Congress:
ECR25
Poster Number:
C-20759
Type:
Poster: EPOS Radiologist (scientific)
Authorblock:
S-W. Lee, M. H. Choi, L. Youngjoon; Seoul/KR
Disclosures:
Sheen-Woo Lee:
Nothing to disclose
Moon Hyung Choi:
Grant Recipient: Siemens Healthineers
Lee Youngjoon:
Nothing to disclose
Keywords:
Abdomen, CT, Segmentation, Tissue characterisation
- Tolonen A, Pakarinen T, Sassi A, Kytta J, Cancino W, Rinta-Kiikka I, et al. Methodology, clinical applications, and future directions of body composition analysis using computed tomography (CT) images: A review. Eur J Radiol 2021;145:109943. doi: 10.1016/j.ejrad.2021.109943
- Petrelli F, Cortellini A, Indini A, Tomasello G, Ghidini M, Nigro O, et al. Association of Obesity With Survival Outcomes in Patients With Cancer: A Systematic Review and Meta-analysis. JAMA Netw Open 2021;4:e213520. doi: 10.1001/jamanetworkopen.2021.3520
- Gibson DJ, Burden ST, Strauss BJ, Todd C, Lal S. The role of computed tomography in evaluating body composition and the influence of reduced muscle mass on clinical outcome in abdominal malignancy: a systematic review. Eur J Clin Nutr 2015;69:1079-1086. doi: 10.1038/ejcn.2015.32
- Pooler BD, Garrett JW, Lee MH, Rush BE, Kuchnia AJ, Summers RM, et al. CT-Based Body Composition Measures and Systemic Disease: A Population-Level Analysis Using Artificial Intelligence Tools in Over 100,000 Patients. AJR Am J Roentgenol 2025. doi: 10.2214/AJR.24.32216
- Bates DDB, Pickhardt PJ. CT-Derived Body Composition Assessment as a Prognostic Tool in Oncologic Patients: From Opportunistic Research to Artificial Intelligence-Based Clinical Implementation. AJR Am J Roentgenol 2022;219:671-680. doi: 10.2214/AJR.22.27749
- Lee JH, Choi SH, Jung KJ, Goo JM, Yoon SH. High visceral fat attenuation and long-term mortality in a health check-up population. J Cachexia Sarcopenia Muscle 2023;14:1495-1507. doi: 10.1002/jcsm.13226
- Kim SH, Jeong JB, Kang J, Ahn DW, Kim JW, Kim BG, et al. Association between sarcopenia level and metabolic syndrome. PLoS One 2021;16:e0248856. doi: 10.1371/journal.pone.0248856
- Aleixo GFP, Shachar SS, Nyrop KA, Muss HB, Malpica L, Williams GR. Myosteatosis and prognosis in cancer: Systematic review and meta-analysis. Crit Rev Oncol Hematol 2020;145:102839. doi: 10.1016/j.critrevonc.2019.102839
- Vedder IR, Levolger S, Dierckx R, Viddeleer AR, Bokkers RPH. Effect of contrast phase on quantitative analysis of skeletal muscle and adipose tissue by computed tomography. Nutrition 2024;126:112492. doi: 10.1016/j.nut.2024.112492
- Perez AA, Pickhardt PJ, Elton DC, Sandfort V, Summers RM. Fully automated CT imaging biomarkers of bone, muscle, and fat: correcting for the effect of intravenous contrast. Abdom Radiol (NY) 2021;46:1229-1235. doi: 10.1007/s00261-020-02755-5
- Lortie J, Gage G, Rush B, Heymsfield SB, Szczykutowicz TP, Kuchnia AJ. The effect of computed tomography parameters on sarcopenia and myosteatosis assessment: a scoping review. J Cachexia Sarcopenia Muscle 2022;13:2807-2819. doi: 10.1002/jcsm.13068
- Pigneur F, Luciani A, Ghosn M, Reizine E, Morel A, Stehle T. Skeletal Muscle Density Is Highly Dependent on CT Instrumentation. Radiology 2023;307:e222839. doi: 10.1148/radiol.222839
