An 11,000-Study Open-Access Dataset of Longitudinal Magnetic Resonance Images of Brain Metastases
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| Autores principales: | , , , , , , , , , , , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866918060503334912 |
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| author | Chadha, Saahil Weiss, David Janas, Anastasia Ramakrishnan, Divya Hager, Thomas Osenberg, Klara Willms, Klara Zhu, Joshua Chiang, Veronica Bakas, Spyridon Maleki, Nazanin Sritharan, Durga V. Schoenherr, Sven Westerhoff, Malte Zawalich, Matthew Davis, Melissa Malhotra, Ajay Bousabarah, Khaled Deuschl, Cornelius Lin, MingDe Aneja, Sanjay Aboian, Mariam S. |
| author_facet | Chadha, Saahil Weiss, David Janas, Anastasia Ramakrishnan, Divya Hager, Thomas Osenberg, Klara Willms, Klara Zhu, Joshua Chiang, Veronica Bakas, Spyridon Maleki, Nazanin Sritharan, Durga V. Schoenherr, Sven Westerhoff, Malte Zawalich, Matthew Davis, Melissa Malhotra, Ajay Bousabarah, Khaled Deuschl, Cornelius Lin, MingDe Aneja, Sanjay Aboian, Mariam S. |
| contents | Brain metastases are a common complication of systemic cancer, affecting over 20% of patients with primary malignancies. Longitudinal magnetic resonance imaging (MRI) is essential for diagnosing patients, tracking disease progression, assessing therapeutic response, and guiding treatment selection. However, the manual review of longitudinal imaging is time-intensive, especially for patients with multifocal disease. Artificial intelligence (AI) offers opportunities to streamline image evaluation, but developing robust AI models requires comprehensive training data representative of real-world imaging studies. Thus, there is an urgent necessity for a large dataset with heterogeneity in imaging protocols and disease presentation. To address this, we present an open-access dataset of 11,884 longitudinal brain MRI studies from 1,430 patients with clinically confirmed brain metastases, paired with clinical and image metadata. The provided dataset will facilitate the development of AI models to assist in the long-term management of patients with brain metastasis. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_14021 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | An 11,000-Study Open-Access Dataset of Longitudinal Magnetic Resonance Images of Brain Metastases Chadha, Saahil Weiss, David Janas, Anastasia Ramakrishnan, Divya Hager, Thomas Osenberg, Klara Willms, Klara Zhu, Joshua Chiang, Veronica Bakas, Spyridon Maleki, Nazanin Sritharan, Durga V. Schoenherr, Sven Westerhoff, Malte Zawalich, Matthew Davis, Melissa Malhotra, Ajay Bousabarah, Khaled Deuschl, Cornelius Lin, MingDe Aneja, Sanjay Aboian, Mariam S. Quantitative Methods Brain metastases are a common complication of systemic cancer, affecting over 20% of patients with primary malignancies. Longitudinal magnetic resonance imaging (MRI) is essential for diagnosing patients, tracking disease progression, assessing therapeutic response, and guiding treatment selection. However, the manual review of longitudinal imaging is time-intensive, especially for patients with multifocal disease. Artificial intelligence (AI) offers opportunities to streamline image evaluation, but developing robust AI models requires comprehensive training data representative of real-world imaging studies. Thus, there is an urgent necessity for a large dataset with heterogeneity in imaging protocols and disease presentation. To address this, we present an open-access dataset of 11,884 longitudinal brain MRI studies from 1,430 patients with clinically confirmed brain metastases, paired with clinical and image metadata. The provided dataset will facilitate the development of AI models to assist in the long-term management of patients with brain metastasis. |
| title | An 11,000-Study Open-Access Dataset of Longitudinal Magnetic Resonance Images of Brain Metastases |
| topic | Quantitative Methods |
| url | https://arxiv.org/abs/2506.14021 |