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A comprehensive dataset of annotated brain metastasis MR images with clinical and radiomic data
Brain metastasis (BM) is one of the main complications of many cancers, and the most frequent malignancy of the central nervous system. Imaging studies of BMs are routinely used for diagnosis of disease, treatment planning and follow-up. Artificial Intelligence (AI) has great potential to provide au...
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/PMC10104872/ https://www.ncbi.nlm.nih.gov/pubmed/37059722 http://dx.doi.org/10.1038/s41597-023-02123-0 |
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author | Ocaña-Tienda, Beatriz Pérez-Beteta, Julián Villanueva-García, José D. Romero-Rosales, José A. Molina-García, David Suter, Yannick Asenjo, Beatriz Albillo, David Ortiz de Mendivil, Ana Pérez-Romasanta, Luis A. González-Del Portillo, Elisabet Llorente, Manuel Carballo, Natalia Nagib-Raya, Fátima Vidal-Denis, Maria Luque, Belén Reyes, Mauricio Arana, Estanislao Pérez-García, Víctor M. |
author_facet | Ocaña-Tienda, Beatriz Pérez-Beteta, Julián Villanueva-García, José D. Romero-Rosales, José A. Molina-García, David Suter, Yannick Asenjo, Beatriz Albillo, David Ortiz de Mendivil, Ana Pérez-Romasanta, Luis A. González-Del Portillo, Elisabet Llorente, Manuel Carballo, Natalia Nagib-Raya, Fátima Vidal-Denis, Maria Luque, Belén Reyes, Mauricio Arana, Estanislao Pérez-García, Víctor M. |
author_sort | Ocaña-Tienda, Beatriz |
collection | PubMed |
description | Brain metastasis (BM) is one of the main complications of many cancers, and the most frequent malignancy of the central nervous system. Imaging studies of BMs are routinely used for diagnosis of disease, treatment planning and follow-up. Artificial Intelligence (AI) has great potential to provide automated tools to assist in the management of disease. However, AI methods require large datasets for training and validation, and to date there have been just one publicly available imaging dataset of 156 BMs. This paper publishes 637 high-resolution imaging studies of 75 patients harboring 260 BM lesions, and their respective clinical data. It also includes semi-automatic segmentations of 593 BMs, including pre- and post-treatment T1-weighted cases, and a set of morphological and radiomic features for the cases segmented. This data-sharing initiative is expected to enable research into and performance evaluation of automatic BM detection, lesion segmentation, disease status evaluation and treatment planning methods for BMs, as well as the development and validation of predictive and prognostic tools with clinical applicability. |
format | Online Article Text |
id | pubmed-10104872 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-101048722023-04-16 A comprehensive dataset of annotated brain metastasis MR images with clinical and radiomic data Ocaña-Tienda, Beatriz Pérez-Beteta, Julián Villanueva-García, José D. Romero-Rosales, José A. Molina-García, David Suter, Yannick Asenjo, Beatriz Albillo, David Ortiz de Mendivil, Ana Pérez-Romasanta, Luis A. González-Del Portillo, Elisabet Llorente, Manuel Carballo, Natalia Nagib-Raya, Fátima Vidal-Denis, Maria Luque, Belén Reyes, Mauricio Arana, Estanislao Pérez-García, Víctor M. Sci Data Data Descriptor Brain metastasis (BM) is one of the main complications of many cancers, and the most frequent malignancy of the central nervous system. Imaging studies of BMs are routinely used for diagnosis of disease, treatment planning and follow-up. Artificial Intelligence (AI) has great potential to provide automated tools to assist in the management of disease. However, AI methods require large datasets for training and validation, and to date there have been just one publicly available imaging dataset of 156 BMs. This paper publishes 637 high-resolution imaging studies of 75 patients harboring 260 BM lesions, and their respective clinical data. It also includes semi-automatic segmentations of 593 BMs, including pre- and post-treatment T1-weighted cases, and a set of morphological and radiomic features for the cases segmented. This data-sharing initiative is expected to enable research into and performance evaluation of automatic BM detection, lesion segmentation, disease status evaluation and treatment planning methods for BMs, as well as the development and validation of predictive and prognostic tools with clinical applicability. Nature Publishing Group UK 2023-04-14 /pmc/articles/PMC10104872/ /pubmed/37059722 http://dx.doi.org/10.1038/s41597-023-02123-0 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Data Descriptor Ocaña-Tienda, Beatriz Pérez-Beteta, Julián Villanueva-García, José D. Romero-Rosales, José A. Molina-García, David Suter, Yannick Asenjo, Beatriz Albillo, David Ortiz de Mendivil, Ana Pérez-Romasanta, Luis A. González-Del Portillo, Elisabet Llorente, Manuel Carballo, Natalia Nagib-Raya, Fátima Vidal-Denis, Maria Luque, Belén Reyes, Mauricio Arana, Estanislao Pérez-García, Víctor M. A comprehensive dataset of annotated brain metastasis MR images with clinical and radiomic data |
title | A comprehensive dataset of annotated brain metastasis MR images with clinical and radiomic data |
title_full | A comprehensive dataset of annotated brain metastasis MR images with clinical and radiomic data |
title_fullStr | A comprehensive dataset of annotated brain metastasis MR images with clinical and radiomic data |
title_full_unstemmed | A comprehensive dataset of annotated brain metastasis MR images with clinical and radiomic data |
title_short | A comprehensive dataset of annotated brain metastasis MR images with clinical and radiomic data |
title_sort | comprehensive dataset of annotated brain metastasis mr images with clinical and radiomic data |
topic | Data Descriptor |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10104872/ https://www.ncbi.nlm.nih.gov/pubmed/37059722 http://dx.doi.org/10.1038/s41597-023-02123-0 |
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