Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
603-03-002 ISMRM Abstract

SMART-Risk Model to Distinguish Recurrence from pseudoprogression in Brain Tumors: A Large multi-institutional study

Primary: Neuro - Tumors
Secondary: Analysis Methods - Classification and Prediction
603-03-002 · AI for Diagnostic and Prognostic Applications · Thursday, 14 May, 4:00 PM–5:50 PM · Auditorium 1
Keywords: Radiomics Neuro-Oncology Tumor Recurrence Machine learning classification
Accepted
Dheerendranath Battalapalli1,2, Hyemin Um3, Marwa Ismail3, Virginia B Hill4, Sushant Puri5, Jennifer S Yu6, Lan Lu7, Ameya P Nayate8, Anthony Higinbotham9, Lisa R Rogers10, Prateek Prasanna11, Mainak Bardhan12, Chengnan Li13, Mustafa M Basree14, Andrew M Baschnagel13, Alan B McMillan15, Ankush Bhatia16, Manmeet Singh Ahluwalia12, Michael C Veronesi13, Pallavi Tiwari2,3,17
1Radilology, University of Wisconsin - Madison, Madison, United States of America
2Biomedical Engineering, University of Wisconsin - Madison, Madison, United States of America
3Radiology, University of Wisconsin - Madison, Madison, United States of America
4Department of Cardiology, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America
5Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, United States of America
6Learner Research Institute, Cleveland Clinic, Cleveland, United States of America
7Department of Radiation Oncology, Cleveland Clinic, Cleveland, United States of America
8Department of Radiology, University Hospitals Cleveland Medical Center, Cleveland, Ohio, United States of America
9Radiology and medical imaging, University of Virginia School of Medicine, Charlottesville, United States of America
10Hermelin Brain Tumor Center, Henry Ford Health, Detroit, United States of America
11Stony Brook University, Stony Brook, United States of America
12Miami Cancer Institute, Miami, United States of America
13University of Wisconsin - Madison, Madison, United States of America
14Human Oncology, University of Wisconsin - Madison, Madison, United States of America
15Medical Physics and Radiology, University of Wisconsin - Madison, Madison, United States of America
16Neurology, University of Wisconsin - Madison, Madison, United States of America
17William S. Middleton Memorial VA, Madison, Wisconsin, United States of America
Presenting Author: Daiki Tamada

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References

1. Zhou Q, Xue C, Ke X, Zhou J. Treatment Response and Prognosis Evaluation in High-Grade Glioma: An Imaging Review Based on MRI. J Magn Reson Imaging. 2022 Aug;56(2):325-340. doi: 10.1002/jmri.28103. Epub 2022 Feb 7. PMID: 35129845. [doi] [pmid]
2. Pope WB. Brain metastases: neuroimaging. Handb Clin Neurol. 2018;149:89-112. doi: 10.1016/B978-0-12-811161-1.00007-4. PMID: 29307364; PMCID: PMC6118134. [doi] [pmid]
3. Derks SHAE, van der Veldt AAM, Smits M. Brain metastases: the role of clinical imaging. Br J Radiol. 2022 Feb 1;95(1130):20210944. doi: 10.1259/bjr.20210944. Epub 2021 Dec 14. PMID: 34808072; PMCID: PMC8822566. [doi] [pmid]
4. van Timmeren JE, Cester D, Tanadini-Lang S, Alkadhi H, Baessler B. Radiomics in medical imaging-"how-to" guide and critical reflection. Insights Imaging. 2020 Aug 12;11(1):91. doi: 10.1186/s13244-020-00887-2. PMID: 32785796; PMCID: PMC7423816. [doi] [pmid]
5. Prasanna, P., Tiwari, P., & Madabhushi, A. (2016). Co-occurrence of Local Anisotropic Gradient Orientations (CoLlAGe): A new radiomics descriptor. Scientific Reports, 6, 37241. https://doi.org/10.1038/srep37241 [doi]
6. Battalapalli, D., Safai, A., Jaramillo, M., Um, H., Ortiz, G.A.P., Bagci, U., Ahluwalia, M.S., Ismail, M. and Tiwari, P., 2025. Graph-Radiomic Learning (GrRAiL) Descriptor to Characterize Imaging Heterogeneity in Confounding Tumor Pathologies. arXiv preprint arXiv:2509.19258.
7. Battalapalli, D., Safai, A., Ismail, M., Hill, V., Statsevych, V., Huang, R., Ahluwalia, M. S. & Tiwari, P. (2024, May). Graph-Radiomics Learning (GrRAiL): Application to Distinguishing Glioblastoma Recurrence from Pseudo-Progression on Structural MRI. 2024 IEEE International Symposium on Biomedical Imaging (ISBI), pp. 1-5. doi: 10.1109/ISBI56570.2024.10635456 [doi]
8. Battalapalli D, Vidyadharan S, Prabhakar Rao BVVSN, Yogeeswari P, Kesavadas C, Rajagopalan V. Fractal dimension: analyzing its potential as a neuroimaging biomarker for brain tumor diagnosis using machine learning. Front Physiol. 2023 Jul 17;14:1201617. doi: 10.3389/fphys.2023.1201617. PMID: 37528895; PMCID: PMC10390093. [doi] [pmid]
9. Ismail M, Hill V, Statsevych V, Huang R, Prasanna P, Correa R, Singh G, Bera K, Beig N, Thawani R, Madabhushi A, Aahluwalia M, Tiwari P. Shape Features of the Lesion Habitat to Differentiate Brain Tumor Progression from Pseudoprogression on Routine Multiparametric MRI: A Multisite Study. AJNR Am J Neuroradiol. 2018 Dec;39(12):2187-2193. doi: 10.3174/ajnr.A5858. Epub 2018 Nov 1. PMID: 30385468; PMCID: PMC6529206. [doi] [pmid]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/603-03-002