ArticleSurgical endoscopy2025
Predicting the prognosis of radical gastrectomy for patients with locally advanced gastric cancer after neoadjuvant chemotherapy using machine learning technology: a multicenter study in China.
Article in Surgical endoscopy, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.
What it found
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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.
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.
Who cites it
1 citing paper in PubMed, 1 synthesis or guideline pooled it.
- Global hotspots and trends in the application of neoadjuvant therapy for gastric cancer: a bibliometric analysis.Frontiers in oncology · 2026Pooled it
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
16 authors.
Funding
Abstract
backgroundNeoadjuvant chemotherapy (NAC) can improve the prognosis of patients with locally advanced gastric cancer (LAGC). However, precise models for accurate prognostic predictions are lacking. We aimed to utilize Cox regression and integrate various machine learning (ML) algorithms to identify and prioritize key factors influencing LAGC overall survival to establish an efficient prognostic prediction model.
methodsData from 385 patients with LAGC who underwent NAC followed by radical gastrectomy at two centers between January 2016 and December 2020 were analyzed (internal training set, n = 167; internal validation set, n = 112; external validation set, n = 106). The internal cohort was randomly divided into training and validation sets in a 6:4 ratio.
resultsThe support vector machine (SVM) model was identified as the best predictive model (AUC values: internal training set, 0.93; internal validation set, 0.74; external validation set, 0.74), outperforming the ypTNM staging system (AUC values: internal training set, 0.9330 vs. 0.7170; internal validation set, 0.7440 vs. 0.6700; external validation set, 0.7403 vs. 0.6960, respectively). In the internal cohort, patients in the HRG (High Risk Group) had significantly lower mean overall survival compared with patients in the LRG (Low Risk Group) (47.33 vs. 64.97 months, respectively; log-rank P = 0.006) and a higher recurrence rate (48.0% vs. 35.6%, respectively; P = 0.041).
conclusionsThe SVM model predicted postoperative survival and recurrence patterns in patients with LAGC post-NAC, and can address the limitations of the ypTNM staging system through providing more targeted decision-making for individualized treatment.
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Registered trials
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.