1naresh
Array
(
    [urn:ac.highwire.org:guest:identity] => Array
        (
            [runtime-id] => urn:ac.highwire.org:guest:identity
            [type] => guest
            [service-id] => ajnr-ac.highwire.org
            [access-type] => Controlled
            [privilege] => Array
                (
                    [urn:ac.highwire.org:guest:privilege] => Array
                        (
                            [runtime-id] => urn:ac.highwire.org:guest:privilege
                            [type] => privilege-set
                            [privilege-set] => GUEST
                        )

                )

            [credentials] => Array
                (
                    [method] => guest
                )

        )

)
1naresh
Array
(
    [urn:ac.highwire.org:guest:identity] => Array
        (
            [runtime-id] => urn:ac.highwire.org:guest:identity
            [type] => guest
            [service-id] => ajnr-ac.highwire.org
            [access-type] => Controlled
            [privilege] => Array
                (
                    [urn:ac.highwire.org:guest:privilege] => Array
                        (
                            [runtime-id] => urn:ac.highwire.org:guest:privilege
                            [type] => privilege-set
                            [privilege-set] => GUEST
                        )

                )

            [credentials] => Array
                (
                    [method] => guest
                )

        )

)
1naresh
Array
(
    [urn:ac.highwire.org:guest:identity] => Array
        (
            [runtime-id] => urn:ac.highwire.org:guest:identity
            [type] => guest
            [service-id] => ajnr-ac.highwire.org
            [access-type] => Controlled
            [privilege] => Array
                (
                    [urn:ac.highwire.org:guest:privilege] => Array
                        (
                            [runtime-id] => urn:ac.highwire.org:guest:privilege
                            [type] => privilege-set
                            [privilege-set] => GUEST
                        )

                )

            [credentials] => Array
                (
                    [method] => guest
                )

        )

)
1naresh
Array
(
    [urn:ac.highwire.org:guest:identity] => Array
        (
            [runtime-id] => urn:ac.highwire.org:guest:identity
            [type] => guest
            [service-id] => ajnr-ac.highwire.org
            [access-type] => Controlled
            [privilege] => Array
                (
                    [urn:ac.highwire.org:guest:privilege] => Array
                        (
                            [runtime-id] => urn:ac.highwire.org:guest:privilege
                            [type] => privilege-set
                            [privilege-set] => GUEST
                        )

                )

            [credentials] => Array
                (
                    [method] => guest
                )

        )

)
1naresh
Array
(
    [urn:ac.highwire.org:guest:identity] => Array
        (
            [runtime-id] => urn:ac.highwire.org:guest:identity
            [type] => guest
            [service-id] => ajnr-ac.highwire.org
            [access-type] => OpenAccess
            [privilege] => Array
                (
                    [urn:ac.highwire.org:guest:privilege] => Array
                        (
                            [runtime-id] => urn:ac.highwire.org:guest:privilege
                            [type] => privilege-set
                            [privilege-set] => GUEST
                        )

                )

            [credentials] => Array
                (
                    [method] => guest
                )

        )

)


  
    









































































































































    Deep Transfer Learning and Radiomics Feature Prediction of Survival of Patients with High-Grade Gliomas | American Journal of Neuroradiology  
    

















    





























    
  
  




    
    

  

      


    

      

  
  
  

    

  

    

  

                
    

      

  

    

  
      
  
  

    


Advertisement



  



  


  
  



  



    

  


  


  


  
  
  
  
  
  

  	      



          


    
      

    
  
    
        
    

  

    
                        

  

                
    

      

	  

		
		

			

			  

  
      
  
  

    

  
  
      
Research ArticleAdult Brain

  
      
Open Access

  
      
Deep Transfer Learning and Radiomics Feature Prediction of Survival of Patients with High-Grade Gliomas
  
    	
W. Han, L. Qin, C. Bay, X. Chen, K.-H. Yu, N. Miskin, A. Li, X. Xu and G. Young

  
    	
