論文

国際誌
2019年10月

Pre-treatment psoas major volume is a predictor of poor prognosis for patients with epithelial ovarian cancer.

Molecular and clinical oncology
  • Yuko Matsubara
  • ,
  • Keiichiro Nakamura
  • ,
  • Hirofumi Matsuoka
  • ,
  • Chikako Ogawa
  • ,
  • Hisashi Masuyama

11
4
開始ページ
376
終了ページ
382
記述言語
英語
掲載種別
研究論文(学術雑誌)
DOI
10.3892/mco.2019.1912
出版者・発行元
SPANDIDOS PUBL LTD

Low skeletal muscle mass (sarcopenia) is an important prognostic risk factor for the outcome of a variety of cancer types. The current study investigated whether skeletal muscle area (SMA), psoas area (PA) and psoas major volume (PV) are associated with progression-free survival (PFS) and overall survival (OS) in patients with epithelial ovarian cancer (OC). A total of 92 OC patients were enrolled in the present study. Pre-treatment with SMA and PA was assessed using computed tomography (CT) and PV was calculated using a three-dimensional-CT (3D-CT). The clinical factors associated with sarcopenia and prognosis were retrospectively evaluated. For all patients, the median PFS and OS were 19 and 32 months, respectively. Patients exhibiting lower PV (<195.6 cm3) had significantly poorer PFS and OS compared with patients exhibiting higher PV (≥195.6 cm3; P=0.018 and P=0.006), while those with low SMA (<92.92 cm2) had significantly worse OS than patients with higher SMA (≥92.92 cm2; P=0.030). PV was also demonstrated to be superior to SMA and PA in prognosis prediction. PV by 3D-CT can serve as an indicator of poor prognosis in patients with OC.

リンク情報
DOI
https://doi.org/10.3892/mco.2019.1912
PubMed
https://www.ncbi.nlm.nih.gov/pubmed/31497297
PubMed Central
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6719253
Web of Science
https://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=JSTA_CEL&SrcApp=J_Gate_JST&DestLinkType=FullRecord&KeyUT=WOS:000496290500008&DestApp=WOS_CPL
ID情報
  • DOI : 10.3892/mco.2019.1912
  • ISSN : 2049-9450
  • eISSN : 2049-9469
  • PubMed ID : 31497297
  • PubMed Central 記事ID : PMC6719253
  • Web of Science ID : WOS:000496290500008

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