Background: Detection of progression is clinically important for the management of glioblastoma. We sought to assess the accuracy of clinical radiological reporting and measured bidimensional products to identify radiological glioblastoma progression. The two were compared to volumetric segmentation.
Methods: In this retrospective study, we included 106 patients with histopathologically verified glioblastomas and two separate MRI scans obtained before surgery. Bidimensional products based on measurements on the axial slice with the largest tumor area were calculated, and growth estimations from the clinical radiological reports were retrieved. The two growth estimations were compared to manual volumetric segmentations. Inter-observer agreement using the bidimensional product was assessed using Kappa-statistics and by calculating the difference between two neuroradiologist in percentage change of the bidimensional product for each tumor.
Results: Clinical radiological reports and bidimensional products showed fairly equal accuracy when compared to volumetric segmentation with an accuracy of 67% and 66-68%, respectively. There was a difference in median volume increase of 6.9 mL (2.4 vs 9.3 mL, p < 0.001) between tumors evaluated as stable and progressed based on the clinical radiological reports. This difference was 8.1 mL (2.0 vs 10.1 ml, p < 0.001) when using the bidimensional products. The bidimensional product reached a moderate inter-observer agreement with a Kappa value of 0.689. For 32% of the tumors, the two neuroradiologists calculated a difference of more than 25% using bidimensional products.
Conclusions: Clinical radiological reporting and the bidimensional product exhibit similar accuracy. The bidimensional product has moderate inter-observer agreement and is prone to underestimating tumor progression, as an average glioblastoma had to grow 10 mL in order to be classified as progressed. These findings underline the assumption that one should try to move towards volumetric assessment of glioblastoma growth in the future.
Keywords: Brain; Glioblastoma; Progression; Segmentation; Tumor; Variability.