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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

Conclusion

  • 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.

 

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