Spitzoid tumors (ST) are a group of melanocytic tumors of high diagnostic complexity. Since 1948, when Sophie Spitz first described them, the diagnostic uncertainty remains until now, especially in the intermediate category known as Spitz tumor of unknown malignant potential (STUMP) or atypical Spitz tumor. Studies developing deep learning (DL) models to diagnose melanocytic tumors using whole slide imaging (WSI) are scarce, and few used ST for analysis, excluding STUMP. To address this gap, we introduce SOPHIE: the first ST dataset with WSIs, including labels as benign, malignant, and atypical tumors, along with the clinical information of each patient. Additionally, we explain two DL models implemented as validation examples using this database.

斯皮茨样肿瘤（ST）是一组诊断复杂性很高的黑素细胞肿瘤。自 1948 年 Sophie Spitz 首次描述以来，诊断的不确定性一直存在至今，特别是在被称为恶性潜能未知的 Spitz 肿瘤 (STUMP) 或非典型 Spitz 肿瘤的中间类别中。开发深度学习 (DL) 模型以使用全玻片成像 (WSI) 诊断黑素细胞肿瘤的研究很少，而且很少使用 ST 进行分析（STUMP 除外）。为了解决这一差距，我们引入了 SOPHIE：第一个带有 WSI 的 ST 数据集，包括良性、恶性和非典型肿瘤的标签，以及每位患者的临床信息。此外，我们还解释了使用该数据库作为验证示例实现的两个深度学习模型。