American Journal of Neuroradiology January 2020,  41 (1) 40-48; DOI: https://doi.org/10.3174/ajnr.A6365 

  
  
  



  


  
  



			

		

	
	
 	
	  
  

		
		

			

			  

  
      
  
  

    
  


  
  



  
      
  
  

    

  

    

  
      
  
  

    
References
  1. 1.↵
    1. Pope  WB, 
    2. Sayre  J, 
    3. Perlina  A, et al
    . MR imaging correlates of survival in patients with high-grade gliomas. AJNR Am J Neuroradiol 2005;26:2466–74 pmid:16286386
    Abstract/FREE Full Text
  2. 2.↵
    1. Zhou  M, 
    2. Scott  J, 
    3. Chaudhury  B, et al
    . Radiomics in brain tumor: image assessment, quantitative feature descriptors, and machine-learning approaches. AJNR Am J Neuroradiol 2018;39:208–16 doi:10.3174/ajnr.A5391 pmid:28982791
    Abstract/FREE Full Text
  3. 3.↵
    1. Vallieres  M, 
    2. Freeman  CR, 
    3. Skamene  SR, et al
    . A radiomics model from joint FDG-PET and MRI texture features for the prediction of lung metastases in soft-tissue sarcomas of the extremities. Phys Med Biol 2015;60:5471–96 doi:10.1088/0031-9155/60/14/5471 pmid:26119045
    CrossRefPubMed
  4. 4.↵
    1. Aerts  HJ, 
    2. Velazquez  ER, 
    3. Leijenaar  RT, et al
    . Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun 2014;5:4006 doi:10.1038/ncomms5006 pmid:24892406
    CrossRefPubMed
  5. 5.↵
    1. Qu  J, 
    2. Qin  L, 
    3. Cheng  S, et al
    . Residual low ADC and high FA at the resection margin correlate with poor chemoradiation response and overall survival in high-grade glioma patients. Eur J Radiol 2016;85:657–64 doi:10.1016/j.ejrad.2015.12.026 pmid:26860681
    CrossRefPubMed
  6. 6.↵
    1. Qin  L, 
    2. Li  A, 
    3. Qu  J, et al
    . Normalization of ADC does not improve correlation with overall survival in patients with high-grade glioma (HGG). J Neurooncol 2018;137:313–19 doi:10.1007/s11060-017-2719-y pmid:29383647
    CrossRefPubMed
  7. 7.↵
    1. Akkus  Z, 
    2. Galimzianova  A, 
    3. Hoogi  A, et al
    . Deep learning for brain MRI segmentation: state of the art and future directions. J Digit Imaging 2017;30:449–59 doi:10.1007/s10278-017-9983-4 pmid:28577131
    CrossRefPubMed
  8. 8.↵
    1. Zhou  X, 
    2. Takayama  R, 
    3. Wang  S, et al
    . Deep learning of the sectional appearances of 3D CT images for anatomical structure segmentation based on an FCN voting method. Med Phys 2017;44:5221–33 doi:10.1002/mp.12480 pmid:28730602
    CrossRefPubMed
  9. 9.↵
    1. Chmelik  J, 
    2. Jakubicek  R, 
    3. Walek  P, et al
    . Deep convolutional neural network-based segmentation and classification of difficult to define metastatic spinal lesions in 3D CT data. Med Image Anal 2018;49:76–88 doi:10.1016/j.media.2018.07.008 pmid:30114549
    CrossRefPubMed
  10. 10.↵
    1. Acharya  UR, 
    2. Oh  SL, 
    3. Hagiwara  Y, et al
    . A deep convolutional neural network model to classify heartbeats. Comput Biol Med 2017;89:389–96 doi:10.1016/j.compbiomed.2017.08.022 pmid:28869899
    CrossRefPubMed
  11. 11.↵
    1. Burlina  P, 
    2. Pacheco  KD, 
    3. Joshi  N, et al
    . Comparing humans and deep learning performance for grading AMD: a study in using universal deep features and transfer learning for automated AMD analysis. Comput Biol Med 2017;82:80–86 doi:10.1016/j.compbiomed.2017.01.018 pmid:28167406
    CrossRefPubMed
  12. 12.↵
    1. Lao  J, 
    2. Chen  Y, 
    3. Li  ZC, et al
    . A deep learning-based radiomics model for prediction of survival in glioblastoma multiforme. Sci Rep 2017;7:1035 doi:10.1038/s41598-017-10649-8 pmid:28871110
    CrossRefPubMed
  13. 13.↵
    1. Nie  D, 
    2. Zhang  H, 
    3. Adeli  E, et al
    . 3D deep learning for multi-modal imaging-guided survival time prediction of brain tumor patients. Med Image Comput Comput Assist Interv 2016;9901:212–20 doi:10.1007/978-3-319-46723-8_25 pmid:28149967
    CrossRefPubMed
  14. 14.↵
    1. Paul  R, 
    2. Hawkins  SH, 
    3. Balagurunathan  Y, et al
