response assessment
A multi-institutional pediatric dataset of clinical radiology MRIs by the Children's Brain Tumor Network
Familiar, Ariana M., Kazerooni, Anahita Fathi, Anderson, Hannah, Lubneuski, Aliaksandr, Viswanathan, Karthik, Breslow, Rocky, Khalili, Nastaran, Bagheri, Sina, Haldar, Debanjan, Kim, Meen Chul, Arif, Sherjeel, Madhogarhia, Rachel, Nguyen, Thinh Q., Frenkel, Elizabeth A., Helili, Zeinab, Harrison, Jessica, Farahani, Keyvan, Linguraru, Marius George, Bagci, Ulas, Velichko, Yury, Stevens, Jeffrey, Leary, Sarah, Lober, Robert M., Campion, Stephani, Smith, Amy A., Morinigo, Denise, Rood, Brian, Diamond, Kimberly, Pollack, Ian F., Williams, Melissa, Vossough, Arastoo, Ware, Jeffrey B., Mueller, Sabine, Storm, Phillip B., Heath, Allison P., Waanders, Angela J., Lilly, Jena V., Mason, Jennifer L., Resnick, Adam C., Nabavizadeh, Ali
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. This includes longitudinal MRIs across various cancer diagnoses, with associated patient-level clinical information, digital pathology slides, as well as tissue genotype and omics data. To facilitate downstream analysis, treatment-na\"ive images for 370 subjects were processed and released through the NCI Childhood Cancer Data Initiative via the Cancer Data Service. Through ongoing efforts to continuously build these imaging repositories, our aim is to accelerate discovery and translational AI models with real-world data, to ultimately empower precision medicine for children.
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"Just Accepted" papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. To investigate if a deep learning convolutional neural network (CNN) could enable low-dose 18F fluorodeoxyglucose (18F-FDG) PET/MRI imaging for correct treatment response assessment of pediatric patients and young adults with lymphoma. In this secondary analysis of prospectively collected data (NCT01542879), 20 patients (mean age, 16.4 6.4 years) with lymphoma underwent 18F-FDG PET/MRI scans between July 2015 and August 2019 at baseline and after induction chemotherapy.