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JEADV:数字皮肤镜监测黑素细胞损害:两种结合静态和动态特征识别黑色素瘤的新方法

2022-03-12 医路坦克 MedSci原创

皮肤黑素瘤(CM)是一种恶性肿瘤,本研究的目的是确定与CM诊断相关的独立预测变量,并开发能够区分DDM期间良性和恶性黑素细胞病变的多变量皮肤镜预测模型,同时探索机器学习技术的潜在贡献。

     皮肤黑素瘤(CM)是一种恶性肿瘤,如果不及时诊断和治疗,可能会危及生命。因此,早期诊断是改善高危患者预后的最有效干预措施。

     在过去的几十年里,由于皮肤镜的引入,黑色素瘤的诊断准确性和敏感性有了显著的提高。然而,因为CM缺乏特定的恶性特征,即使用皮肤镜,也很难区分一些早期的CMS和良性的黑素细胞病变。

     然而,由于良性病变通常是稳定的,而黑色素瘤往往会以不可预测的方式随着时间的推移而改变形状、大小和颜色,因此数字皮肤镜监测(DDM)在评估病变的可疑变化方面的有效性已达成共识,这可以帮助早期黑色素瘤的诊断或避免不必要的良性病变切除。

     由于我们对与恶性肿瘤相关变化的有限了解,我们仍然不能准确地预测良性黑素细胞病变会随着时间的推移而改变到什么程度,特别是在年轻患者中。在这方面,Buhlet al发现Nevi和CMS在皮肤镜下的修改有一些差异,提出了一种能够区分DDM期间良恶性病变的诊断算法。特别是,在这些作者提出的7点检查表中包含关于动态变化的信息后,敏感性从47.5%提高到77.1%,而特异性几乎保持不变(98.1%对99.0%)。报道称,对于准确的CM诊断,非对称生长、结构紊乱的蓝灰色区域、蓝灰色不典型网络和外围条纹都显示出高的特异性,但敏感性不足。因此,到目前为止,仍然没有经过验证的模型来评估黑素细胞病变的动态变化及其在临床实践中的应用。

    本研究的目的是确定与CM诊断相关的独立预测变量,并开发能够区分DDM期间良性和恶性黑素细胞病变的多变量皮肤镜预测模型,同时探索机器学习技术的潜在贡献。模型结果汇总在两个可用作临床决策支持(CDS)工具的风险计算器中。

     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。建立了基于Logistic回归和随机森林的两个多变量预测模型。

     我们评估了173个病变(65个皮肤黑色素瘤和108个痣)。42例为原位黑色素瘤,侵袭性黑色素瘤的平均厚度为0.35 mm。黑色素瘤的中位随访时间为9.8个月,痣的中位随访时间为9.1个月。Logistic回归和随机森林模型的AUC值分别为0.87和0.89,显著高于静态评估模型(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,它们将建议的模型转换为用户友好的应用程序,以帮助临床医生进行决策。

    总之,本研究表明,整合黑素细胞病变的动态和静态评估可以显著提高CM检测的诊断准确性;但在高危患者的情况下,规范的诊断算法可能无法提供足够的准确性。因此,我们建议在DDM期间使用这两种诊断工具来更好地区分黑色素瘤和痣。

文献来源:Zenone M,  Zocchi L,  Moccia C, Digital dermoscopy monitoring of melanocytic lesions: Two novel calculators combining static and dynamic features to identify melanoma.J Eur Acad Dermatol Venereol 2022 Mar;36(3)

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    2022-08-13 sunylz
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