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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

Results

Results showed several features demonstrating significant difference between the true progression and pseudoprogression groups. For enhancing disease on T1W imaging, the significant GLCM features were contrast and homogeneity, and significant GLRLM features were grey level non-uniformity and run length non-uniformity. For perilesional oedema, the significant GLRLM features were grey level non-uniformity and run length non-uniformity. There were also significant differences in the volume of enhancing disease and perilesional oedema between both groups. Results are summarised in Table 1 (Fig. 3).

 

Fig. 3: Results table demonstrating the significant features and differences between the true progression (tPD) and pseudoprogression (psPD) groups.
References: - Birmingham/UK

 

The results suggest that computer vision can detect differences between tumours demonstrating early true progression, despite there being no discernible differences to oncologists and radiologists, both clinically and on imaging.

 

 

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