Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
469-06-004 ISMRM Abstract

Chemo-Informed Radiomics: Integrating Temozolomide Status for Improved Glioblastoma Response Prediction

Primary: Neuro - Tumors
Secondary: Diffusion - Diffusion Analysis & Visualization
469-06-004 · Radiomics: Applications · Tuesday, 12 May, 4:55 PM–5:50 PM · Digital Posters Row J
Keywords: Radiomics Glioblastoma MRI diffusion imaging Pseudoprogression Chemotherapy
Accepted
Taha Belbadaoui1, Andrew Forester1, Philippe Dionne1,2,3, Gérémy Michaud 1,2,3,4, Louis Gagnon3,5
1Physics, Engineering Physics and Optics, Université Laval, Québec, Canada
2Medical Physics, CHU de Québec - Université Laval, Québec, Canada
3CERVO Brain Research Center, Québec, Canada
4Université Laval, Québec, Canada
5Radiology and Nuclear Medicine, Université Laval, Québec, Canada
Presenting Author: Gérémy Michaud

Synopsis

Motivation:
Goals:
Approach:
Results:
Full abstract & presentation

The full text, figures, and any recorded presentation for this abstract are not shown here. Log in if you are a member or registered attendee with access.

Full abstracts, figures, and presentations for Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition are available to registered attendees. This content becomes freely available to the public roughly two years after the meeting.

To request or purchase access, contact the ISMRM Central Office at info@ismrm.org.

Log in

References

1. Wen PY, Macdonald DR, Reardon DA, et al. Updated response assessment criteria for high-grade gliomas: Response Assessment in Neuro-Oncology (RANO) Working Group. J Clin Oncol. 2010;28(11):1963-1972. doi:10.1200/JCO.2009.26.3541 PMID:20231676 [doi] [pmid]
2. Verma N, Cowperthwaite MC, Burnett MG, Markey MK. Differentiating tumor recurrence from treatment necrosis: a review of neuro-oncologic imaging strategies. Neuro Oncol. 2013;15(5):515-534. doi:10.1093/neuonc/nos307 PMID:23325863 [doi] [pmid]
3. White NS, Leergaard TB, D’Arceuil H, Bjaalie JG, Dale AM. Probing tissue microstructure with restriction spectrum imaging: histological and theoretical validation. Hum Brain Mapp. 2013;34(2):327-346. doi:10.1002/hbm.21454 PMID:23169482 [doi] [pmid]
4. Gagnon L, Gupta D, Mastorakos G, et al. The University of California San Diego annotated post-treatment high-grade glioma multimodal MRI dataset (UCSD-PTGBM) [dataset]. The Cancer Imaging Archive. 2025. doi:10.7937/fwv2-dt74 [doi]
5. 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 PMID:29092951 [doi] [pmid]
6. Isensee F, Jaeger PF, Kohl SAA, Petersen J, Maier-Hein KH. nnU-Net: a self-configuring method for deep learning–based biomedical image segmentation. Nat Methods. 2021;18(2):203-211. doi:10.1038/s41592-020-01008-z PMID:33288961 [doi] [pmid]
7. Gagnon L, Gupta D, Mastorakos G, et al. Deep learning segmentation of infiltrative and enhancing cellular tumor at pre- and posttreatment multishell diffusion MRI of glioblastoma. Radiology: Artificial Intelligence. 2024;6(5):e230489. doi:10.1148/ryai.230489 PMID:39166970 [doi] [pmid]
8. Xu QS, Liang YZ. Monte Carlo cross validation. Chemometr Intell Lab Syst. 2001;56(1):1-11. doi:10.1016/S0169-7439(00)00122-2 [doi]
9. Geurts P, Ernst D, Wehenkel L. Extremely randomized trees. Mach Learn. 2006;63:3-42. doi:10.1007/s10994-006-6226-1 [doi]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/469-06-004