Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
469-06-005 ISMRM Abstract

A Radiomics-Based Model for Early Differentiation of Pseudoprogression in Post-Treatment Glioblastoma

Primary: Analysis Methods - Radiomics
Secondary: Analysis Methods - Classification and Prediction
469-06-005 · Radiomics: Applications · Tuesday, 12 May, 4:55 PM–5:50 PM · Digital Posters Row J
Keywords: Radiomics Machine learning Pseudoprogression Tumor progression
Accepted
Catarina Passarinho1,2, Gulnur S Ungan3, Carles Majós4, Albert Pons-Escoda4, Ana Matoso1,5, Marta P Loureiro 1,2, Patricia Figueiredo1, Rita G Nunes1, Margarida Julià-Sapé6
1Institute for Systems and Robotics – Lisboa and Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal
2Hospital da Luz, Luz Saúde, Lisbon, Portugal
3Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, United States of America
4Grup de Neuro-oncologia, Institut d'Investigació Biomèdica de Bellvitge (IDIBELL), Hospital Universitari de Bellvitge, Barcelona, Spain
5Hospital da Luz Learning Health, Luz Saúde, Lisbon, Portugal
6Departament de Bioquímica i Biologia Molecular and Institut de Biotecnologia i Biomedicina, Universitat Autònoma de Barcelona, Cerdanyola del Vallès, Spain
Presenting Author: Marta P Loureiro

Synopsis

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References

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2. Ari, A. P., et al. (2022) Pseudoprogression prediction in high grade primary CNS tumors by use of radiomics. Scientific Reports, 12(1), 5915. doi:10.1038/s41598-022-09945-9 [doi]
3. Parvez, K., et al. (2014) The diagnosis and treatment of pseudoprogression, radiation necrosis and brain tumor recurrence. International Journal of Molecular Sciences, 15(7), 11832–11846. doi:10.3390/ijms150711832 [doi]
4. Reddy, S., et al. (2025) Radiomics and radiogenomics in differentiating progression, pseudoprogression, and radiation necrosis in gliomas. Biomedicines, 13(7), 1778. doi:10.3390/biomedicines13071778 [doi]
5. Turk, S., et al. (2022) Comparative study of radiologists vs machine learning in differentiating biopsy-proven pseudoprogression and true progression in diffuse gliomas. Neuroscience Informatics, 2(3), 100088. doi:10.1016/j.neuri.2022.100088 [doi]
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7. Mammadov, O., et al. (2022) Radiomics for pseudoprogression prediction in high grade gliomas: added value of MR contrast agent. Heliyon, 8(8), e10023. doi:10.1016/j.heliyon.2022.e10023 [doi]
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9. Ungan, G., et al. (2024) Early pseudoprogression and progression lesions in glioblastoma patients are both metabolically heterogeneous. NMR in Biomedicine, 37(4), e5095. doi:10.1002/nbm.5095 [doi]
10. Wen, P. Y., et al. (2010) Updated response assessment criteria for high-grade gliomas: response assessment in neuro-oncology working group. Journal of Clinical Oncology: Official Journal of the American Society of Clinical Oncology, 28(11), 1963–1972. doi:10.1200/JCO.2009.26.354111. [doi]
11. https://github.com/ANTsX/ANTs/wiki/N4BiasFieldCorrection
12. van Griethuysen, J. J. M., et al. (2017) Computational radiomics system to decode the radiographic phenotype. Cancer Research, 77(21), e104–e107. doi:10.1158/0008-5472.can-17-0339 [doi]
13. Mylona, E., et al. (2024) Optimizing radiomics for prostate cancer diagnosis: feature selection strategies, machine learning classifiers, and MRI sequences. Insights into Imaging, 15(1), 265. doi:10.1186/s13244-024-01783-9 [doi]
14. https://pyradiomics.readthedocs.io/en/latest/features.html

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/469-06-005