Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
652-01-001 / 652-01-001 ISMRM Abstract

Harmonizing Patch-Based Radiomics in Longitudinal Glioblastoma MRI: Can Canonical Correlation Analysis Boost Interpretation?

Primary: Analysis Methods - Data Processing
Secondary: Analysis Methods - Radiomics
652-01-001 · Prognostication and Predictive Value of MR Biomarkers · Thursday, 14 May, 8:30 AM–10:06 AM · Power Pitch Theatre 2
652-01-001 · Prognostication and Predictive Value of MR Biomarkers · Thursday, 14 May, 8:30 AM–10:06 AM · Power Pitch Theatre 2
Keywords: Glioblastoma Longitudinal Studies Harmonization Canonical Correlation Analysis
Accepted
Marta P Loureiro 1,2, Catarina Passarinho1,2, Ana Matoso1,3, M. Rosário Oliveira4, Pedro Vilela5, Patricia Figueiredo1, Rita G Nunes1
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
3Hospital da Luz Learning Health, Luz Saúde, Lisbon, Portugal
4CEMAT and Department of Mathematics, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal
5Imaging Department, Hospital da Luz, Luz Saúde, Lisbon, Portugal
Presenting Author: Marta P Loureiro

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References

1. Ostrom QT, Price M, Neff C, et al. CBTRUS Statistical Report: Primary Brain and Other Central Nervous System Tumors Diagnosed in the United States in 2015-2019. Neuro-Oncol. 2022;24(Suppl 5):v1-v95. doi:10.1093/neuonc/noac202 [doi]
2. Gilard V, Tebani A, Dabaj I, et al. Diagnosis and Management of Glioblastoma: A Comprehensive Perspective. J Pers Med. 2021;11(4):258. doi:10.3390/jpm11040258 [doi]
3. Carré A, Klausner G, Edjlali M, et al. Standardization of brain MR images across machines and protocols: bridging the gap for MRI-based radiomics. Sci Rep. 2020;10(1):12340. doi:10.1038/s41598-020-69298-z [doi]
4. Fortin JP, Sweeney EM, Muschelli J, Crainiceanu CM, Shinohara RT. Removing inter-subject technical variability in magnetic resonance imaging studies. NeuroImage. 2016;132:198. doi:10.1016/j.neuroimage.2016.02.036 [doi]
5. Fortin JP, Cullen N, Sheline YI, et al. Harmonization of cortical thickness measurements across scanners and sites. NeuroImage. 2017;167:104. doi:10.1016/j.neuroimage.2017.11.024 [doi]
6. Beer JC, Tustison NJ, Cook PA, et al. Longitudinal ComBat: A method for harmonizing longitudinal multi-scanner imaging data. NeuroImage. 2020;220:117129. doi:10.1016/j.neuroimage.2020.117129 [doi]
7. Zhuang X, Yang Z, Cordes D. A technical review of canonical correlation analysis for neuroscience applications. Hum Brain Mapp. 2020;41(13):3807-3833. doi:10.1002/hbm.25090 [doi]
8. Kickingereder P, Isensee F, Tursunova I, 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):728-740. doi:10.1016/S1470-2045(19)30098-1 [doi]
9. van Griethuysen JJM, Fedorov A, Parmar C, et al. Computational Radiomics System to Decode the Radiographic Phenotype. Cancer Res. 2017;77(21):e104-e107. doi:10.1158/0008-5472.CAN-17-0339 [doi]

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http://echo.ismrm.org/p/ISMRM2026/652-01-001