- Kim DW, Kim KW, Ko Y, Park T, Lee J, Lee JB, et al. Effects of Contrast Phases on Automated Measurements of Muscle Quantity and Quality Using CT. Korean J Radiol 2021;22:1909-1917. doi: 10.3348/kjr.2021.0105
- Nachit M, Horsmans Y, Summers RM, Leclercq IA, Pickhardt PJ. AI-based CT Body Composition Identifies Myosteatosis as Key Mortality Predictor in Asymptomatic Adults. Radiology 2023;307:e222008. doi: 10.1148/radiol.222008
- Pickhardt PJ, Summers RM, Garrett JW. Automated CT-Based Body Composition Analysis: A Golden Opportunity. Korean J Radiol 2021;22:1934-1937. doi: 10.3348/kjr.2021.0775
- Park HJ, Shin Y, Park J, Kim H, Lee IS, Seo DW, et al. Development and Validation of a Deep Learning System for Segmentation of Abdominal Muscle and Fat on Computed Tomography. Korean J Radiol 2020;21:88-100. doi: 10.3348/kjr.2019.0470
- Weston AD, Korfiatis P, Kline TL, Philbrick KA, Kostandy P, Sakinis T, et al. Automated Abdominal Segmentation of CT Scans for Body Composition Analysis Using Deep Learning. Radiology 2019;290:669-679. doi: 10.1148/radiol.2018181432
- Bedrikovetski S, Seow W, Kroon HM, Traeger L, Moore JW, Sammour T. Artificial intelligence for body composition and sarcopenia evaluation on computed tomography: A systematic review and meta-analysis. Eur J Radiol 2022;149:110218. doi: 10.1016/j.ejrad.2022.110218
- Koitka S, Kroll L, Malamutmann E, Oezcelik A, Nensa F. Fully automated body composition analysis in routine CT imaging using 3D semantic segmentation convolutional neural networks. Eur Radiol 2021;31:1795-1804. doi: 10.1007/s00330-020-07147-3
- Mai DVC, Drami I, Pring ET, Gould LE, Lung P, Popuri K, et al. A systematic review of automated segmentation of 3D computed-tomography scans for volumetric body composition analysis. J Cachexia Sarcopenia Muscle 2023;14:1973-1986. doi: 10.1002/jcsm.13310
- Park J, Joo I, Jeon SK, Kim JM, Park SJ, Yoon SH. Automated abdominal organ segmentation algorithms for non-enhanced CT for volumetry and 3D radiomics analysis. Abdom Radiol (NY) 2024. doi: 10.1007/s00261-024-04581-5
- Starekova J, Hernando D, Pickhardt PJ, Reeder SB. Quantification of Liver Fat Content with CT and MRI: State of the Art. Radiology 2021;301:250-262. doi: 10.1148/radiol.2021204288
- Hu N, Yan G, Tang M, Wu Y, Song F, Xia X, et al. CT-based methods for assessment of metabolic dysfunction associated with fatty liver disease. Eur Radiol Exp 2023;7:72. doi: 10.1186/s41747-023-00387-0
- Kwon JH, Lee SS, Yoon JS, Suk HI, Sung YS, Kim HS, et al. Liver-to-Spleen Volume Ratio Automatically Measured on CT Predicts Decompensation in Patients with B Viral Compensated Cirrhosis. Korean J Radiol 2021;22:1985-1995. doi: 10.3348/kjr.2021.0348
- Romero-Cristobal M, Clemente-Sanchez A, Ramon E, Tellez L, Canales E, Ortega-Lobete O, et al. CT-derived liver and spleen volume accurately diagnose clinically significant portal hypertension in patients with hepatocellular carcinoma. JHEP Rep 2023;5:100645. doi: 10.1016/j.jhepr.2022.100645
- Javadi S, Elsherif S, Bhosale P, Jensen CT, Layman RR, Jacobsen MC, et al. Quantitative attenuation accuracy of virtual non-enhanced imaging compared to that of true non-enhanced imaging on dual-source dual-energy CT. Abdom Radiol (NY) 2020;45:1100-1109. doi: 10.1007/s00261-020-02415-8
