Rashid Sarem, Klebanov Nikolai, Lin William M, Tsao Hensin
Wellman Center for Photomedicine, Massachusetts General Hospital, Boston, Massachusetts, USA.
Department of Dermatology, Massachusetts General Hospital, Boston, Massachusetts, USA.
JID Innov. 2021 Aug 20;1(4):100047. doi: 10.1016/j.xjidi.2021.100047. eCollection 2021 Dec.
Pathogenic phenotypes in cutaneous melanoma have been vastly cataloged, although these classifications lack concordance and are confined to either morphological or molecular contexts. In this study, we perform unsupervised k-medoids clustering as a machine learning technique of 2,978 primary cutaneous melanomas at Mass General Brigham and apply this information to elucidate computer-defined subsets within the clinicopathologic domain. We identified five optimally separated clusters of melanoma that occupied two distinct clinicopathologic subspaces: a lower-grade partition associated with common or dysplastic nevi (i.e., nevus-associated melanomas) and a higher-grade partition lacking precursor lesions (i.e., de novo melanomas). Our model found de novo melanomas to be more mitogenic, more ulcerative, and thicker than nevus-associated melanomas, in addition to harboring previously unreported differences in radial and vertical growth phase status. The utilization of mixed clinicopathologic variables, reflective of actual clinical data contained in surgical pathology reports, has the potential to increase the biological relevance of existing melanoma classification schemes and facilitate the discovery of new genomic subtypes.
皮肤黑色素瘤的致病表型已被大量分类，尽管这些分类缺乏一致性，且局限于形态学或分子背景。在本研究中，我们将无监督k-中心点聚类作为一种机器学习技术，应用于麻省总医院布莱根分院的2978例原发性皮肤黑色素瘤，并运用这些信息来阐明临床病理领域中计算机定义的子集。我们识别出了五个最佳分离的黑色素瘤簇，它们占据了两个不同的临床病理子空间：一个与普通或发育异常痣相关的低级别分区（即痣相关黑色素瘤）和一个缺乏前驱病变的高级别分区（即新发黑色素瘤）。我们的模型发现，新发黑色素瘤比痣相关黑色素瘤更具促有丝分裂活性、更易溃疡且更厚，此外还存在先前未报道的在放射状和垂直生长期状态方面的差异。利用反映手术病理报告中实际临床数据的混合临床病理变量，有可能提高现有黑色素瘤分类方案的生物学相关性，并促进新基因组亚型的发现。
