Radiomic Analysis of Lumbar Spine Clinical QCT Scans for Vertebral Fracture Risk Assessment: Investigating First-Order Features and Predictive Power (#205)
Nicolai R Krekiehn
1
,
Eren B Yilmaz
1
2
,
Niklas C Koser
1
,
Oluwabukunmi M Akinloye
3
,
Sandra Freitag-Wolf
3
,
Eric S Orwoll
4
,
Claus-C. Glüer
1
- i²Lab@Section Biomedical Imaging, Dept. of Radiology and Neuroradiology, University Hospital Schleswig-Holstein (UKSH), Campus Kiel, Kiel, Schleswig-Holstein, Germany
- Dept. of Computer Science, Ostfalia University of Applied Sciences, Wolfenbüttel, Germany
- Institute of Medical Informatics and Statistics, Kiel University, University Hospital Schleswig-Holstein (UKSH), Campus Kiel, Kiel, Schleswig-Holstein, Germany
- Division of Endocrinology, Diabetes and Clinical Nutrition, School of Medicine, Oregon Health & Science University, Portland, Oregon, USA
Publish consent withheld
- Krekiehn, N.R., Yilmaz, E.B., Kruse, H.C., Meyer, C., Glüer, C.C. (2023). Automated Deep-learning-based Vertebral Body Localization and Instance Segmentation for Osteoporosis Assessment using CT. In: Deserno, T.M., Handels, H., Maier, A., Maier-Hein, K., Palm, C., Tolxdorff, T. (eds) Bildverarbeitung für die Medizin 2023. BVM 2023. Informatik aktuell. Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-658-41657-7_37
- Joost J.M. van Griethuysen, Andriy Fedorov, Chintan Parmar, Ahmed Hosny, Nicole Aucoin, Vivek Narayan, Regina G.H. Beets-Tan, Jean-Christophe Fillion-Robin, Steve Pieper, Hugo J.W.L. Aerts; Computational Radiomics System to Decode the Radiographic Phenotype. Cancer Res 1 November 2017; 77 (21): e104–e107. https://doi.org/10.1158/0008-5472.CAN-17-0339