- Choi MH, Lee YJ, Choi YJ, Pak S. Dual-energy CT of the liver: True noncontrast vs. virtual noncontrast images derived from multiple phases for the diagnosis of fatty liver. Eur J Radiol 2021;140:109741. doi: 10.1016/j.ejrad.2021.109741
- Kim S, Kang BS, Kwon WJ, Bang M, Lim S, Park GM, et al. Abdominal Organs Attenuation Values and Abdominal Aortic Calcifications on Virtual and True Noncontrast Images Obtained With Third-Generation Dual-Source Dual-Energy Computed Tomography. J Comput Assist Tomogr 2020;44:490-500. doi: 10.1097/RCT.0000000000001057
- Kang HJ, Lee DH, Park SJ, Han JK. Virtual noncontrast images derived from dual-energy CT for assessment of hepatic steatosis in living liver donors. Eur J Radiol 2021;139:109687. doi: 10.1016/j.ejrad.2021.109687
- Catania R, Jia L, Haghshomar M, Miller FH, Borhani AA. Detection of moderate hepatic steatosis on contrast-enhanced dual-source dual-energy CT: Role and accuracy of virtual non-contrast CT. Eur J Radiol 2024;172:111328. doi: 10.1016/j.ejrad.2024.111328
- Lee YS, Hong N, Witanto JN, Choi YR, Park J, Decazes P, et al. Deep neural network for automatic volumetric segmentation of whole-body CT images for body composition assessment. Clin Nutr 2021;40:5038-5046. doi: 10.1016/j.clnu.2021.06.025
- Kim EH, Kim KW, Shin Y, Lee J, Ko Y, Kim YJ, et al. Reference Data and T-Scores of Lumbar Skeletal Muscle Area and Its Skeletal Muscle Indices Measured by CT Scan in a Healthy Korean Population. J Gerontol A Biol Sci Med Sci 2021;76:265-271. doi: 10.1093/gerona/glaa065
- Rollins KE, Javanmard-Emamghissi H, Awwad A, Macdonald IA, Fearon KCH, Lobo DN. Body composition measurement using computed tomography: Does the phase of the scan matter? Nutrition 2017;41:37-44. doi: 10.1016/j.nut.2017.02.011
- Liang H, Du S, Yan G, Zhou Y, Yang T, Zhang Z, et al. Dual-energy CT of the pancreas: comparison between virtual non-contrast images and true non-contrast images in the detection of pancreatic lesion. Abdom Radiol (NY) 2023;48:2596-2603. doi: 10.1007/s00261-023-03914-0
- Bae JS, Lee DH, Joo I, Jeon SK, Han JK. Utilization of virtual non-contrast images derived from dual-energy CT in evaluation of biliary stone disease: Virtual non-contrast image can replace true non-contrast image regarding biliary stone detection. Eur J Radiol 2019;116:34-40. doi: 10.1016/j.ejrad.2019.04.008
- Laukamp KR, Kessner R, Halliburton S, Zopfs D, Gupta A, Grosse Hokamp N. Virtual Noncontrast Images From Portal Venous Phase Spectral-Detector CT Acquisitions for Adrenal Lesion Characterization. J Comput Assist Tomogr 2021;45:24-28. doi: 10.1097/RCT.0000000000000982
- Graser A, Johnson TR, Hecht EM, Becker CR, Leidecker C, Staehler M, et al. Dual-energy CT in patients suspected of having renal masses: can virtual nonenhanced images replace true nonenhanced images? Radiology 2009;252:433-440. doi: 10.1148/radiol.2522080557
- Slebocki K, Kraus B, Chang DH, Hellmich M, Maintz D, Bangard C. Incidental Findings in Abdominal Dual-Energy Computed Tomography: Correlation Between True Noncontrast and Virtual Noncontrast Images Considering Renal and Liver Cysts and Adrenal Masses. J Comput Assist Tomogr 2017;41:294-297. doi: 10.1097/RCT.0000000000000503