Journal of Korean Biological Nursing Science

Korean society of Biological Nursing Science (한국기초간호학회)
권호 표지
DOI QR코드

DOI QR Code

Muscle Radiation Attenuation in the Erector Spinae and Multifidus Muscles as a Determinant of Survival in Patients with Gastric Cancer

  • An, Soomin (College of Nursing Science, Kyung Hee University) ;
  • Kim, Youn-Jung (College of Nursing Science, Kyung Hee University) ;
  • Han, Ga Young (Department of Music, Chang Shin University) ;
  • Eo, Wankyu (Department of Internal Medicine, College of Medicine, Kyung Hee University)
  • Received : 2022.02.03
  • Accepted : 2022.02.25
  • Published : 2022.02.28
Download PDF
⟨ Previous Next ⟩

Abstract

Purpose: To determine the prognostic role of muscle area and muscle radiation attenuation in the erector spinae (ES) and multifidus (MF) muscles in patients undergoing gastrectomy. Methods: Patients with stage I-III gastric cancer undergoing gastrectomy were retrospectively enrolled in this study. Clinicopathologic characteristics were collected and analyzed. Both paraspinal muscle index of ES/MF muscles (PMIEM) and paraspinal muscle radiation attenuation in the same muscles (PMRAEM) were analyzed at the 3rd lumbar level using axial computed tomographic images. Cox regression analysis was applied to estimate overall survival (OS) and disease-free survival (DFS). Results: There was only a weak correlation between PMIEM and PMRAEM (r= 0.28). Multivariate Cox regression revealed that PMRAEM, but not PMIEM, was an important determinant of survival. PMRAEM along with age, tumor-node-metastasis (TNM) stage, perineural invasion, and serum albumin level were significant determinants of both OS and DFS that constituted Model 1. Harrell's concordance index and integrated area under receiver operating characteristic curve were greater for Model 1 than for Model 2 (consisting of the same covariates as Model 1 except PMRAEM) or Model 3 (consisting of only TNM stage). Conclusion: PMRAEM, but not PMIEM, was an important determinant of survival. Because there was only a weak correlation between PMIEM and PMRAEM in this study, it was presumed that they were mutually exclusive. Model 1 consisting of age, TNM stage, perineural invasion, serum albumin level, and PMRAEM was greater than nested models (i.e., Model 2 or Model 3) in predicting survival outcomes.

