The Ronald O. Perelman Department of Dermatology, New York University School of Medicine, New York, New York.
The Ronald O. Perelman Department of Dermatology, New York University School of Medicine, New York, New York.
J Am Acad Dermatol. 2020 Oct;83(4):983-992. doi: 10.1016/j.jaad.2020.03.121. Epub 2020 Apr 26.
Managing the balance between accurately identifying early stage melanomas while avoiding obtaining biopsy specimens of benign lesions (ie, overbiopsy) is the major challenge of melanoma detection. Decision making can be especially difficult in patients with extensive atypical nevi. Recognizing that the primary screening modality for melanoma is subjective examination, studies have shown a tendency toward overbiopsy. Even low-risk routine surgical procedures are associated with morbidity, mounting health care costs, and patient anxiety. Recent advancements in noninvasive diagnostic modalities have helped improve diagnostic accuracy, especially when managing melanocytic lesions of uncertain diagnosis. Breakthroughs in artificial intelligence have also shown exciting potential in changing the landscape of melanoma detection. In the first article in this continuing medical education series, we review novel diagnostic technologies, such as automated 2- and 3-dimensional total body imaging with sequential digital dermoscopic imaging, reflectance confocal microscopy, and electrical impedance spectroscopy, and we explore the logistics and implications of potentially integrating artificial intelligence into existing melanoma management paradigms.
在准确识别早期黑色素瘤的同时避免对良性病变(即过度活检)获取活检标本是黑色素瘤检测的主要挑战。在有广泛不典型痣的患者中,决策可能特别困难。认识到黑色素瘤的主要筛查方式是主观检查,研究表明存在过度活检的趋势。即使是低风险的常规手术也与发病率、医疗成本增加和患者焦虑有关。非侵入性诊断方式的最新进展有助于提高诊断准确性,特别是在处理不确定诊断的黑素细胞病变时。人工智能的突破也显示出在改变黑色素瘤检测格局方面的令人兴奋的潜力。在这个继续教育系列的第一篇文章中,我们回顾了新型诊断技术,如二维和三维全自动全身成像与连续数字皮肤镜成像、反射共聚焦显微镜和电阻抗光谱术,并探讨了将人工智能潜在地整合到现有的黑色素瘤管理模式中的后勤和影响。