CT-defined low-skeletal muscle mass as a prognostic marker for survival in prostate cancer: A systematic review and meta-analysis

11Citations
Citations of this article
13Readers
Mendeley users who have this article in their library.
Get full text

Abstract

Background: Low-skeletal muscle mass (LSMM) is defined as progressive skeletal muscle loss, which can be assessed by imaging modalities. It was shown that this parameter is predictive and prognostic of several clinically relevant factors in several tumor entities. Our aim was to establish the effect of LSMM on overall survival (OS) in prostate cancer patients based on a large patient sample. Methods: MEDLINE library, EMBASE and SCOPUS databases were screened for the associations between LSMM and mortality in prostate cancer patients up to April 2021. The primary endpoint of the systematic review was the hazard ratio of LSMM on OS. In total, five studies were suitable for the analysis and included into the present study. Results: The included studies comprised over all 1221 patients. The identified frequency of LSMM was 61.02%. The pooled hazard ratio for the effect of LSMM on OS was 1.4 [95% CI 0.7-2.5] in univariate analysis and was 1.6 [95% CI 1.2-2.1] in multivariate analysis. Conclusion: CT-defined LSMM has a frequency of 61% in patients with PC and shows a positive association with the overall survival with a hazard ratio of 1.4. LSMM assessment should therefore be included into clinical routine as a relevant prognostic biomarker.

Figures

References Powered by Scopus

32920Citations
4683Readers

This article is free to access.

This article is free to access.

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Cite

CITATION STYLE

APA

Meyer, H. J., Wienke, A., & Surov, A. (2022). CT-defined low-skeletal muscle mass as a prognostic marker for survival in prostate cancer: A systematic review and meta-analysis. Urologic Oncology: Seminars and Original Investigations, 40(3), 103.e9-103.e16. https://doi.org/10.1016/j.urolonc.2021.08.009

Readers over time

‘21‘22‘23‘2402468

Readers' Seniority

Tooltip

Professor / Associate Prof. 2

50%

PhD / Post grad / Masters / Doc 2

50%

Readers' Discipline

Tooltip

Medicine and Dentistry 5

71%

Agricultural and Biological Sciences 1

14%

Nursing and Health Professions 1

14%

Article Metrics

Tooltip
Mentions
News Mentions: 1

Save time finding and organizing research with Mendeley

Sign up for free
0