a Dermatology Clinic, Maggiore Hospital , University of Trieste , Trieste , Italy.
Expert Rev Anticancer Ther. 2018 Oct;18(10):1007-1012. doi: 10.1080/14737140.2018.1507822. Epub 2018 Aug 13.
Cutaneous melanoma is the sixth most common malignant cancer in the USA. Among different subtypes of melanoma, nodular melanoma (NM) accounts about 14% of all cases but is responsible for more than 40% of melanoma deaths. Early diagnosis is the best method to improve melanoma prognosis. Unfortunately, early diagnosis of NM is particularly challenging given that patients often lack identifiable risk factors such as many moles or freckles. Moreover, early NM may mimic a range of benign skin lesions that are not routinely excised or biopsied in every day practice. For this reason, specific clinical and skin imaging clues have been proposed to improve early detection of NM. Areas covered: The review discusses about the noninvasive tools to diagnose thin melanoma, particularly NM. Expert commentary: Currently, dermatologists present a wide opportunity of diagnostic tools. Current data suggest that the early diagnosis of NM is a major challenge as the majority of early NM are symmetric, roundish, and lack specific pattern. Another promising strategy is based on recent data suggesting that artificial intelligence based on deep convolutional neural networking is able to outperform average dermatologist. Further research is necessary to validate the performance of this method in the real world and in the clinical setting.
皮肤黑色素瘤是美国第六大常见的恶性肿瘤。在不同的黑色素瘤亚型中，结节性黑色素瘤（NM）约占所有病例的 14%，但却导致超过 40%的黑色素瘤死亡。早期诊断是改善黑色素瘤预后的最佳方法。不幸的是，NM 的早期诊断特别具有挑战性，因为患者通常缺乏可识别的风险因素，例如许多痣或雀斑。此外，早期 NM 可能类似于一系列良性皮肤病变，在日常实践中并非常规切除或活检。出于这个原因，已经提出了一些特定的临床和皮肤成像线索来提高 NM 的早期检测。
本综述讨论了用于诊断薄型黑色素瘤，特别是 NM 的非侵入性工具。
目前，皮肤科医生提供了广泛的诊断工具选择。目前的数据表明，NM 的早期诊断是一个主要挑战，因为大多数早期 NM 是对称的、圆形的，并且缺乏特定的模式。另一个有前途的策略是基于最近的数据，表明基于深度卷积神经网络的人工智能能够超过平均皮肤科医生的表现。需要进一步的研究来验证该方法在现实世界和临床环境中的性能。