Keywords

References

  1. Balachandran VP, Gonen M, Smith JJ, DeMatteo RP. Nomograms in oncology: more than meets the eye. The Lancet Oncology. 2015;16(4):e173-80. https://doi.org/10.1016/s1470-2045(14)71116-7
  2. Onate-Ocana LF, Aiello-Crocifoglio V, Gallardo-Rincon D, Herrera-Goepfert R, Brom-Valladares R, Carrillo JF, et al. Serum albumin as a significant prognostic factor for patients with gastric carcinoma. Annals of Surgical Oncology. 2007;14(2):381-389. https://doi.org/10.1245/s10434-006-9093-x
  3. Kuwada K, Kuroda S, Kikuchi S, Yoshida R, Nishizaki M, Kagawa S, et al. Sarcopenia and comorbidity in gastric cancer surgery as a useful combined factor to predict eventual death from other causes. Annals of Surgical Oncology. 2018;25(5):1160-1166. https://doi.org/10.1245/s10434-018-6354-4
  4. Lenchik L, Boutin RD. Sarcopenia: Beyond muscle atrophy and into the new frontiers of opportunistic imaging, precision medicine, and machine learning. Seminars in Musculoskeletal Radiology. 2018;22(3):307-322. https://doi.org/10.1055/s-0038-1641573
  5. Amini B, Boyle SP, Boutin RD, Lenchik L. Approaches to assessment of muscle mass and myosteatosis on computed tomography: a systematic review. The Journals of Gerontology, Series A: Biological Sciences. 2019;74(10):1671-1678. https://doi.org/10.1093/gerona/glz034
  6. Kazemi-Bajestani SM, Mazurak VC, Baracos V. Computed tomography-defined muscle and fat wasting are associated with cancer clinical outcomes. Seminars in Cell & Developmental Biology. 2016;54:2-10. https://doi.org/10.1016/j.semcdb.2015.09.001
  7. Shachar SS, Williams GR, Muss HB, Nishijima TF. Prognostic value of sarcopenia in adults with solid tumours: a meta-analysis and systematic review. European Journal of Cancer. 2016;57:58-67. https://doi.org/10.1016/j.ejca.2015.12.030
  8. Kuwada K, Kuroda S, Kikuchi S, Yoshida R, Nishizaki M, Kagawa S, et al. Clinical impact of sarcopenia on gastric cancer. Anticancer Research. 2019;39(5):2241-2249. https://doi.org/10.21873/anticanres.13340
  9. Hacker UT, Hasenclever D, Linder N, Stocker G, Chung HC, Kang YK, et al. Prognostic role of body composition parameters in gastric/gastroesophageal junction cancer patients from the EXPAND trial. Journal of Cachexia, Sarcopenia and Muscle. 2020;11(1):135-144. https://doi.org/10.1002/jcsm.12484
  10. Ramirez-Velez R, Ezzatvar Y, Izquierdo M, Garcia-Hermoso A. Effect of exercise on myosteatosis in adults: a systematic review and meta-analysis. Journal of Applied Physiology. 2021;130(1):245-255. https://doi.org/10.1152/japplphysiol.00738.2020
  11. Aubrey J, Esfandiari N, Baracos VE, Buteau FA, Frenette J, Putman CT, et al. Measurement of skeletal muscle radiation attenuation and basis of its biological variation. Acta physiologica. 2014;210(3):489-497. https://doi.org/10.1111/apha.12224
  12. Wronska A, Kmiec Z. Structural and biochemical characteristics of various white adipose tissue depots. Acta physiologica. 2012;205(2):194-208. https://doi.org/10.1111/j.1748-1716.2012.02409.x
  13. Wang Y, Tian G, Chen S, Li N. Myosteatosis reduces overall survival in patients with digestive system malignancies: a meta-analysis with trial sequential analysis. Nutrition Research. 2021;94:25-33. https://doi.org/10.1016/j.nutres.2021.08.003
  14. Aleixo GFP, Shachar SS, Nyrop KA, Muss HB, Malpica L, Williams GR. Myosteatosis and prognosis in cancer: systematic review and meta-analysis. Critical Reviews in Oncology/Hematology. 2020;145:102839. https://doi.org/10.1016/j.critrevonc.2019.102839
  15. Dohzono S, Sasaoka R, Takamatsu K, Hoshino M, Nakamura H. Prognostic value of paravertebral muscle density in patients with spinal metastases from gastrointestinal cancer. Supportive Care in Cancer. 2019;27(4):1207-1213. https://doi.org/10.1007/s00520-018-4465-x
  16. Eo W, Kwon J, An S, Lee S, Kim S, Nam D, et al. Clinical significance of paraspinal muscle parameters as a prognostic factor for survival in gastric cancer patients who underwent curative surgical Resectio. Journal of Cancer. 2020;11(19):5792-5801. https://doi.org/10.7150/jca.46637
  17. An S, Eo W, Kim YJ. Muscle-related parameters as determinants of survival in patients with stage I-III gastric cancer undergoing gastrectomy. Journal of Cancer. 2021;12(18):5664-5673. https://doi.org/10.7150/jca.61199
  18. Agten A, Stevens S, Verbrugghe J, Eijnde BO, Timmermans A, Vandenabeele F. The lumbar multifidus is characterised by larger type I muscle fibres compared to the erector spinae. Anatomy & Cell Biology. 2020;53(2):143-150. https://doi.org/10.5115/acb.20.009
  19. Lauren P. The two histological main types of gastric carcinoma: diffuse and so-called intestinal-type carcinoma: an attempt at a histo-clinical classification. Acta Pathologica et Microbiologica Scandinavica. 1965;64:31-49. https://doi.org/10.1111/apm.1965.64.1.31
  20. Santiago JM, Sasako M, Osorio J. TNM-7th edition 2009 (UICC/AJCC) and Japanese classification 2010 in gastric cancer. Towards simplicity and standardisation in the management of gastric cancer. Cirugia Espanola (English Edition). 2011;89(5):275-281. https://doi.org/10.1016/j.ciresp.2010.10.011
  21. Tsekoura M, Tsepis E, Billis E, Gliatis J. Sarcopenia in patients with chronic obstructive pulmonary disease: A study of prevalence and associated factors in Western Greek population. Lung India. 2020;37(6):479-484. https://doi.org/10.4103/lungindia.lungindia_143_20
  22. Gomez-Perez SL, Haus JM, Sheean P, Patel B, Mar W, Chaudhry V, et al. Measuring abdominal circumference and skeletal muscle from a single cross-sectional computed tomography image: a step-by-step guide for clinicians using National Institutes of Health Image. Journal of Parenteral and Enteral Nutrition. 2016;40(3):308-318. https://doi.org/10.1177/0148607115604149
  23. Pencina MJ, D'Agostino RB. Overall C as a measure of discrimination in survival analysis: model specific population value and confidence interval estimation. Statistics in Medicine. 2004;23(13):2109-2123. https://doi.org/10.1002/sim.1802
  24. Chen Z, Lin RM, Bai YK, Zhang Y. Establishment and verification of prognostic nomograms for patients with gastrointestinal stromal tumors: a SEER-based study. BioMed Research International. 2019;2019:8293261. https://doi.org/10.1155/2019/8293261
  25. Taaffe DR, Henwood TR, Nalls MA, Walker DG, Lang TF, Harris TB. Alterations in muscle attenuation following detraining and retraining in resistance-trained older adults. Gerontology. 2009;55(2):217-223. https://doi.org/10.1159/000182084
  26. Bansal A, Heagerty PJ. A tutorial on evaluating the time-varying discrimination accuracy of survival models used in dynamic decision making. Medical Decision Making. 2018;38(8):904-916. https://doi.org/10.1177/0272989x18801312
  27. Bansal A, Heagerty PJ. A comparison of landmark methods and time-dependent ROC methods to evaluate the time-varying performance of prognostic markers for survival outcomes. Diagnostic and Prognostic Research. 2019;3(1):1-13. https://doi.org/10.1186/s41512-019-0057-6