Formula for the Cross-Sectional Area of the Muscles of the Third Lumbar Vertebra Level from the Twelfth Thoracic Vertebra Level Slice on Computed Tomography.
Yuria IshidaKeisuke MaedaYosuke YamanakaRemi MatsuyamaRyoko KatoMakoto YamaguchiTomoyuki NonogakiAkio ShimizuJunko UeshimaKenta MurotaniNaoharu MoriPublished in: Geriatrics (Basel, Switzerland) (2020)
The purpose of this study was to investigate a means by which to reflect muscle mass using chest computed tomography (CT). A cross-sectional study was conducted with patients aged ≥ 65 years having abdominal and chest CT scans. The formula to predict third lumbar vertebra (L3) cross-sectional area (CSA) of the muscles from the erector muscles of the spine at the twelfth thoracic vertebra (Th12) level slice on CT was created using the five-fold cross-validation method. Correlation between predicted L3 CSA and measured L3 CSA of the muscles was assessed by intraclass correlation coefficients (ICC) and correlation coefficients (r) in the data of the development, and predictability was examined with accuracy and F-values in the validation study. The development study included 161 patients. The developed formula was as follows: -1006.38 + 16.29 × age + 1161.80 × sex (if female, 0; if male, 1) + 55.91 × body weight + 2.22 × CSA of the erector muscles at Th12. The formula demonstrated strong concordance and correlation (ICC = 0.849 [0.800-0.887] and r = 0.858 [0.811-0.894]). The validation study included 34 patients. The accuracy and F-value between predicted CSA and measured CSA were high (accuracy = 0.889-0.944, F-value = 0.931-0.968). We developed a formula predicting CSA at L3 using Th12 CT slice. This formula could be used to assess decreased muscle mass even with chest CT alone.
Keyphrases
- computed tomography
- image quality
- dual energy
- end stage renal disease
- contrast enhanced
- positron emission tomography
- ejection fraction
- cross sectional
- newly diagnosed
- magnetic resonance imaging
- prognostic factors
- human milk
- magnetic resonance
- pain management
- physical activity
- spinal cord injury
- deep learning
- preterm infants
- preterm birth
- low birth weight