A Spitzoid Tumor dataset with clinical metadata and Whole Slide Images for Deep Learning models
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 atyp...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-10-01
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| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-023-02585-2 |
| _version_ | 1797578286536065024 |
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| author | Andrés Mosquera-Zamudio Laëtitia Launet Rocío del Amor Anaïs Moscardó Adrián Colomer Valery Naranjo Carlos Monteagudo |
| author_facet | Andrés Mosquera-Zamudio Laëtitia Launet Rocío del Amor Anaïs Moscardó Adrián Colomer Valery Naranjo Carlos Monteagudo |
| author_sort | Andrés Mosquera-Zamudio |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T22:19:47Z |
| format | Article |
| id | doaj.art-e0f3faeba6fa482da828995bf272eadb |
| institution | Directory Open Access Journal |
| issn | 2052-4463 |
| language | English |
| last_indexed | 2024-03-10T22:19:47Z |
| publishDate | 2023-10-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Data |
| spelling | doaj.art-e0f3faeba6fa482da828995bf272eadb2023-11-19T12:20:12ZengNature PortfolioScientific Data2052-44632023-10-011011710.1038/s41597-023-02585-2A Spitzoid Tumor dataset with clinical metadata and Whole Slide Images for Deep Learning modelsAndrés Mosquera-Zamudio0Laëtitia Launet1Rocío del Amor2Anaïs Moscardó3Adrián Colomer4Valery Naranjo5Carlos Monteagudo6Pathology Department Hospital Clínico Universitario de Valencia, Universidad de ValenciaInstituto Universitario de Investigación en Tecnología Centrada en el Ser Humano, HUMAN-tech Universitat Politècnica de ValènciaInstituto Universitario de Investigación en Tecnología Centrada en el Ser Humano, HUMAN-tech Universitat Politècnica de ValènciaPathology Department Hospital Clínico Universitario de Valencia, Universidad de ValenciaInstituto Universitario de Investigación en Tecnología Centrada en el Ser Humano, HUMAN-tech Universitat Politècnica de ValènciaInstituto Universitario de Investigación en Tecnología Centrada en el Ser Humano, HUMAN-tech Universitat Politècnica de ValènciaPathology Department Hospital Clínico Universitario de Valencia, Universidad de ValenciaAbstract 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.https://doi.org/10.1038/s41597-023-02585-2 |
| spellingShingle | Andrés Mosquera-Zamudio Laëtitia Launet Rocío del Amor Anaïs Moscardó Adrián Colomer Valery Naranjo Carlos Monteagudo A Spitzoid Tumor dataset with clinical metadata and Whole Slide Images for Deep Learning models Scientific Data |
| 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 |
| url | https://doi.org/10.1038/s41597-023-02585-2 |
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