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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 Spit...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10579378/ https://www.ncbi.nlm.nih.gov/pubmed/37845235 http://dx.doi.org/10.1038/s41597-023-02585-2 |
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author | Mosquera-Zamudio, Andrés Launet, Laëtitia del Amor, Rocío Moscardó, Anaïs Colomer, Adrián Naranjo, Valery Monteagudo, Carlos |
author_facet | Mosquera-Zamudio, Andrés Launet, Laëtitia del Amor, Rocío Moscardó, Anaïs Colomer, Adrián Naranjo, Valery Monteagudo, Carlos |
author_sort | Mosquera-Zamudio, Andrés |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-10579378 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-105793782023-10-18 A Spitzoid Tumor dataset with clinical metadata and Whole Slide Images for Deep Learning models Mosquera-Zamudio, Andrés Launet, Laëtitia del Amor, Rocío Moscardó, Anaïs Colomer, Adrián Naranjo, Valery Monteagudo, Carlos Sci Data Data Descriptor 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. Nature Publishing Group UK 2023-10-16 /pmc/articles/PMC10579378/ /pubmed/37845235 http://dx.doi.org/10.1038/s41597-023-02585-2 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Data Descriptor Mosquera-Zamudio, Andrés Launet, Laëtitia del Amor, Rocío Moscardó, Anaïs Colomer, Adrián Naranjo, Valery Monteagudo, Carlos A Spitzoid Tumor dataset with clinical metadata and Whole Slide Images for Deep Learning models |
title | A Spitzoid Tumor dataset with clinical metadata and Whole Slide Images for Deep Learning models |
title_full | A Spitzoid Tumor dataset with clinical metadata and Whole Slide Images for Deep Learning models |
title_fullStr | A Spitzoid Tumor dataset with clinical metadata and Whole Slide Images for Deep Learning models |
title_full_unstemmed | A Spitzoid Tumor dataset with clinical metadata and Whole Slide Images for Deep Learning models |
title_short | A Spitzoid Tumor dataset with clinical metadata and Whole Slide Images for Deep Learning models |
title_sort | spitzoid tumor dataset with clinical metadata and whole slide images for deep learning models |
topic | Data Descriptor |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10579378/ https://www.ncbi.nlm.nih.gov/pubmed/37845235 http://dx.doi.org/10.1038/s41597-023-02585-2 |
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