Postoperative Changes in Body Composition Predict Long-Term Prognosis in Patients with Gastric Cancer

Each of postoperative skeletal muscle loss, body fat loss, and muscle hyperdensity negatively affected prognosis of GC patients after surgery, and the BC risk scoring assessment well predicted the prognosis of postoperative patients with GC.

Authors

Institutions

Publication Details

Journal
Cancers
Published
2025-02-21
DOI
https://doi.org/10.3390/cancers17050738
Citations
6
Primary Topic
Nutrition and Health in Aging
Type
article
Field-Weighted Citation Impact
6.99

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Postoperative Changes in Body Composition Predict Long-Term Prognosis in Patients with Gastric Cancer

Mikito Mori, Chihiro Kosugi, Kazuo Narushima, Akihiro Usui et al.
6 citations
Cancers
Nutrition and Health in Aging
6.99
article

Postoperative Changes in Body Composition Predict Long-Term Prognosis in Patients with Gastric Cancer

Mikito Mori, Chihiro Kosugi, Kazuo Narushima, Akihiro Usui, Yoshihiro Nabeya, Hiroyuki Nojima, Kiyohiko Shuto, Masato Yamazaki, Keiji Koda, Hiroaki Shimizu
article en
6 citations

Abstract

Each of postoperative skeletal muscle loss, body fat loss, and muscle hyperdensity negatively affected prognosis of GC patients after surgery, and the BC risk scoring assessment well predicted the prognosis of postoperative patients with GC.

CancersVol. 17(5)
Chiba Cancer Center (JP), Teikyo University Chiba Medical Center (JP)
Japan Society for the Promotion of Science
Openalex Percentile: Top 3%
Nutrition and Health in Aging
6.99
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.