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ECR 2019 / C-2003
Radiomic evaluation of treatment response in patients with glioblastoma: a preliminary study
Congress: ECR 2019
Poster No.: C-2003
Type: Scientific Exhibit
Keywords: Cancer, Computer Applications-Detection, diagnosis, MR, Neuroradiology brain, CNS, Artificial Intelligence
Authors: M. D. Patel1, J. Zhan2, K. Natarajan1, R. Flintham3, N. Davies 3, P. Sanghera1, A. Peet1, V. Duddalwar4, V. Sawlani1; 1Birmingham/UK, 2Qingdao/CN, 3Birmingham /UK, 4Los Angeles/US
DOI:10.26044/ecr2019/C-2003

References

  1. Brandes AA, Franceschi E, Tosoni A, et al. MGMT promoter methylation status can predict the incidence and outcome of pseudoprogression after concomitant radiochemotherapy in newly diagnosed glioblastoma patients. J Clin Oncol. 2008;26(13):2192-7.
  2. Gillies RJ, Kinahan PE, Hricak H. Radiomics: Images Are More than Pictures, They Are Data. Radiology. 2016;278(2):563-77.
  3. Erickson BJ, Korfiatis P, Akkus Z, Kline TL. Machine Learning for Medical Imaging. Radiographics. 2017;37(2):505-515.
  4. Paul A. Yushkevich, Joseph Piven, Heather Cody Hazlett, Rachel Gimpel Smith, Sean Ho, James C. Gee, and Guido Gerig. User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 2006 Jul 1;31(3):1116-28.
  5. Sarthak Pati, Spyridon Bakas, Aristeidis Sotiras, Ratheesh Kalarot, Patmaa Sridharan, Mark Bergman, Saima Rathore, Hamed Akbari, Paul Yushkevich, Taki Shinohara, Yong Fan, Despina Kontos, Ragini Verma, Christos Davatzikos. "Cancer Imaging Phenomics Toolkit (CaPTk): A Radio(geno)mics Software Platform Leveraging Quantitative Imaging Analytics for Computational Oncology", 103rd Scientific Assembly and Annual Meeting of the Radiological Society of North America (RSNA), Nov.26–Dec.1, 2017, Chicago IL.

 

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