Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
652-01-002 / 652-01-002 ISMRM Abstract

Prediction of High Mortality and Morbidity Incident Disease Using Machine Learning with Wholistic Imaging and Clinical Traits

Primary: Analysis Methods - Classification and Prediction
Secondary: Neuro - Alzheimer's Disease
652-01-002 · Prognostication and Predictive Value of MR Biomarkers · Thursday, 14 May, 8:30 AM–10:06 AM · Power Pitch Theatre 2
652-01-002 · Prognostication and Predictive Value of MR Biomarkers · Thursday, 14 May, 8:30 AM–10:06 AM · Power Pitch Theatre 2
Keywords: Diagnosis/Prediction Machine Learning/Artificial Intelligence Multimodal Risk Stratification
Accepted
Karl Landheer 1, Benjamin Geraghty1, Joseph Herman1, Joshua Backman1, Manuel A Ferreira1, Goncalo R Abecasis1, Jonathan Marchini1
1Regeneron Genetics Center, LLC, Tarrytown, United States of America
Presenting Author: Karl Landheer

Synopsis

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References

1. Summers, P. et al. Whole-body Magnetic Resonance Imaging: Technique, Guidelines and Key Applications. Ecancermedicalscience 15, 1–16 (2021).
2. Martucci, M. et al. Magnetic Resonance Imaging of Primary Adult Brain Tumors: State of the Art and Future Perspectives. Biomedicines 11, (2023).
3. Rajiah, P. S., François, C. J. & Leiner, T. Cardiac MRI: State of the Art. Radiology 307, 1–15 (2023).
4. Kuhn, T. et al. Translating State-of-the-Art Brain Magnetic Resonance Imaging (MRI) Techniques into Clinical Practice: Multimodal MRI Differentiates Dementia Subtypes in a Traditional Clinical Setting. Quantitative Imaging in Medicine and Surgery 11, 4056–4073 (2021).
5. Littlejohns, T. J. et al. The UK Biobank Imaging Enhancement of 100,000 Participants: Rationale, Data Collection, Management and Future Directions. Nature Communications 2624 (2020).
6. Sudlow, C. et al. UK Biobank: An Open Access Resource for Identifying the Causes of a Wide Range of Complex Diseases of Middle and Old Age. PLoS Medicine 12, e1001779 (2015).
7. Chen, T., & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785–794.
8. Barnwal, A., Cho, H., & Hocking, T. Survival Regression with Accelerated Failure Time Model in XGBoost. Journal of Computational and Graphical Statistics, 31(4), 1292–1302 (2022).
9. Harrell, F. E., Califf, R. M., Pryor, D. B., Lee, K. L., & Rosati, R. A. Evaluating the Yield of Medical Tests. The Journal of the American Medical Association, 247(18), 2543–2546 (1982).
10. Lapuschkin S., Wäldchen S., Binder A., Montavon G., Samek W., Müller K.R. Unmasking Clever Hans Predictors and Assessing what Machines Really Learn. Nature Communications 10(1), 1096 (2019).

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/652-01-002