Table 1 Patient characteristics of the Burdenko’s Glioblastoma Progression Dataset (Burdenko) and the GlioCMV UKER progression dataset (UKER).

From: A self-supervised multimodal deep learning approach to differentiate post-radiotherapy progression from pseudoprogression in glioblastoma

Variable

Burdenko (n = 59)

GlioCMV UKER (n = 20)

Gender

 Female, n (%)

28 (47.5)

11 (55)

 Male, n (%)

31 (52.5)

9 (45)

IDH Mutation Status

 Mutant, n (%)

8 (13.6)

1 (5)

 Wildtype, n (%)

36 (61.0)

19 (95)

 Unknown, n (%)

15 (25.4)

0 (0)

MGMT Methylation Status

 Methylated, n (%)

14 (23.7)

9 (45)

 Unmethylated, n (%)

23 (40.0)

3(15)

 Unknown, n (%)

22 (37.3)

8 (40)

Progression Classification

 True Progression, n (%)

34 (57.6)

11 (55)

 Pseudoprogression, n (%)

25 (42.4)

9 (45)

Age in years, median, range

57, (18–82)

58.5, (45–75)