    . Deep feature transfer learning in combination with traditional features predicts survival among patients with lung adenocarcinoma. Tomography 2016;2:388–95 doi:10.18383/j.tom.2016.00211 pmid:28066809
    CrossRefPubMed
  15. 15.↵
    1. Zhen  X, 
    2. Chen  J, 
    3. Zhong  Z, et al
    . Deep convolutional neural network with transfer learning for rectum toxicity prediction in cervical cancer radiotherapy: a feasibility study. Phys Med Biol 2017;62:8246–63 doi:10.1088/1361-6560/aa8d09 pmid:28914611
    CrossRefPubMed
  16. 16.↵
    1. Collewet  G, 
    2. Strzelecki  M, 
    3. Mariette  F
    . Influence of MRI acquisition protocols and image intensity normalization methods on texture classification. Magn Reson Imaging 2004;22:81–91 doi:10.1016/j.mri.2003.09.001 pmid:14972397
    CrossRefPubMed
  17. 17.↵
    1. Gibbs  P, 
    2. Turnbull  LW
    . Textural analysis of contrast-enhanced MR images of the breast. Magn Reson Med 2003;50:92–98 doi:10.1002/mrm.10496 pmid:12815683
    CrossRefPubMed
  18. 18.↵
    1. Simonyan  K, 
    2. Zisseman  A
    . Very deep convolutional networks for large-scale image recognition. Computer Vision and Pattern Recognition 2014;arXiv:1409.1556
  19. 19.↵
    1. Krizhevsky  A, 
    2. Sutskever  I, 
    3. Hinton  GE
    . ImageNet classification with deep convolutional neural networks. J Commun. ACM 2017;60:84–90
  20. 20.↵
    1. Simon  N, 
    2. Friedman  J, 
    3. Hastie  T, et al
    . Regularization paths for Cox’s proportional hazards model via coordinate descent. J Stat Softw 2011;39:1–13 pmid:21572908, 27065756
    CrossRefPubMedWeb of Science
  21. 21.↵
    1. Feinstein  AR
    . Clinical Epidemiology: The Architecture of Clinical Research. Philadelphia: W.B. Saunders; 1985:xii
  22. 22.↵
    1. Paul  R, 
    2. Hawkins  SH, 
    3. Hall  LO, et al
    . Combining deep neural network and traditional image features to improve survival prediction accuracy for lung cancer patients from diagnostic CT. In: Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics, Budapest, Hungary. October 9–12, 2016 doi:10.1109/SMC.2016.7844626
  23. 23.↵
    1. Ahmed  KB, 
    2. Hall  LO, 
    3. Goldgof  DB, et al
    . Fine-tuning convolutional deep features for MRI based brain tumor classification. In: Proceedings of Medical Imaging 2017: Computer-Aided Diagnosis, Orlando, Florida. February 11–16, 2017
  24. 24.↵
    1. Choi  JY, 
    2. Yoo  TK, 
    3. Seo  JG, et al
    . Multi-categorical deep learning neural network to classify retinal images: a pilot study employing small database. PLoS One 2017;12:e0187336 doi:10.1371/journal.pone.0187336 pmid:29095872
    CrossRefPubMed
  25. 25.↵
    1. Kaiming  H, 
    2. Zhang  X, 
    3. Shaoqing  R, 
    4. Jian  S
    . Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, Navada. June 26 to July 1, 2016
  26. 26.↵
    1. Parmar  C, 
    2. Grossmann  P, 
    3. Bussink  J, et al
    . Machine learning methods for quantitative radiomic biomarkers. Sci Rep 2015;5:13087 doi:10.1038/srep13087 pmid:26278466
    CrossRefPubMed
  27. 27.↵
    1. Yin  P, 
    2. Mao  N, 
    3. Zhao  C, et al
    . Comparison of radiomics machine-learning classifiers and feature selection for differentiation of sacral chordoma and sacral giant cell tumour based on 3D computed tomography features. Eur Radiol 2019;29:1841–47 doi:10.1007/s00330-018-5730-6 pmid:30280245
    CrossRefPubMed
  28. 28.↵
    1. de Carvalho Filho  AO, 
    2. Silva  AC, 
    3. de Paiva  AC, et al
    . Lung-nodule classification based on computed tomography using taxonomic diversity indexes and an SVM. J Signal Process Syst 2017;87:179–96 doi:10.1007/s11265-016-1134-5
    CrossRef
  29. 29.↵
    1. Yu  KH, 
    2. Zhang  C, 
    3. Berry  GJ, et al
    . Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features. Nat Commun 2016;7:12474 doi:10.1038/ncomms12474 pmid:27527408
    CrossRefPubMed
  30. 30.↵
    1. Cha  S
    . CNS tumors: monitoring therapeutic response and outcome prediction. Top Magn Reson Imaging 2006;17:63–68 doi:10.1097/RMR.0b013e31802ee598 pmid:17198223
    CrossRefPubMed
  


