151751-Najiba-Chargi

43 Diagnostics: validation of skeletal muscle mass measurement 15. B. Heymsfield S, Wang Z, Baumgartner RN, Ross R. Human Body Composition: Advanc - es in Models and Methods. Annu Rev Nutr . 1997;17(1):527-558. doi:10.1146/annurev. nutr.17.1.527 16. Prado CMM, Heymsfield SB. Lean Tissue Im - aging. J Parenter Enter Nutr . 2014;38(8):940- 953. doi:10.1177/0148607114550189 17. Bol GH, Kotte ANTJ, van der Heide UA, La - gendijk JJW. Simultaneous multi-modality ROI delineation in clinical practice. Comput Methods Programs Biomed . 2009;96(2):133- 140. doi:10.1016/j.cmpb.2009.04.008 18. Koo TK, Li MY. A Guideline of Selecting and Reporting Intraclass Correlation Coeffi - cients for Reliability Research. J Chiropr Med . 2016;15(2):155-163. doi:10.1016/j. jcm.2016.02.012 19. Landis JR, Koch GG. The Measurement of Observer Agreement for Categorical Data. Biometrics . 1977. doi:10.2307/2529310 20. Bril SI, Wendrich AW, Swartz JE, et al. In - terobserver agreement of skeletal muscle mass measurement on head and neck CT imaging at the level of the third cervical vertebra. Eur Arch Oto-Rhino-Laryngology . 2019;276(4):1175-1182. doi:10.1007/s00405- 019-05307-w 21. Baracos VE. Psoas as a sentinel muscle for sarcopenia: a flawed premise. J Cachex- ia Sarcopenia Muscle . 2017. doi:10.1002/ jcsm.12221 22. Weston AD, Korfiatis P, Kline TL, et al. Au - tomated Abdominal Segmentation of CT Scans for Body Composition Analysis Using Deep Learning. Radiology . 2019;290(3):669- 679. doi:10.1148/radiol.2018181432 23. Koitka S, Kroll L, Malamutmann E, Oezcelik A, Nensa F. Fully-automated body compo - sition analysis in routine ct imaging using 3d semantic segmentation convolutional neural networks. arXiv . 2020. 24. Blanc-Durand P, Campedel L, Mule S, et al. Prognostic value of anthropometric measures extracted from whole-body CT using deep learning in patients with non-small-cell lung cancer. Eur Radiol . 2020;30(6):3528-3537. doi:10.1007/s00330- 019-06630-w 25. Schweitzer L, Geisler C, Pourhassan M, et al. What is the best reference site for a single MRI slice to assess whole body skel- etal muscle and adipose tissue volumes in healthy adults? Am J Clin Nutr . 2015. doi:10.3945/ajcn.115.111203 26. van Vugt JLA, van Putten Y, van der Kall IM, et al. Estimated skeletal muscle mass and density values measured on comput- ed tomography examinations in over 1000 living kidney donors. Eur J Clin Nutr . 2019. doi:10.1038/s41430-018-0287-7 27. Van Der Werf A, Langius JAE, De Van Der Schueren MAE, et al. Percentiles for skeletal muscle index, area and radiation attenua- tion based on computed tomography imag- ing in a healthy Caucasian population. Eur J Clin Nutr . 2018;72(2):288-296. doi:10.1038/ s41430-017-0034-5 28. Zwart AT, Becker JN, Lamers MJ, et al. Skeletal muscle mass and sarcopenia can be determined with 1.5-T and 3-T neck MRI scans, in the event that no neck CT scan is performed. Eur Radiol . 2020. doi:10.1007/ s00330-020-07440-1 29. Chargi N, Ansari E, Huiskamp LFJ, Bol G, de Bree R. Agreement between skeletal muscle mass measurements using computed to- mography imaging andmagnetic resonance imaging in head and neck cancer patients. Oral Oncol . 2019;99:104341. doi:10.1016/j. oraloncology.2019.06.022 30. Baum U, Greess H, Lell M, Nömayr A, Lenz M. Imaging of head and neck tumors - Meth - ods: CT, spiral-CT, multislice-spiral-CT. Eur J Radiol . 2000. doi:10.1016/S0720- 048X(99)00139-4 2