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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.

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  • 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.

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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.

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科学领域:

  • 在瘤学瘤学.
  • 放射学 放射学是一门学科.
  • 机器学习 机器学习
  • 儿科神经瘤学 儿科神经瘤学

背景情况:

  • 儿科低度质瘤 (pLGGs) 是最常见的儿科脑瘤.
  • 包括KIAA1549-BRAF融合和BRAF V600E突变在内的BRAF变异在PLGG中很常见,并影响治疗决策.
  • 准确预测BRAF突变状态对于有效的pLGG管理至关重要.

研究的目的:

  • 开发和评估基于多类放射学的机器学习模型,用于预测儿科低度质瘤中BRAF突变状态 (融合,V600E,非BRAF).
  • 用单次和多次序MRI数据评估模型的性能.
  • 为了比较仅临床,仅放射学和组合模型的预测能力.

主要方法:

  • 对511名患有PLGG的儿科患者手术前MRI扫描的回顾性分析.
  • 使用PyRadiomics进行手动瘤细分和放射学特征提取.
  • 培训随机森林分类器用于三类BRAF状态预测.
  • 评估使用一个次序的交叉验证,并比较单次序与多次序方法.

主要成果:

  • 在单个序列中,FLAIR序列表现出最高的性能 (AUC 0.82),其次是T2WI (0.80),ADC (0.77) 和CE-T1WI (0.75).
  • 结合的临床放射学模型始终优于单源模型.
  • 在一组180名具有所有四个序列的患者中,多序列放射学 (特征连接和整体建模) 实现了0.79的宏AUC,优于单序列方法.
  • 来自FLAIR的特征占主导地位,但整合T2,ADC和CE-T1WI改善了分类平衡.

结论:

  • 机器学习模型利用多序MRI显示了对PLGG中BRAF突变状态的非侵入性预测的希望.
  • 虽然FLAIR是最好的单个序列,但整合多个序列可以提高预测性能和平衡.
  • 多序放射学为PLGG的精确治疗指导提供了有价值的工具,特别是当组织活检是不可行的时.