Abstract #4511

The Yale Glioma Dataset: Developing An Open Access, Annotated MRI Database

Matthew L. Sala¹, Jan Lost², Niklas Tillmanns², Sara Merkaj³, Marc von Reppert⁴, Divya Ramakrishnan¹, Khaled Bousabarah⁵, Anita Huttner⁶, Sanjay Aneja⁷, Arman Avesta⁷, Antonio Omuro⁸, and Mariam Aboian¹

¹Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, United States, ²University of Düsseldorf, Düsseldorf, Germany, ³University of Ulm, Ulm, Germany, ⁴Leipzig University, Leipzig, Germany, ⁵Visage Imaging, Düsseldorf, Germany, ⁶Pathology, Yale School of Medicine, New Haven, CT, United States, ⁷Therapeutic Radiology, Yale School of Medicine, New Haven, CT, United States, ⁸Neurology, Yale School of Medicine, New Haven, CT, United States

Synopsis

Keywords: Tumors, Machine Learning/Artificial Intelligence, GliomaRecent development of Machine Learning (ML) tools for analysis of CNS tumors demonstrates great potential benefit to research and clinical practice but has been hindered by a lack of external validation. There is a critical need for open access to large individual hospital-based datasets with expert annotations. Here, we present the Yale Glioma Dataset, a database of 1,033 patients featuring annotated segmentations on FLAIR and T1 post-gadolinium, tumor grading and classification, and further clinical information. Open access of this database will support the development and validation of new AI algorithms for glioma detection and segmentation.

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