565-04-013 ISMRM Abstract

Optimizing MRI Sequence Selection for Glioma Classification - Do we really need all sequences?

Primary: Neuro - Tumors

Secondary: Analysis Methods - Radiomics

565-04-013 · Neuroradiomics · Wednesday, 13 May, 2:35 PM–3:30 PM · Digital Posters Row F

Keywords: Machine Learning/Artificial Intelligence Radiomics Glioma SEQUENCES

Accepted

Kimberly Amador¹, Helge Kniep², Nils D Forkert¹, Jens Fiehler², Thomas Lindner ²

¹MIPLab, University of Calgary, Calgary, Canada

²Neuroradiology, University Hospital Hamburg Eppendorf, Germany

Presenting Author: Thomas Lindner

Synopsis

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References

1. Calabrese E, Villanueva-Meyer JE, Rudie JD, Rauschecker AM, Baid U, Bakas S, Cha S, Mongan JT, Hess CP. The University of California San Francisco Preoperative Diffuse Glioma MRI Dataset. Radiol Artif Intell. 2022 Oct 5;4(6):e220058. doi: 10.1148/ryai.220058. PMID: 36523646; PMCID: PMC9748624. [doi] [pmid]

2. Srivastava R, Cole L, Amador K, Forkert ND, Dunbar M, Shevell MI, Oskoui M, Basu AP, Rivkin MJ, Shany E, de Vries LS, Dewey D, Letourneau N, Mouches P, Hill MD, Kirton A. Risk Factors for Perinatal Arterial Ischemic Stroke: A Machine Learning Approach. Neurology. 2024 Jun;102(11):e209393. doi: 10.1212/WNL.0000000000209393. Epub 2024 May 15. PMID: 38748936. [doi] [pmid]

3. Lin, G., Chen, W., Geng, Y. et al. A multimodal MRI-based machine learning framework for classifying cognitive impairment in cerebral small vessel disease. Sci Rep 15, 13112 (2025). https://doi.org/10.1038/s41598-025-97552-9 [doi]

4. Talai AS, Sedlacik J, Boelmans K, Forkert ND. Utility of Multi-Modal MRI for Differentiating of Parkinson's Disease and Progressive Supranuclear Palsy Using Machine Learning. Front Neurol. 2021 Apr 14;12:648548. doi: 10.3389/fneur.2021.648548. PMID: 33935946; PMCID: PMC8079721. [doi] [pmid]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/565-04-013