Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
570-11-249 ISMRM Abstract

Utilization of Data-Driven Multivariate Decision Tree Age-Sex Stratification of Pediatric Spinal Cord MR Images

Primary: Pediatrics - Normal Development
Secondary: Analysis Methods - Data Processing
570-11-249 · Pediatric MR Imaging in Practice · Wednesday, 13 May, 4:55 PM–5:50 PM · Traditional Posters | Exhibition Hall
Keywords: Pediatric spinal cord Age-sex stratification Multivariate decision tree
Accepted
Zahra Sadeghi Adl1, Devon Middleton2,3,4, Laura Krisa3,4,5,6, Sara Naghizadehkashani1, Mary Jane Mulcahey6, Feroze Mohamed 2,3,4
1Department of Radiology, Jefferson Integrated Magnetic Resonance Imaging Center, Thomas Jefferson University, Philadelphia, Pennsylvania, United States of America
2Thomas Jefferson University, Philadelphia, United States of America
3Radiology, Thomas Jefferson University, Philadelphia, United States of America
4Jefferson Integrated Magnetic Resonance Imaging Center, Department of Radiology, Thomas Jefferson University, Philadelphia, United States of America
5Occupational Therapy, Thomas Jefferson University, Philadelphia, United States of America
6Department of Occupational Therapy, Thomas Jefferson University, Philadelphia, United States of America
Presenting Author: Feroze Mohamed

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References

1. Reynolds, B., et al., Quantification of DTI in the pediatric spinal cord: application to clinical evaluation in a healthy patient population. American Journal of Neuroradiology, 2019. 40(7): p. 1236-1241. DOI: 10.3174/ajnr.A6104 · PMID: 31196859 [doi] [pmid]
2. Alizadeh, M., et al., Age related diffusion and tractography changes in typically developing pediatric cervical and thoracic spinal cord. Neuroimage Clin. 2018; 18: 784–92. DOI: 10.1016/j.nicl.2018.03.014 · PMID: 29876264 [doi] [pmid]
3. Sadeghi-Adl, Z., et al. "Severity Classification of Pediatric Spinal Cord Injuries Using Structural MRI Measures and Deep Learning: A Comprehensive Analysis Across All Vertebral Levels." American Journal of Neuroradiology (2025). DOI: 10.3174/ajnr.A8770 · PMID: 40194851. [doi] [pmid]
4. Papinutto, N., et al., MRI measurement of upper cervical spinal cord cross‐sectional area in children. Journal of Neuroimaging, 2020. 30(5): p. 598-602. DOI: 10.1111/jon.12758 · PMID: 32639671. [doi] [pmid]
5. Saksena, S., et al., Diffusion tensor imaging of the normal cervical and thoracic pediatric spinal cord. American Journal of Neuroradiology, 2016. 37(11): p. 2150-2157. DOI: 10.3174/ajnr.A4883 · PMID: 27418470 · PMCID: PMC7963763. [doi] [pmid]
6. Altman, D.G. and P. Royston, The cost of dichotomising continuous variables. Bmj, 2006. 332(7549): p. 1080.
7. Ma, J., et al., Poor handling of continuous predictors in clinical prediction models using logistic regression: a systematic review. Journal of Clinical Epidemiology, 2023. 161: p. 140-151. DOI: 10.1016/j.jclinepi.2023.07.017 · PMID: 37536504. [doi] [pmid]
8. De Leener, B., et al., SCT: Spinal Cord Toolbox, an open-source software for processing spinal cord MRI data. Neuroimage, 2017. 145: p. 24-43. DOI: 10.1016/j.neuroimage.2016.10.009 · PMID: 27720818. [doi] [pmid]

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http://echo.ismrm.org/p/ISMRM2026/570-11-249