MRI Response Assessment in Patients with Glioblastoma Treated with Dendritic Cell-Based Immunotherapy - European Medical Journal

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MRI Response Assessment in Patients with Glioblastoma Treated with Dendritic Cell-Based Immunotherapy

2 Mins
Neurology
Authors:
Johanna Heugenhauser,1 Malik Galijasevic,2,3 *Stephanie Mangesius,2,3 Georg Goebel,4 Friedrich Erhart,5,6 Josef Pichler,7 Georg Widhalm,5 Günther Stockhammer,1 Sarah Iglseder,1 Christian Freyschlag,8 Stefan Oberndorfer,9 Karin Bordihn,10 Gord von Campe,11 Thomas Czech,5 Birgit Surböck,12 Tadeja Urbanic Purkart,13 Christine Marosi,14 Thomas Felzmann,15 *Martha Nowosielski1

1. Department of Neurology, Medical University of Innsbruck, Austria
2. Department of Neuroradiology, Medical University of Innsbruck, Austria
3. Neuroimaging Core Facility, Medical University of Innsbruck, Austria
4. Department of Medical Statistics, Informatics and Health Economics, Medical University of Innsbruck, Austria
5. Department of Neurosurgery, Medical University of Vienna, Austria
6. Department of Neurology, Medical University of Vienna, Austria
7. Department of Internal Medicine and Neurooncology, Johannes Kepler University, Linz, Austria
8. Department of Neurosurgery, Medical University of Innsbruck, Austria
9. Department of Neurology, Landsteiner Private University, St. Pölten, Austria
10. Department of Neurosurgery, Paracelsus Private Medical University, Salzburg, Austria
11. Department of Neurosurgery, Medical University of Graz, Austria
12. Department of Neurology, Klinik Favoriten, Wiener Gesundheitsverbund, Vienna, Austria
13. Department of General Neurology and Neuroradiology, Vascular and Interventional Radiology, Medical University of Graz, Austria
14. Clinical Division of Medical Oncology, Department for Internal Medicine I, Medical University of Vienna, Austria
15. Activartis Biotech GmbH, Vienna, Austria
*Correspondence to [email protected] and [email protected]

Disclosure:

The authors have declared no conflicts of interest.

Citation:
EMJ Neurol. ;10[1]:31-32. DOI/10.33590/emjneurol/ 10034324. https://doi.org/10.33590/emjneurol/10034324.
Keywords:
Glioblastoma (GB), immunotherapy, immunotherapy response assessment in neuro-oncology (iRANO), modified response assessment in neuro-oncology (mRANO), radiologic response criteria, volumetric measurements.

Each article is made available under the terms of the Creative Commons Attribution-Non Commercial 4.0 License.

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.

RESULTS

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

CONCLUSION

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.

References
Hygino da Cruz LC, Jr. et al. Pseudoprogression and pseudoresponse: imaging challenges in the assessment of posttreatment glioma. AJNR Am J Neuroradiol. 2011;32(11):1978-85. Macdonald DR et al. Response criteria for phase II studies of supratentorial malignant glioma. J Clin Oncol. 1990;8(7):1277-80. Wen PY et al. Updated response assessment criteria for high-grade gliomas: response assessment in neuro-oncology working group. J Clin Oncol. 2010;28(11):1963-72. Clarke JL, Chang S. Pseudoprogression and pseudoresponse: challenges in brain tumor imaging. Curr Neurol Neurosci Rep. 2009;9(3):241-6. Ellingson BM et al. Modified criteria for radiographic response assessment in glioblastoma clinical trials. Neurotherapeutics. 2017;14(2):307-20. Okada H et al. Immunotherapy response assessment in neuro-oncology: a report of the RANO working group. Lancet Oncol. 2015;16(15):e534-42. Kickingereder P et al. Automated quantitative tumour response assessment of MRI in neuro-oncology with artificial neural networks: a multicentre, retrospective study. Lancet Oncol. 2019;20(5):728-40. Yushkevich PA et al. User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability. Neuroimage. 2006;31(3):1116-28. Heugenhauser J et al. MRI response assessment in glioblastoma patients treated with dendritic-cell-based immunotherapy. Cancers (Basel). 2022;14(6):1579.

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