Deep Learning for Pediatric Posterior Fossa Tumor Detection and Classification: A Multi-Institutional Study.
Publication/Presentation Date
9-1-2020
Abstract
BACKGROUND AND PURPOSE: Posterior fossa tumors are the most common pediatric brain tumors. MR imaging is key to tumor detection, diagnosis, and therapy guidance. We sought to develop an MR imaging-based deep learning model for posterior fossa tumor detection and tumor pathology classification.
MATERIALS AND METHODS: The study cohort comprised 617 children (median age, 92 months; 56% males) from 5 pediatric institutions with posterior fossa tumors: diffuse midline glioma of the pons (
RESULTS: Model tumor detection accuracy exceeded an AUROC of 0.99 and was similar to that of 4 radiologists. Model tumor classification accuracy was 92% with an F
CONCLUSIONS: We present a multi-institutional deep learning model for pediatric posterior fossa tumor detection and classification with the potential to augment and improve the accuracy of radiologic diagnosis.
Volume
41
Issue
9
First Page
1718
Last Page
1725
ISSN
1936-959X
Published In/Presented At
Quon, J. L., Bala, W., Chen, L. C., Wright, J., Kim, L. H., Han, M., Shpanskaya, K., Lee, E. H., Tong, E., Iv, M., Seekins, J., Lungren, M. P., Braun, K. R. M., Poussaint, T. Y., Laughlin, S., Taylor, M. D., Lober, R. M., Vogel, H., Fisher, P. G., Grant, G. A., … Yeom, K. W. (2020). Deep Learning for Pediatric Posterior Fossa Tumor Detection and Classification: A Multi-Institutional Study. AJNR. American journal of neuroradiology, 41(9), 1718–1725. https://doi.org/10.3174/ajnr.A6704
Disciplines
Medicine and Health Sciences | Pediatrics
PubMedID
32816765
Department(s)
Department of Pediatrics
Document Type
Article