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    Index by author — July 01, 2020, 41 (7) | American Journal of Neuroradiology  
    
















    





















    
  
  




    
    

  

      


    

      

  
  
  

    

  

    

  

                
    

      

  

    

  
      
  
  

    


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Index by author
  	
      
July 01, 2020; Volume 41,Issue 7 

  
  
   

  
  



  


  
  



			

		

	
	
 	
	  
  

		
		

			

			  

  
      
  
  

    
  1. Horowitz, P.
    1. 
  
  
      
      Head & Neck 
      
  
      
      
  
      Squamous Cell Carcinoma Arising from Sinonasal Inverted Papilloma  
    	
      D.T. Ginat, A. Trzcinska and P. Horowitz
      
  
    	
      American Journal of Neuroradiology July 2020, 41 (7) 1156-1159; DOI: https://doi.org/10.3174/ajnr.A6583 
      
  
  
  

      

  2. Horvath, A.
    1. 
  
  
      
      Adult Brain 
      
  
      
      Open Access
      
  
      Distinguishing Extravascular from Intravascular Ferumoxytol Pools within the Brain: Proof of Concept in Patients with Treated Glioblastoma  
    	
      R.F. Barajas, D. Schwartz, H.L. McConnell, C.N. Kersch, X. Li, B.E. Hamilton, J. Starkey, D.R. Pettersson, J.P. Nickerson, J.M. Pollock, R.F. Fu, A. Horvath, L. Szidonya, C.G. Varallyay, J.J. Jaboin, A.M. Raslan, A. Dogan, J.S. Cetas, J. Ciporen, S.J. Han, P. Ambady, L.L. Muldoon, R. Woltjer, W.D. Rooney and E.A. Neuwelt
      
  
    	
      American Journal of Neuroradiology July 2020, 41 (7) 1193-1200; DOI: https://doi.org/10.3174/ajnr.A6600 
      
  
  
  

      

  3. Houdart, E.
    1. 
  
  
      
      LETTER 
      
  
      
      
  
      Meta-Analysis as a Symptom: The Example of Flow Diverters  
    	
      E. Houdart
      
  
    	
      American Journal of Neuroradiology July 2020, 41 (7) E51; DOI: https://doi.org/10.3174/ajnr.A6594 
      
  
  
  

      

  4. Hu, C.
    1. 
  
  
      
      EDITOR'S CHOICEPediatrics 
      
  
      
      Open Access
      
  
      Automatic Machine Learning to Differentiate Pediatric Posterior Fossa Tumors on Routine MR Imaging  
    	
      H. Zhou, R. Hu, O. Tang, C. Hu, L. Tang, K. Chang, Q. Shen, J. Wu, B. Zou, B. Xiao, J. Boxerman, W. Chen, R.Y. Huang, L. Yang, H.X. Bai and C. Zhu
      
  
    	
      American Journal of Neuroradiology July 2020, 41 (7) 1279-1285; DOI: https://doi.org/10.3174/ajnr.A6621 
      
  
    	
      This retrospective study included preoperative MR imaging of 288 pediatric patients with pediatric posterior fossa tumors, including medulloblastoma (n=111), ependymoma (n=70), and pilocytic astrocytoma (n=107). Radiomics features were extracted from T2-weighted images, contrast-enhanced T1-weighted images, and ADC maps. Models generated by standard manual optimization by a machine learning expert were compared with automatic machine learning via the Tree-Based Pipeline Optimization Tool for performance evaluation. The authors conclude that automatic machine learning based on routine MR imaging classified pediatric posterior fossa tumors with high accuracy compared with manual expert pipeline optimization and qualitative expert MR imaging review.
      
  
  

      

  5. Hu, R.
    1. 
  
  
      
      EDITOR'S CHOICEPediatrics 
      
  
      
      Open Access
      
  
      Automatic Machine Learning to Differentiate Pediatric Posterior Fossa Tumors on Routine MR Imaging  
    	
      H. Zhou, R. Hu, O. Tang, C. Hu, L. Tang, K. Chang, Q. Shen, J. Wu, B. Zou, B. Xiao, J. Boxerman, W. Chen, R.Y. Huang, L. Yang, H.X. Bai and C. Zhu
      
  
    	
      American Journal of Neuroradiology July 2020, 41 (7) 1279-1285; DOI: https://doi.org/10.3174/ajnr.A6621 
      
  
    	
      This retrospective study included preoperative MR imaging of 288 pediatric patients with pediatric posterior fossa tumors, including medulloblastoma (n=111), ependymoma (n=70), and pilocytic astrocytoma (n=107). Radiomics features were extracted from T2-weighted images, contrast-enhanced T1-weighted images, and ADC maps. Models generated by standard manual optimization by a machine learning expert were compared with automatic machine learning via the Tree-Based Pipeline Optimization Tool for performance evaluation. The authors conclude that automatic machine learning based on routine MR imaging classified pediatric posterior fossa tumors with high accuracy compared with manual expert pipeline optimization and qualitative expert MR imaging review.
      
  
  

      

  6. Huang, R.Y.
    1. 
  
  
      
      EDITOR'S CHOICEPediatrics 
      
  
      
      Open Access
      
  
      Automatic Machine Learning to Differentiate Pediatric Posterior Fossa Tumors on Routine MR Imaging  
    	
      H. Zhou, R. Hu, O. Tang, C. Hu, L. Tang, K. Chang, Q. Shen, J. Wu, B. Zou, B. Xiao, J. Boxerman, W. Chen, R.Y. Huang, L. Yang, H.X. Bai and C. Zhu
      
  
    	
      American Journal of Neuroradiology July 2020, 41 (7) 1279-1285; DOI: https://doi.org/10.3174/ajnr.A6621 
      
  
    	
      This retrospective study included preoperative MR imaging of 288 pediatric patients with pediatric posterior fossa tumors, including medulloblastoma (n=111), ependymoma (n=70), and pilocytic astrocytoma (n=107). Radiomics features were extracted from T2-weighted images, contrast-enhanced T1-weighted images, and ADC maps. Models generated by standard manual optimization by a machine learning expert were compared with automatic machine learning via the Tree-Based Pipeline Optimization Tool for performance evaluation. The authors conclude that automatic machine learning based on routine MR imaging classified pediatric posterior fossa tumors with high accuracy compared with manual expert pipeline optimization and qualitative expert MR imaging review.
      
  
  

      

  7. Huang, Z.
    1. 
  
  
      
      Interventional 
      
  
      
      Open Access
      
  
      A Hemodynamic Mechanism Correlating with the Initiation of MCA Bifurcation Aneurysms  
    	
      Z. Huang, M. Zeng, W.G. Tao, F.Y. Zeng, C.Q. Chen, L.B. Zhang and F.H. Chen
      
  
    	
      American Journal of Neuroradiology July 2020, 41 (7) 1217-1224; DOI: https://doi.org/10.3174/ajnr.A6615 
      
  
  
  

      

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