Ariana M Familiar, Anahita Fathi Kazerooni, Hannah Anderson, Aliaksandr Lubneuski, Karthik Viswanathan, Rocky Breslow, Nastaran Khalili, Sina Bagheri, Debanjan Haldar, Meen Chul Kim, Sherjeel Arif, Rachel Madhogarhia, Thinh Q Nguyen, Elizabeth A Frenkel, Zeinab Helili, Jessica Harrison, Keyvan Farahani, Marius George Linguraru, Ulas Bagci, Yury Velichko, Jeffrey Stevens, Sarah Leary, Robert M Lober, Stephani Campion, Amy A Smith, Denise Morinigo, Brian Rood, Kimberly Diamond, Ian F Pollack, Melissa Williams, Arastoo Vossough, Jeffrey B Ware, Sabine Mueller, Phillip B Storm, Allison P Heath, Angela J Waanders, Jena V Lilly, Jennifer L Mason, Adam C Resnick, Ali Nabavizadeh
Pediatric brain and spinal cancers remain the leading cause of cancer-related death in children. Advancements in clinical decision-support in pediatric neuro-oncology utilizing the wealth of radiology imaging data collected through standard care, however, has significantly lagged other domains. Such data is ripe for use with predictive analytics such as artificial intelligence (AI) methods, which require large datasets. To address this unmet need, we provide a multi-institutional, large-scale pediatric dataset of 23,101 multi-parametric MRI exams acquired through routine care for 1,526 brain tumor patients, as part of the Children's Brain Tumor Network...
October 2, 2023: ArXiv