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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

Methods and materials


We retrospectively analysed 20 MRI studies of patients with biopsy-proven glioblastoma who had standard chemo-radiotherapy treatment and early progressive enhancing disease. Studies were labelled as true progression (n=11) if there was progression or death within six months or pseudoprogression (n=9) if there was no further progression within six months. 


The T1-weighted post-contrast and T2-weighted sequences were co-registered to allow segmentation of tumour components. Enhancing disease and perilesional oedema were segmented from the two sequences respectively to create volumes of interest (Fig. 2) using ITK-SNAP open-source software4 with a semi-automatic method with manual adjustment.


Fig. 2: Co-registration of T1-weighted post-contrast and T2-weighted MRI images with segmentation of enhancing tumour (red) and peritumoural oedema (yellow). Volumes of interest created (right).
References: - Birmingham/UK


Radiomic texture features were extracted from the volumes of interest using CaPTk software5 and more than 280 features were extracted per MRI sequence. The features included morphological, grey level co-occurrence matrix (GLCM), grey level run length matrix (GLRLM), neighbouring grey tone difference matrix (NGTDM) and grey level size-zone matrix (GLSZM) features. Statistical analysis was performed using SPSS software to calculate the differences between the true progression and pseudoprogression groups. 


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