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Abstract #0403

Developing an Open Access Brain Metastasis Database: Yale Brain Metastasis Database

Divya Ramakrishnan1, Leon Jekel2, Matthew Sala1, Manpreet Kaur3, Anastasia Janas1, Gabriel Cassinelli Petersen4, Khaled Bousabarah5, MingDe Lin6, Sara Merkaj7, Marc von Reppert8, and Mariam Aboian1
1Department of Radiology, Yale School of Medicine, New Haven, CT, United States, 2University of Essen, Essen, Germany, 3Ludwig Maximilians Universität (LMU), Munich, Germany, 4University of Goettingen, Goettingen, Germany, 5Visage Imaging, Dusseldorf, Germany, 6Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, United States, 7University of Ulm, Ulm, Germany, 8University of Leipzig, Leipzig, Germany

Synopsis

Keywords: Tumors, Machine Learning/Artificial Intelligence, Brain MetastasisWhile there are many machine learning (ML) algorithms for brain metastasis (BM) detection and segmentation, very few have been validated on external datasets. There is a critical need for open access BM datasets for development and validation of more robust algorithms. Here, we present the Yale Brain Metastasis database of 290 patients with annotated segmentations of BM on T1 post-gadolinium and associated survival information. A subset of 228 patients have FLAIR segmentations, clinical features, and qualitative imaging features. Open access of this database will greatly aid in the development and validation of new AI algorithms for BM detection and segmentation.

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