BACKGROUND AND AIMS
In glioblastoma (GB), patients’ reliable response assessment criteria are crucial in order to accurately compare the responsiveness of different therapies in clinical trials, and to differentiate between therapy-induced changes and true tumour progression. Radiochemotherapy and immunologic strategies, as well as radiologic phenomena such as pseudoprogression (PsP), challenge the current imaging response criteria.1 In 1990, the MacDonald criteria were introduced, using two-dimensional tumour measurements, as well as corticosteroid use and the clinical performance of the patient for response assessment.2 The Response Assessment in Neuro-oncology (RANO) criteria3 additionally utilise T2-weighted or fluid-attenuated inversion recovery image sequences to account for non-enhancing tumour components and therapy-induced MRI changes.3,4 To better account for the phenomenon of PsP, the modified RANO (mRANO) criteria were proposed in 2017.5 In consideration of unique patterns of PsP during immunotherapy of GB, the immunotherapy RANO (iRANO) criteria6 were developed.
The aim of this study was to compare the different response criteria in patients with GB treated by dendritic cell immunotherapy in addition to standard of care, and in order to detect the best response criteria for prediction of progression-free survival (PFS) and overall survival (OS).
MATERIAL AND METHODS
The authors retrospectively analysed imaging data from 76 patients with newly diagnosed GB World Health Organization (WHO) Grade IV, treated with either standard of care (SOC), or SOC plus a personalised dendritic cell-based vaccine. For 2D, response-assessment criteria MacDonald,2 RANO,3 mRANO,5 and iRANO,6 were used, and for 3D assessment methods Vol-mRANO5 and Vol-RANO7 were applied to each available MRI scan obtained from each patient (postoperative and follow-up MRI). In order to calculate tumour volume, tumour segmentation was performed using a semiautomated active contour method (ITK-SNAP 3.8.0).8 Differences in PFS among the assessment criteria were calculated by the Kruskal–Wallis test, and results were corrected for multiple comparison (Bonferroni’s adjustment). For correlation analysis between PFS and OS, the Spearman test was used.
Overall, there was a significant difference in median PFS between mRANO (8.6 months) and Vol-mRANO (8.6 months) compared with MacDonald (4.0 months), RANO (4.2 months) and Vol-RANO (5.4 months). In the audencel subgroup, there was a significant difference in median PFS between mRANO (8.1 months) and Vol-mRANO (8.6 months) compared with MacDonald (4.2 months). The best correlation between PFS and OS was detected for Vol-mRANO (r=0.69) and mRANO (r=0.65), followed by MacDonald (r=0.44), RANO (r=0.45), Vol-RANO (r=0.46), and iRANO (r=0.50). The impact of progressive disease on median OS at the 4-month landmark time was most distinct for mRANO, Vol-mRANO, and iRANO, and at the 8-month landmark time for mRANO and Vol-mRANO. For those criteria, the greatest difference in OS between stable disease and progressive disease at the specific landmark time was observed. By applying mRANO, 19 patients (25.0%) had confirmed PsP. When iRANO was applied to patients treated with SOC plus audencel, four patients (11.1%) had confirmed PsP.9
mRANO criteria are superior to MacDonald and RANO for predicting progression in patients with newly diagnosed GB treated with SOC±additional audencel-based immunotherapy. Moreover, the best correlation between PFS and OS was seen for mRANO and Vol-mRANO.