Dermatology Clinic, Department of Medical Sciences, University of Turin, Turin, Italy.
Cancer Epidemiology Unit, Department of Medical Sciences, University of Turin and CPO Piemonte, Turin, Italy.
J Eur Acad Dermatol Venereol. 2022 Mar;36(3):391-402. doi: 10.1111/jdv.17852. Epub 2021 Dec 17.
Early diagnosis is the most effective intervention to improve the prognosis of cutaneous melanoma. Even though the introduction of dermoscopy has improved the diagnostic accuracy, it can still be difficult to distinguish some melanomas from benign melanocytic lesions. Digital dermoscopy monitoring can identify dynamic changes of melanocytic lesions: To date, some algorithms were proposed, but a universally accepted one is still lacking.
To identify independent predictive variables associated with the diagnosis of cutaneous melanoma and develop a multivariable dermoscopic prediction model able to discriminate benign from malignant melanocytic lesions undergoing digital dermoscopy monitoring.
We collected dermoscopic images of melanocytic lesions excised after dermoscopy monitoring and carried out static and dynamic evaluations of dermoscopic features. We built two multivariable predictive models based on logistic regression and random forest.
We evaluated 173 lesions (65 cutaneous melanomas and 108 nevi). Forty-two melanomas were in situ, and the median thickness of invasive melanomas was 0.35 mm. The median follow-up time was 9.8 months for melanomas and 9.1 for nevi. The logistic regression and random forest models performed with AUC values of 0.87 and 0.89, respectively, were substantially higher than those of the static evaluation models (ABCD TDS score, 0.57; 7-point checklist, 0.59). Finally, we built two risk calculators, which translate the proposed models into user-friendly applications, to assist clinicians in the decision-making process.
The present study demonstrates that the integration of dynamic and static evaluations of melanocytic lesions is a safe approach that can significantly boost the diagnostic accuracy for cutaneous melanoma. We propose two diagnostic tools that significantly increase the accuracy in discriminating melanoma from nevi during digital dermoscopy monitoring.
早期诊断是改善皮肤黑素瘤预后的最有效干预措施。尽管皮肤镜的引入提高了诊断准确性，但仍难以将一些黑素瘤与良性黑素细胞病变区分开来。数字皮肤镜监测可以识别黑素细胞病变的动态变化：迄今为止，已经提出了一些算法，但仍然缺乏普遍接受的算法。
确定与皮肤黑素瘤诊断相关的独立预测变量，并开发一种能够区分数字皮肤镜监测下良性和恶性黑素细胞病变的多变量皮肤镜预测模型。
我们收集了皮肤镜监测后切除的黑素细胞病变的皮肤镜图像，并对皮肤镜特征进行了静态和动态评估。我们基于逻辑回归和随机森林建立了两个多变量预测模型。
我们评估了 173 个病变（65 个皮肤黑素瘤和 108 个痣）。42 个黑素瘤为原位，侵袭性黑素瘤的中位厚度为 0.35mm。黑素瘤的中位随访时间为 9.8 个月，痣为 9.1 个月。逻辑回归和随机森林模型的 AUC 值分别为 0.87 和 0.89，明显高于静态评估模型（ABCD TDS 评分，0.57；7 分检查表，0.59）。最后，我们构建了两个风险计算器，将提出的模型转化为用户友好的应用程序，以协助临床医生做出决策。
本研究表明，黑素细胞病变的动态和静态评估相结合是一种安全的方法，可以显著提高皮肤黑素瘤的诊断准确性。我们提出了两种诊断工具，在数字皮肤镜监测中显著提高了区分黑素瘤和痣的准确性。
