在儿科低级结质瘤中基于多类放射学预测BRAF突变状态,使用多序MRI

Anat Yahav Dovrat1, Khashayar Namdar2, Matthias W Wagner2

  • 1From the Department of Diagnostic & Interventional Imaging (A.Y.D., K.N., M.W.W., M.S., M.N., M.D.S., F.K., B.B.E.-W.), Division of Neurosurgery (P.D.), Department of Neurooncology (U.T.), Paediatric Laboratory Medicine (C.H.), Division of Pathology, The Hospital for Sick Children, University of Toronto, Canada; Neurosciences & Mental Health Research Program (K.N., M.W.W., F.K., B.B.E.-W.), SickKids Research Institute, Toronto, ON, Canada; Department of Medical Imaging (A.Y.D., M.W.W., F. K., B.B.E.-W.), Institute of Medical Science (K.N., F.K.), Computer Science (F.K.), Mechanical and Industrial Engineering (F.K.), University of Toronto, Toronto, ON, Canada; Department of Diagnostic and Interventional Neuroradiology (M.W.W.), University Hospital Augsburg, Germany; Department of Radiology (K.W.Y), Phoenix Children's Hospital, AZ, USA; Department of Neurosurgery, Stanford School of Medicine, CA, USA and Vector Institute (K.N., F.K.), Toronto, ON, Canada. anat.yahav.dovrat@gmail.com.

概括

使用多序MRI的机器学习模型可以预测儿科低度质瘤 (pLGG) 中的BRAF突变状态. 整合多个MRI序列可以提高预测的准确性,为指导PLGG治疗提供一种非侵入性工具.

相关概念视频