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    Index by author — September 01, 2020, 41 (9) | American Journal of Neuroradiology  
    
















    





















    
  
  




    
    

  

      


    

      

  
  
  

    

  

    

  

                
    

      

  

    

  
      
  
  

    


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Index by author
  	
      
September 01, 2020; Volume 41,Issue 9 

  
  
   

  
  



  


  
  



			

		

	
	
 	
	  
  

		
		

			

			  

  
      
  
  

    
  1. Valenca, M.M.
    1. 
  
  
      
      Head & Neck 
      
  
      
      Open Access
      
  
      Anosmia in COVID-19 Associated with Injury to the Olfactory Bulbs Evident on MRI  
    	
      M.F.V.V. Aragão, M.C. Leal, O.Q. Cartaxo Filho, T.M. Fonseca and M.M. Valença
      
  
    	
      American Journal of Neuroradiology September 2020, 41 (9) 1703-1706; DOI: https://doi.org/10.3174/ajnr.A6675 
      
  
  
  

      

  2. Vitanza, N.A.
    1. 
  
  
      
      EDITOR'S CHOICEPediatrics 
      
  
      
      
  
      Deep Learning for Pediatric Posterior Fossa Tumor Detection and Classification: A Multi-Institutional Study  
    	
      J.L. Quon, W. Bala, L.C. Chen, J. Wright, L.H. Kim, M. Han, K. Shpanskaya, E.H. Lee, E. Tong, M. Iv, J. Seekins, M.P. Lungren, K.R.M. Braun, T.Y. Poussaint, S. Laughlin, M.D. Taylor, R.M. Lober, H. Vogel, P.G. Fisher, G.A. Grant, V. Ramaswamy, N.A. Vitanza, C.Y. Ho, M.S.B. Edwards, S.H. Cheshier and K.W. Yeom
      
  
    	
      American Journal of Neuroradiology September 2020, 41 (9) 1718-1725; DOI: https://doi.org/10.3174/ajnr.A6704 
      
  
    	
      This 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, medulloblastoma, pilocytic astrocytoma, and ependymoma. There were 199 controls. Tumor histology served as ground truth except for diffuse midline glioma of the pons, which was primarily diagnosed by MR imaging. A modified ResNeXt-50-32x4d architecture served as the backbone for a multitask classifier model, using T2-weighted MRI as input to detect the presence of tumor and predict tumor class. Model tumor detection accuracy exceeded an AUC of 0.99 and was similar to that of 4 radiologists. Model tumor classification accuracy was 92% with an F1 score of 0.80. The model was most accurate at predicting diffuse midline glioma of the pons, followed by pilocytic astrocytoma and medulloblastoma. Ependymoma prediction was the least accurate.
      
  
  

      

  3. Vivas-buitrago, T.
    1. 
  
  
      
      Adult Brain 
      
  
      
      Open Access
      
  
      Neuroimaging Advances in Deep Brain Stimulation: Review of Indications, Anatomy, and Brain Connectomics  
    	
      E.H. Middlebrooks, R.A. Domingo, T. Vivas-Buitrago, L. Okromelidze, T. Tsuboi, J.K. Wong, R.S. Eisinger, L. Almeida, M.R. Burns, A. Horn, R.J. Uitti, R.E. Wharen, V.M. Holanda and S.S. Grewal
      
  
    	
      American Journal of Neuroradiology September 2020, 41 (9) 1558-1568; DOI: https://doi.org/10.3174/ajnr.A6693 
      
  
  
  

      

  4. Vogel, H.
    1. 
  
  
      
      EDITOR'S CHOICEPediatrics 
      
  
      
      
  
      Deep Learning for Pediatric Posterior Fossa Tumor Detection and Classification: A Multi-Institutional Study  
    	
      J.L. Quon, W. Bala, L.C. Chen, J. Wright, L.H. Kim, M. Han, K. Shpanskaya, E.H. Lee, E. Tong, M. Iv, J. Seekins, M.P. Lungren, K.R.M. Braun, T.Y. Poussaint, S. Laughlin, M.D. Taylor, R.M. Lober, H. Vogel, P.G. Fisher, G.A. Grant, V. Ramaswamy, N.A. Vitanza, C.Y. Ho, M.S.B. Edwards, S.H. Cheshier and K.W. Yeom
      
  
    	
      American Journal of Neuroradiology September 2020, 41 (9) 1718-1725; DOI: https://doi.org/10.3174/ajnr.A6704 
      
  
    	
      This 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, medulloblastoma, pilocytic astrocytoma, and ependymoma. There were 199 controls. Tumor histology served as ground truth except for diffuse midline glioma of the pons, which was primarily diagnosed by MR imaging. A modified ResNeXt-50-32x4d architecture served as the backbone for a multitask classifier model, using T2-weighted MRI as input to detect the presence of tumor and predict tumor class. Model tumor detection accuracy exceeded an AUC of 0.99 and was similar to that of 4 radiologists. Model tumor classification accuracy was 92% with an F1 score of 0.80. The model was most accurate at predicting diffuse midline glioma of the pons, followed by pilocytic astrocytoma and medulloblastoma. Ependymoma prediction was the least accurate.
      
  
  

      

  


  
  



  
      
  
  

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