Authors
Seungho Lee, Yun Ho Roh, Ho-Jung Shin, Jeong Ho Song, Sung Eun Kim, In-Seob Lee, Han Hong Lee, Oh Jeong, Mi Ran Jung, Hoon Hur, Ye Seob Jee, Hyoung-Il Kim
Published in
Journal of gastric cancer. Volume 26. Issue 4. Pages 516-533.
Abstract
Peritoneal metastasis from gastric cancer (GC) is generally considered incurable. Although conversion surgery is the preferred strategy, surgeons frequently encounter clinical scenarios requiring upfront surgery. Currently, objective criteria for predicting prognosis after upfront R0 gastrectomy remain undefined.
This retrospective multicenter cohort study analyzed data from 792 patients derived from the PASS-META cohort and an independent validation dataset collected between 2014 and 2021. The final analysis included 80 patients with peritoneal oligometastasis (P1/P2) who underwent upfront R0 gastrectomy for risk stratification, and 632 controls who underwent R2 gastrectomy or no gastrectomy for survival comparison. A component-wise gradient-boosting survival model was developed and simplified into an 8-item risk scoring system.
The machine learning (ML) model demonstrated robust discrimination in external validation (concordance index, 0.811; 95% confidence interval, 0.677-0.955) and stratified patients into low- and high-risk groups with significantly different survival outcomes (log-rank P<0.001), independent of systemic chemotherapy compliance. Low-risk patients achieved a 5-year survival rate exceeding 30%, whereas high-risk patients showed survival rates comparable to those of patients who underwent R2 gastrectomy or no gastrectomy. The simplified 8-item risk groups showed high concordance with the ML model risk groups (F1-score, 0.975).
We developed and validated a prediction model and an 8-item risk scoring system that effectively stratified patients with peritoneal oligometastatic GC according to their prognosis after upfront R0 gastrectomy. Although prospective validation is warranted, this framework provides objective prognostic guidance that may support intraoperative decision-making, distinguishing patients with favorable long-term outcomes from those whose survival is comparable to that of noncurative approaches.
PMID:
42845254
Bibliographic data and abstract were imported from PubMed on 08 Oct 2026.
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