European Radiology

, Volume 29, Issue 2, pp 494–500 | Cite as

Muscle mass estimation on breast magnetic resonance imaging in breast cancer patients: comparison between psoas muscle area on computer tomography and pectoralis muscle area on MRI

  • Federica RossiEmail author
  • Francesca Valdora
  • Emanuele Barabino
  • Massimo Calabrese
  • Alberto Stefano Tagliafico



To evaluate the correlation between psoas muscle area (TPA) on CT images and pectoralis muscle area (PMA) on MRI in breast cancer patients.


This retrospective study was institutional review board approved and women involved gave written informed consent. Twenty six patients with both body CT and breast MRI available were evaluated. Two radiologists calculated TPA on 1.25-mm and 5-mm body CT images. Two radiologists measured PMA on axial T1-weighted images. Statistical analysis included inter- and intra-reader agreement and correlation between TPA on CT and PMA on MRI.


The Pearson r correlation coefficient was 0.70 (95% CI 0.41–0.81) and the coefficient of determination was 0.49. The inter-reader agreement was k = 0.85 and k = 0.79 for axial 1.25-mm and 5-mm CT images, respectively. The intra-reader agreement of reader 1 was k = 0.98 and k = 0.94 for 1.25-mm and 5-mm CT images, respectively. The intra-reader agreement of reader 2 was k = 0.95 and k = 0.94 for 1.25-mm and 5-mm CT images, respectively. On axial T1-weighted images, the inter-reader agreement for radiologists evaluating the PMA was k = 0.61. Intra-observer agreement of reader 1 and reader 2 for PMA estimation was good (0.62 and 0.64), respectively.


The correlation between TPA on CT images and PMA on MRI was very good. Pectoralis muscle area on breast MRI could be useful to estimate muscle mass in women with breast cancer.

Key Points

• Pectoralis muscle area can be estimated on breast MRI

• Total psoas area on CT and pectoralis muscle area on MRI are strongly correlated

• Pectoralis muscle area on breast MRI could estimate the skeletal muscle mass


Magnetic resonance imaging Breast cancer Muscle mass Psoas muscle Pectoralis muscle 



American Cancer Society


Computer tomography


European Society of Breast Imaging


Magnetic resonance imaging


National Comprehensive Cancer Network


Pectoralis muscle area


Total psoas area



The authors state that this work has not received any funding.

Compliance with ethical standards


The scientific guarantor of this publication is Alberto Stefano Tagliafico.

Conflict of interest

The authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article.

Statistics and biometry

One of the authors has significant statistical expertise.

Informed consent

Written informed consent was obtained from all subjects (patients) in this study.

Ethical approval

Institutional review board approval was obtained.


