|ECR 2019 / C-2003|
|Radiomic evaluation of treatment response in patients with glioblastoma: a preliminary study|
- 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.
Thematically related posters
ECR 2019 / C-3246
Precision Radiology in Glioma Prognostication: Machine Learning & Quantifiable Biomarkers