Evidence map›Paper›PMID 42845254›Full record

ArticleJournal of gastric cancer2026

Prognostic Model for Survival After Upfront R0 Gastrectomy in Peritoneal Oligometastatic Gastric Cancer.

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 and 2 more

Abstract readMulticenter Study
In one paragraph

Article in Journal of gastric cancer, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Seungho LeeDepartment of Surgery, Yonsei University College of Medicine, Seoul, Korea.ORCID https://orcid.org/0000-0001-6958-7581
Yun Ho RohDepartment of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, Korea.ORCID https://orcid.org/0000-0003-0841-7161
Ho-Jung ShinDepartment of Surgery, Ajou University School of Medicine, Suwon, Korea.ORCID https://orcid.org/0000-0001-8938-5012
Jeong Ho SongDepartment of Surgery, Ajou University School of Medicine, Suwon, Korea.ORCID https://orcid.org/0000-0002-2356-7152
Sung Eun KimDepartment of Surgery, Chonnam National University School of Medicine, Gwangju, Korea.ORCID https://orcid.org/0000-0001-7633-4339
In-Seob LeeDepartment of Surgery, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.ORCID https://orcid.org/0000-0003-3099-0140
Han Hong LeeDepartment of Surgery, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea.ORCID https://orcid.org/0000-0002-7541-8490
Oh JeongDepartment of Surgery, Chonnam National University School of Medicine, Gwangju, Korea.ORCID https://orcid.org/0000-0002-7076-6998
Mi Ran JungDepartment of Surgery, Chonnam National University School of Medicine, Gwangju, Korea.ORCID https://orcid.org/0000-0002-4665-972X
Hoon HurDepartment of Surgery, Ajou University School of Medicine, Suwon, Korea.ORCID https://orcid.org/0000-0002-5435-5363
Ye Seob JeeDepartment of Surgery, Dankook University Hospital, Dankook University College of Medicine, Cheonan, Korea. ysjee@dkuh.co.kr.ORCID https://orcid.org/0000-0003-3280-6351
Hyoung-Il KimDepartment of Surgery, Yonsei University College of Medicine, Seoul, Korea. cairus@yuhs.ac.ORCID https://orcid.org/0000-0002-6134-4523

Funding

Korea Health Industry Development Institute RS-2025-02253005Ministry of Health and Welfare RS-2023-KH134639Ministry of Health and Welfare RS-2026-25517509
6 · The paper itself

Abstract

purposePeritoneal 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. MATERIALS AND

methodsThis 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.

resultsThe 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).

conclusionsWe 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.

Indexed as

GastrectomyPeritoneal NeoplasmsStomach NeoplasmsAgedFemaleHumansMachine LearningMaleMiddle AgedPredictive Learning ModelsPrognosisRetrospective StudiesSurvival RateMachine learningPeritoneal oligometastasisUpfront R0 gastrectomy

Identifiers

PMID42845254
PMCPMC13655688

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.