown structureless areas (69/89; 77.5%), point and axial symmetry of pigmentation (63/89; 70.8%) and the presence of dots/globules (45/89; 50.6%). Among the selected dermoscopic screening algorithms, the 7-point checklist reached the highest sensitivity of 92.9%, whereas the CASH algorithm achieved the highest specificity of 93.3%. The predictive model reached a balanced accuracy of 88.3% on the independent test set and 89.5% on the validation set. Conclusions: This is the first large-scale study on the dermoscopic features of EEMLs. A predictive diagnostic model was proposed to aid external ear melanoma recognition in the clinical setting. Previously unreported dermoscopic feature (‘red circles’) and dermoscopic pattern (‘annular-globular pattern’) were described.