Mosquera-Zamudio A, Launet L, Del Amor R, Moscardó A, Colomer A, Naranjo V, Monteagudo C. A Spitzoid Tumor dataset with clinical metadata and Whole Slide Images for Deep Learning models.
Sci Data 2023;
10:704. [PMID:
37845235 PMCID:
PMC10579378 DOI:
10.1038/s41597-023-02585-2]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Accepted: 09/22/2023] [Indexed: 10/18/2023] Open
Abstract
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.
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