  
  



  




  


  
  



  
      
  
  

    
  


  
  



  
      
  
  

     Back to top  


  
  



			

		

		
		

			

			  

  
        
In this issue

    
  
  

    
  


  
  



  
      
  
  

    

  

    

  
      
  
  

    


Advertisement



  



  


  
  



  



  


  
  



  
      
  
  

    

	  
  

		

          

            

  
      
  
  

     Print  


  
  



  
      
  
  

     Download PDF  


  
  



  
      
  
  

    
 Email Article
  


  
  



  
      
  
  

    
 Citation Tools
  


  
  



          

        

        
        

          

            



  
      
  
  

    

  

    

  
      
  
  

    

  
  
  
  
      
Deep Transfer Learning and Radiomics Feature Prediction of Survival of Patients with High-Grade Gliomas
  
    	
W. Han, L. Qin, C. Bay, X. Chen, K.-H. Yu, N. Miskin, A. Li, X. Xu, G. Young

  
    	
 American Journal of Neuroradiology Jan 2020, 41 (1) 40-48; DOI: 10.3174/ajnr.A6365 

  
  
  



  


  
  



  
      
  
  

    

  
    
  
  
    
  
  
    
  


  


  
  



  
      
  
  

    
del.icio.us logo Twitter logo Facebook logo Mendeley logo
  


  
  



  



  


  
  



  
      
  
  

    
  


  
  



          

        

	
 	
	
	


  


  
  



  
      
  
  

    
              Purchase  


  
  



  
        
Jump to section

    
  
  

    
  


  
  



  
      
  
  

    
  


  
  



			

		

	
	
 	
	
	


    

  



  

                
    

      

  

    

  
      
  
  

    


Advertisement