Brought to you by
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


  • This pilot study has shown that radiomic texture features can differentiate between early true progression and pseudoprogression in glioblastoma. 
  • The most significant radiomic features distinguishing pseudoprogression from true progression were contrast, homogeneity, grey level non-uniformity and run length non-uniformity. The volumes of enhancing disease and perilesional oedema were also significantly different between both groups.
  • Big data incorporating machine learning is required to produce strong prediction models for earlier prediction of treatment response.


POSTER ACTIONS Add bookmark Contact presenter Send to a friend Download pdf
2 clicks for more privacy: On the first click the button will be activated and you can then share the poster with a second click.

This website uses cookies. Learn more