The loss of primary cilia on melanocytes is a useful biomarker for the distinction of melanoma from conventional melanocytic nevi. It is unknown whether ciliation status is beneficial for diagnosing spitzoid tumors—a subclass of melanomas that present inherently ambiguous histology and are challenging to classify. We evaluated the Ciliation Index (CI) in 68 cases of spitzoid tumors ranging from Spitz nevi and atypical Spitz tumors to spitzoid melanoma. We found a significant decrease in CI within the spitzoid melanoma group when compared with either the Spitz nevi or atypical Spitz tumors groups. In addition, we used a machine-learning–based algorithm to determine the value of CI when considered in combination with other histopathologic and molecular features commonly used for diagnosis. We found that a low CI was consistently ranked as a top predictive feature in the diagnosis of malignancy. Predictive models trained on only the top four predictive features (CI, asymmetry, hyperchromatism, and cytologic atypia) outperformed standard histologic assessment in an independent validation cohort of 56 additional cases. The results provide an alternative approach to evaluate diagnostically challenging melanocytic lesions, and further support the use of CI as an ancillary diagnostic test.

黑色素细胞上原发性纤毛的丢失是区分黑色素瘤与常规黑素细胞痣的有用生物标记。尚不清楚纤支状态是否对诊断类扁平状肿瘤（一种黑色素瘤的子类）有益，该类亚基瘤具有固有的组织学特征，难以分类。我们评估了68例从Spitz痣和非典型Spitz肿瘤到Spitzoid黑色素瘤的Spitzoid肿瘤的纤毛指数（CI）。我们发现与Spitz nevi或非典型Spitz肿瘤组相比，Spitzoid黑色素瘤组的CI显着降低。此外，当与其他通常用于诊断的组织病理学和分子特征结合考虑时，我们使用了基于机器学习的算法来确定CI的值。我们发现，低CI一直被认为是恶性肿瘤诊断中的顶级预测特征。在另外56个病例的独立验证队列中，仅在前四个预测特征（CI，不对称性，增色性和细胞学非典型性）上训练的预测模型优于标准组织学评估。结果为评估具有挑战性的黑素细胞病变提供了另一种方法，并进一步支持将CI用作辅助诊断测试。