A Spitzoid Tumor dataset with clinical metadata and Whole Slide Images for Deep Learning models

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.

Automated summary

Spitzoid tumors are a group of melanocytic tumors with high diagnostic complexity. There has been uncertainty in their diagnosis since they were first described in 1948. Few studies have developed deep learning models using whole slide imaging for diagnosing these tumors, especially for the intermediate category known as Spitz tumor of unknown malignant potential. To address this gap, we introduce the first dataset with whole slide imaging for Spitzoid tumors, along with clinical information of each patient. Additionally, we provide two deep learning models implemented using this database as validation examples.