Predictive Effectiveness of the Glasgow Prognostic Score for Gastrointestinal Stromal Tumors.


Journal

Nutrition and cancer
ISSN: 1532-7914
Titre abrégé: Nutr Cancer
Pays: United States
ID NLM: 7905040

Informations de publication

Date de publication:
2021
Historique:
pubmed: 5 8 2020
medline: 9 9 2021
entrez: 5 8 2020
Statut: ppublish

Résumé

The aim of this study was to evaluate the significance of the Glasgow prognostic score (GPS) in patients with resected gastrointestinal stromal tumors (GISTs). Forty-six GIST patients who underwent radical resection between January 2004 and December 2011 were enrolled in this retrospective study. The clinicopathological parameters examined included predictors of recurrence-free survival (RFS). Univariate and multivariate analysis of prognostic factors related to RFS were calculated using Cox proportional hazards model. The GPS classification system revealed 37 (80.4%), 6 (13.1%), and 3 (6.5%) patients with a GPS of 0, 1, and 2, respectively. Patients with GPS 1/2 had a significantly shorter RFS compared to those with GPS 0 (

Identifiants

pubmed: 32748650
doi: 10.1080/01635581.2020.1800763
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

1333-1339

Auteurs

Tsunehiko Maruyama (T)

Department of Surgery, Mito Saiseikai General Hospital, Mito, Japan.
Department of Gastroenterological Surgery, Ibaraki Medical Center, Tokyo Medical University, Tokyo, Japan.

Mitsugi Shimoda (M)

Department of Gastroenterological Surgery, Ibaraki Medical Center, Tokyo Medical University, Tokyo, Japan.

Akihiro Sako (A)

Department of Surgery, Hitachi General Hospital, Hitachi, Japan.

Kazumitsu Ueda (K)

Department of Surgery, Hitachi General Hospital, Hitachi, Japan.

Hiroyuki Hakoda (H)

Department of Surgery, Hitachi General Hospital, Hitachi, Japan.

Akiko Sakata (A)

Department of Pathology, Hitachi General Hospital, Hitachi, Japan.

Shuji Suzuki (S)

Department of Gastroenterological Surgery, Ibaraki Medical Center, Tokyo Medical University, Tokyo, Japan.

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Classifications MeSH