Stojkovic-Filipovic J, Tiodorovic D, Lallas A, Akay B N, Longo C, Rosendahl C, Dobrosavljevic D, Nazzaro G, Argenziano G, Zalaudek I, Tromme I, Tschandl P, Puig S, Lanssens S, Kittler H
Clinic of Dermatology and Venereology, Clinical Center of Serbia, Department of Dermatology, Faculty of Medicine, University of Belgrade, Belgrade, Serbia.
Clinic of Dermatovenereology, Clinical Center of Nis, Medical Faculty, University of Nis, Nis, Serbia.
J Eur Acad Dermatol Venereol. 2021 Apr;35(4):900-905. doi: 10.1111/jdv.17059. Epub 2021 Jan 5.
Combined blue nevi (CBN) may mimic melanoma and are relatively often biopsied for diagnostic reasons.
To better characterize CBN and to compare it with melanoma.
We collected clinical and dermatoscopic images of 111 histologically confirmed CBN and contrasted their dermatoscopic characteristics with 132 partly blue coloured melanomas. Furthermore, we compared the accuracy of human experts using pattern analysis with a computer algorithm based on deep learning.
Combined blue nevi are usually flat or slightly elevated and, in comparison with melanoma, more frequent on the head and neck. Dermatoscopically, they are typified by a blue structureless part in combination with either brown clods (n = 52, 46.8%), lines (n = 28, 25.2%) or skin-coloured or brown structureless areas (n = 31, 27.9%). In contrast with melanoma, the blue part of CBN is more often well defined (18.9% vs. 4.5%, P < 0.001) and more often located in the centre (22.5% vs. 5.3%, P < 0.001). Melanomas are more often chaotic (OR: 28.7, 95% CI: 14.8-55.7, P < 0.001), have at least one melanoma clue (OR: 10.8, 95% CI: 5.2-22.2 P < 0.001) in particular white lines (OR: 37.1, 95% CI: 13.4-102.9, P < 0.001). Using simplified pattern analysis (chaos and clues), two raters reached sensitivities of 93.9% (95% CI: 88.4-97.3%) and 92.4% (95% CI: 86.5-96.3%) at corresponding specificities of 59.5% (95% CI: 49.7-68.7%) and 65.8% (95% CI: 56.2-74.5%). The human accuracy with pattern analysis was on par with a state-of-the-art computer algorithm based on deep learning that achieved an area under the curve of (0.92, 95% CI: 0.87-0.96) and a specificity of 85.3% (95% CI: 76.5-91.7%) at a given sensitivity of 83.6% (95% CI: 72.5-91.5%).
CBN usually lack melanoma clues, in particular white lines. The accuracy of pattern analysis for combined nevi is acceptable, and histopathologic confirmation may not be necessary in exemplary cases.
联合蓝痣(CBN)可能类似黑色素瘤,并且由于诊断原因相对经常进行活检。
更好地描述联合蓝痣并将其与黑色素瘤进行比较。
我们收集了111例经组织学证实的联合蓝痣的临床和皮肤镜图像,并将其皮肤镜特征与132例部分呈蓝色的黑色素瘤进行对比。此外,我们将人类专家使用模式分析的准确性与基于深度学习的计算机算法进行了比较。
联合蓝痣通常扁平或略有隆起,与黑色素瘤相比,在头颈部更为常见。在皮肤镜下,它们的特征是蓝色无结构部分与棕色团块(n = 52,46.8%)、线条(n = 28,25.2%)或肤色或棕色无结构区域(n = 31,27.9%)相结合。与黑色素瘤相比,联合蓝痣的蓝色部分边界更清晰(18.9%对4.5%,P < 0.001),且更常位于中央(22.5%对5.3%,P < 0.001)。黑色素瘤更常表现为形态紊乱(比值比:28.7,95%置信区间:14.8 - 55.7,P < 0.001),至少有一个黑色素瘤特征(比值比:10.8,95%置信区间:5.2 - 22.2,P < 0.001),特别是白线(比值比:37.1,95%置信区间:13.4 - 102.9,P < 0.001)。使用简化模式分析(紊乱和特征),两名评估者在相应的特异性分别为59.5%(95%置信区间:49.7 - 68.7%)和65.8%(95%置信区间:56.2 - 74.5%)时,敏感性分别达到93.9%(95%置信区间:88.4 - 97.3%)和92.4%(95%置信区间:86.5 - 96.3%)。人类使用模式分析的准确性与基于深度学习的先进计算机算法相当,该算法在给定敏感性为83.6%(95%置信区间:72.5 - 91.5%)时,曲线下面积为(0.92,95%置信区间:0.87 - 0.96),特异性为85.3%(95%置信区间:76.5 - 91.7%)。
联合蓝痣通常缺乏黑色素瘤特征,特别是白线。联合痣的模式分析准确性是可接受的,在典型病例中可能无需组织病理学确诊。