566-06-010 ISMRM Abstract

Automated Quadriceps Segmentation and Torque Prediction from Multi-Vendor MRI: Cross-Sectional Area vs. Radiomics

Primary: Musculoskeletal - Muscle

Secondary: Analysis Methods - Segmentation and Detection

566-06-010 · Quantitative Outcome Measures in MSK MRI · Wednesday, 13 May, 4:55 PM–5:50 PM · Digital Posters Row G

Keywords: Diagnosis/Prediction Radiomics Muscle Force Biomarker estimation Deep Learning Segmentation

Accepted

Ozkan Cigdem^1,2, Eros Montin^1,2, Salim Bin Ghouth^1,2, Akshay Chaudhari^3,4, Donnie Cameron^5,6, Luigi Ferrucci⁶, Garry E Gold³, Cem M Deniz^1,2, Valentina Mazzoli^1,2

¹Department of Radiology, Center for Advanced Imaging Innovation and Research (CAI²R), New York University Grossman School of Medicine, New York, United States of America

²Department of Radiology, Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, New York, United States of America

³Department of Radiology, Stanford University, Stanford, United States of America

⁴Biomedical Data Science, Stanford University, Stanford, United States of America

⁵Department of Medical Imaging, Radboud University Medical Center, Nijmegen, Netherlands

⁶Translational Gerontology Branch, Intramural Research Program, National Institute on Aging, National Institutes of Health, Maryland, United States of America

Presenting Author: Meeghage Randika Perera

Synopsis

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References

1. A. J. Cruz-Jentoft, A. A. Sayer, Sarcopenia, The Lancet 393 (10191) (2019) 2636–2646. doi:https://doi.org/10.1016/S0140-6736(19)31138-9. URL https://www.sciencedirect.com/science/article/pii/S0140673619311389 [doi]

2. A. B. de Lima, D. Henrinques-Neto, D. Scott, A. de Araújo Pinto, G. dos Santos Ribeiro, M. Peralta, K. A. Miranda, P. Campos, E. R. Gouveia, Relationship between physical function and sarcopenia in the older adults from amazonas: A cross-sectional study, PLOS ONE 20 (3) (2025) e0320079. doi:10.1371/journal. pone.0320079. URL https://doi.org/10.1371/journal.pone.0320079 [doi]

3. A. A. Sayer, R. Cooper, H. Arai, P. M. Cawthon, M.-J. N. Essomba, R. A. Fielding, M. D. Grounds, M. D. Witham, A. J. Cruz-Jentoft, Sarcopenia, Nature Reviews Disease Primers 10 (1) (2024) 68. doi:10.1038/s41572-024-00550-w URL https://doi.org/10.1038/s41572-024-00550-w [doi]

4. V. Mazzoli, Y. Vainberg, M. Hall, M. Barbieri, J. Asay, J. Muccini, J. Rosenberg, F. Kogan, S. Delp, G. Gold, Improved strength prediction combining mri biomarkers of muscle quantity and quality, NMR in Biomedicine 38 (9) (2025) e70112, e70112 NBM-25-0417. arXiv:https: //analyticalsciencejournals.onlinelibrary.wiley.com/doi/pdf/10.1002/nbm.70112,doi:https://doi.org/10.1002/nbm.7011. URL https://analyticalsciencejournals.onlinelibrary.wiley.com/doi/abs/10.1002/nbm.70112 [doi]

5. M. Codari, M. Zanardo, M. E. di Sabato, E. Nocerino, C. Messina, L. M. Sconfienza, F. Sardanelli, MRI-derived biomarkers related to sarcopenia: A systematic review, Journal of Magnetic Resonance Imaging 51 (4) (2020) 1117–1127, epub 2019 Sep 13. doi:10.1002/jmri.26931. URL https://doi.org/10.1002/jmri.26931 [doi]

6. F. Isensee, P. F. Jaeger, S. A. A. Kohl, J. Petersen, K. H. Maier-Hein, nnu-net: a self-configuring method for deep learning-based biomedical image segmentation, Nature Methods 18 (2) (2021) 203–211. doi:10.1038/s41592-020-01008-z. URL https://doi.org/10.1038/s41592-020-01008-z [doi]

7. J. J. M. van Griethuysen, A. Fedorov, C. Parmar, A. Hosny, N. Aucoin, V. Narayan, R. G. H. Beets-Tan, J.-C. Fillon-Robin, S. Pieper, H. J. W. L. Aerts, Computational radiomics system to decode the radiographic phenotype, Cancer Research 77 (21) (2017) e104–e107. doi:10.1158/0008-5472.CAN-17-0339. [doi]

8. O. Cigdem, E. Montin, S. Chen, C. Zhang, K. Cho, R. Kijowski, R. Lattanzi, C. Deniz, Predicting time-to-total knee replacement: Deep learning vs radiomics, in: Proceedings of the 2025 ISMRM & ISMRT Annual Meeting & Exhibition, Honolulu, Hawaii, 2025, abstract #2879.

9. R. Tibshirani, Regression shrinkage and selection via the lasso, Journal of the Royal Statistical Society: Series B (Methodological) 58 (1) (1996) 267–288.

10. T. Chen, C. Guestrin, XGBoost, in: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, New York, NY, USA, 2016.

11. T. Akiba, S. Sano, T. Yanase, T. Ohta, M. Koyama, Optuna: A next-generation hyperparameter optimization framework (2019). arXiv:1907.10902.

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http://echo.ismrm.org/p/ISMRM2026/566-06-010