• retrospective

• cross-sectional study

• performed at one institution


  1. 1.
    Baumgartner RN, Koehler KM, Gallagher D et al (1998) Epidemiology of sarcopenia among the elderly in New Mexico. Am J Epidemiol 147:755–763CrossRefGoogle Scholar
  2. 2.
    Muscaritoli M, Anker SD, Argilés J et al (2010) Consensus definition of sarcopenia, cachexia and pre-cachexia: joint document elaborated by special interest groups (SIG) “cachexia-anorexia in chronic wasting diseases” and “nutrition in geriatrics”. Clin Nutr 29:154–159CrossRefGoogle Scholar
  3. 3.
    Torre LA, Bray F, Siegel RL, Ferlay J, Lortet-Tieulent J, Jemal A (2015) Global cancer statistics, 2012. CA Cancer J Clin 65:87–108CrossRefGoogle Scholar
  4. 4.
    Stewart B, Wild CP (2014) World cancer report 2014. World Health Organization, Geneva. Accessed 26 Oct 2017
  5. 5.
    Del Fabbro E, Parsons H, Warneke CL et al (2012) The relationship between body composition and response to neoadjuvant chemotherapy in women with operable breast cancer. Oncologist 17:1240–1245CrossRefGoogle Scholar
  6. 6.
    Prado CM, Baracos VE, McCargar LJ et al (2009) Sarcopenia as a determinant of chemotherapy toxicity and time to tumor progression in metastatic breast cancer patients receiving capecitabine treatment. Clin Cancer Res 15:2920–2926CrossRefGoogle Scholar
  7. 7.
    Deluche E, Leobon S, Desport JC, Venat-Bouvet L, Usseglio J, Tubiana-Mathieu N (2018) Impact of body composition on outcome in patients with early breast cancer. Support Care Cancer 26:861–868CrossRefGoogle Scholar
  8. 8.
    Klassen O, Schmidt ME, Ulrich CM et al (2017) Muscle strength in breast cancer patients receiving different treatment regimes. J Cachexia Sarcopenia Muscle 8:305–316CrossRefGoogle Scholar
  9. 9.
    Villaseñor A, Ballard-Barbash R, Baumgartner K et al (2012) Prevalence and prognostic effect of sarcopenia in breast cancer survivors: the HEAL study. J Cancer Surviv 6:398–406CrossRefGoogle Scholar
  10. 10.
    Shen W, Punyanitya M, Wang Z et al (1985) Total body skeletal muscle and adipose tissue volumes: estimation from a single abdominal cross-sectional image. J Appl Physiol 1985:2333e8Google Scholar
  11. 11.
    Jones KI, Doleman B, Scott S, Lund JN, Williams JP (2015) Simple psoas cross-sectional area measurement is a quick and easy method to assess sarcopenia and predicts major surgical complications. Colorectal Dis 17:20–26CrossRefGoogle Scholar
  12. 12.
    Senkus E, Kyriakides S, Ohno S et al (2015) Primary breast cancer: ESMO clinical practice guidelines for diagnosis, treatment and follow-up. Ann Oncol 5:8–30Google Scholar
  13. 13.
    Gilbert FJ, Selamoglu A (2018) Personalised screening: is this the way forward? Clin Radiol 73:327–333CrossRefGoogle Scholar
  14. 14.
    Kinsey CM, San José Estépar R, van der Velden J, Cole BF, Christiani DC, Washko GR (2017) Lower pectoralis muscle area is associated with a worse overall survival in non-small cell lung cancer. Cancer Epidemiol Biomarkers Prev 26:38–43CrossRefGoogle Scholar
  15. 15.
    Go SI, Park MJ, Song HN et al (2017) A comparison of pectoralis versus lumbar skeletal muscle indices for defining sarcopenia in diffuse large B-cell lymphoma - two are better than one. Oncotarget 8:47007–47019PubMedPubMedCentralGoogle Scholar
  16. 16.
    Bland JM, Altman DG (2003) Applying the right statistics: analyses of measurement studies. Ultrasound Obstet Gynecol 22:85–93CrossRefGoogle Scholar
  17. 17.
    Landis JR, Koch GG (1977) The measurement of observer agreement for categorical data. Biometrics 33:159–174CrossRefGoogle Scholar
  18. 18.
    Bland JM, Altman DG (1996) Measurement error proportional to the mean. BMJ 313:106CrossRefGoogle Scholar
  19. 19.
    Bland JM, Altman DG (1996) Measurement error. BMJ 313:744CrossRefGoogle Scholar
  20. 20.
    Furtner J, Berghoff AS, Albtoush OM et al (2017) Survival prediction using temporal muscle thickness measurements on cranial magnetic resonance images in patients with newly diagnosed brain metastases. Eur Radiol 27:3167–3173CrossRefGoogle Scholar
  21. 21.
    Rier HN, Jager A, Sleijfer S, van Rosmalen J, Kock MCJM, Levin MD (2017) Low muscle attenuation is a prognostic factor for survival in metastatic breast cancer patients treated with first line palliative chemotherapy. Breast 31:9–15CrossRefGoogle Scholar
  22. 22.
    Mann RM, Balleyguier C, Baltzer PA et al (2015) Breast MRI: EUSOBI recommendations for women’s information. Eur Radiol 25:3669–3678CrossRefGoogle Scholar
  23. 23.
    Strigel RM, Rollenhagen J, Burnside ES et al (2017) Screening breast MRI outcomes in routine clinical practice: comparison to BI-RADS benchmarks. Acad Radiol 24:411–417CrossRefGoogle Scholar
  24. 24.
    Saslow D, Boetes C, Burke W et al (2007) American Cancer Society guidelines for breast screening with MRI as an adjunct to mammography. CA Cancer J Clin 57:75–89Google Scholar
  25. 25.
    Warner E, Messersmith H, Causer P, Eisen A, Shumak R, Plewes D (2008) Systematic review: using magnetic resonance imaging to screen women at high risk for breast cancer. Ann Intern Med 148:671–679CrossRefGoogle Scholar
  26. 26.
    Sardanelli F, Podo F, Santoro F et al (2011) High Breast Cancer Risk Italian 1 (HIBCRIT-1) Study (2011) Multicenter surveillance of women at high genetic breast cancer risk using mammography, ultrasonography, and contrast-enhanced magnetic resonance imaging (the high breast cancer risk Italian 1 study): final results. Invest Radiol 46:94–105CrossRefGoogle Scholar
  27. 27.
    Lehman CD, Lee JM, DeMartini WB et al (2016) Screening MRI in women with a personal history of breast cancer. J Natl Cancer Inst.
  28. 28.
    Boutin RD, Kaptuch JM, Bateni CP, Chalfant JS, Yao L (2016) Influence of IV contrast administration on CT measures of muscle and bone attenuation: implications for sarcopenia and osteoporosis evaluation. AJR Am J Roentgenol 207:1046–1054CrossRefGoogle Scholar

Copyright information

© European Society of Radiology 2018

Authors and Affiliations

  1. 1.Department of Health SciencesDISSAL- University of GenovaGenoaItaly
  2. 2.Ospedale Policlinico San MartinoGenoaItaly

Personalised recommendations