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Prognostic impact of skeletal muscle volume derived from cross-sectional computed tomography images in breast cancer

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Breast Cancer Research and Treatment Aims and scope Submit manuscript

A Letter to the Editor to this article was published on 25 April 2019

Abstract

Purpose

This study aimed to determine whether the prognosis of breast cancer is affected by muscle or fat volume as measured from computed tomography (CT) images.

Methods

We identified 1460 patients with chest CT who were diagnosed as having breast cancer at the National Cancer Center, Korea, between January 2001 and December 2009. Using CT images of 10-mm slices, we measured the cross-sectional areas of skeletal muscle and adipose tissue at the 3rd lumbar vertebrae, and derived their volumes. The skeletal muscle volume, fat volume, and muscle-to-fat ratio were evaluated for association with overall survival (OS) and recurrence-free survival (RFS).

Results

The median skeletal muscle and fat volumes among the patients were 93.3 cc (range 39.6–236.9) and 420.1 cc (range 19.5–1392.3), respectively. Patients with higher muscle volume had better prognosis than those with lower muscle volume [hazard ratio (HR) 0.56, 95% confidence interval (CI) 0.34–0.92, P = 0.022 for OS; HR 0.72, 95% CI 0.52–0.99, P = 0.046 for RFS]. However, body mass index (BMI) and fat volume were not associated with prognosis. In addition, muscle volume was a significant prognosticator for OS, regardless of BMI (HR 0.55, 95% CI 0.32–0.93, P = 0.034 in BMI < 25.0; HR 0.44, 95% CI 0.21–0.91, P = 0.026 in BMI ≥ 25.0). Among older patients (≥ 50), those with higher muscle volume showed better OS and RFS (HR 0.44, 95% CI 0.23–0.85, P = 0.015; HR 0.55, 95% CI 0.34–0.90, P = 0.017, respectively).

Conclusion

This study demonstrated that breast cancer patients with higher skeletal muscle volume showed more favorable prognosis.

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Funding

This work was funded by the National Cancer Center Grant Nos. 1532200.

Author information

Author notes

  1. Eun Jin Song, Chan Wha Lee and So-Youn Jung have contributed equally to the study. Eun Jin Song and So-Youn Jung are co-first author and Chan Wha Lee is co-corresponding author.

Authors and Affiliations

  1. Center for Breast Cancer, Research Institute and Hospital, National Cancer Center, 323 Ilsan-ro, Ilsandong-gu, Goyang, 10408, Republic of Korea

    Eun Jin Song, Chan Wha Lee, So-Youn Jung, Byeong Nam Kim, Keun Seok Lee, Seeyoun Lee, Han-Sung Kang, In Hae Park, Yun Ju Kim, Kyungran Ko & Eun Sook Lee

  2. Department of Surgery, Seonam Hospital, Seoul, Republic of Korea

    Eun Jin Song

  3. Department of Surgery, Keimyung University School of Medicine, Daegu, Republic of Korea

    Moo Hyun Lee

  4. Biometric Research Branch, Research Institute, National Cancer Center, Goyang, Republic of Korea

    Sohee Kim & Byung-Ho Nam

Authors
  1. Eun Jin Song
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  2. Chan Wha Lee
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  14. Eun Sook Lee
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Corresponding authors

Correspondence to Chan Wha Lee or Eun Sook Lee.

Ethics declarations

Conflict of interest

Eun Jin Song has received research grant from the National Cancer Center. All authors except Eun Jin Song declare that they no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals. The study protocol was approved by the institutional review board of the National Cancer Center (IRB No.: NCC2015-0006).

Electronic supplementary material

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Supplement figure 1

. Distribution of muscle volume and fat volume (TIF 134 KB)

Supplement figure 2

. Biological subgroup analysis. A forest plot showing the hazard ratios and 95% confidence intervals associated with the variables considered in the subgroup analysis for overall survival (left) and for recurrence-free survival (right). With respect to muscle volume, the triple-negative subgroup (ER and PR and Her2 Negative) was especially associated with the statistically significant hazard ratio of overall survival and recurrence-free survival (TIF 173 KB)

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Song, E.J., Lee, C.W., Jung, SY. et al. Prognostic impact of skeletal muscle volume derived from cross-sectional computed tomography images in breast cancer. Breast Cancer Res Treat 172, 425–436 (2018). https://doi.org/10.1007/s10549-018-4915-7

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  • DOI: https://doi.org/10.1007/s10549-018-4915-7

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