Three-Point Checklist Dermoscopy for Melanoma Screening: Experience in Medical Students New to Dermatology

Abstract

Introduction: Melanoma early detection and optimal referral to the specialist, starts in primary care. The medical formation is usually deficient in training general physicians in early detection and risk management for most skin malignancies. A three-point dermoscopy checklist is used as a screening tool for differentiating malignant and benign pigmented lesions in non-expert clinicians using dermoscopy. Objectives: To evaluate the impact of brief medical training on the three-point dermoscopy algorithm in third-year medical students, new to dermatology, and determine the levels of sensitivity and specificity to differentiate malignant and benign pigmented lesions. Methods: Optional dermoscopic lecture for third-year medical students, new to dermatology, in the context of general medical semiology courses, with posterior case discussion and evaluation of 50 dermoscopy cases, 25 benign and 25 malignant. Students were asked to classify malignant versus benign pathology, based on the three-point dermoscopy algorithm discussed. Sensitivity, specificity, and predictive values were calculated according to the student’s responses. Results: 3250 responses from 65 students. Malignant pathology was misclassified as being in 154 responses, while benign pathology was misclassified as malignant in 668 responses. Sensitivity and specificity for differentiating malignant lesions were 89.70% and 61.99% respectively. Moderate interobserver agreement was found (Kappa Value 0.50 [CI 0.47-0.54]). Conclusion: When evaluating melanocytic lesions, the focus of the primary healthcare and general medical formation should emphasize the correct determination of malignant or benign pathology. Teaching the three-point dermoscopy rule to medical students, new to dermatology, yields satisfactory levels of sensitivity and specificity, comparable to general physicians.

Keywords

• Dermoscopy
• Medical Education
• Primary Care
• Skin Cancer
• Pattern Recognition