ArticleBMC gastroenterology2026
CT semantic features of primary gastric cancer: a preoperative predictive model for peritoneal metastasis with superior efficacy to conventional direct CT assessment.
Article in BMC gastroenterology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
objectivesThis study aims to evaluate the utility of CT semantic features of primary Gastric Cancer(GC) compared to conventional CT assessments for predicting Peritoneal Metastasis(PM) , and to construct a preoperative predictive model.
methodsWe conducted a retrospective analysis involving 257 pathologically confirmed GC patients (92 with PM and 165 without PM), utilizing preoperative contrast-enhanced abdominal CT alongside clinicopathological data. Univariate and multivariate logistic regression analyses were performed to identify risk factors for PM, leading to the development of three predictive models: one based on primary tumor CT signs, another on peritoneal CT signs, and a combined model integrating both sets of features.
resultsIndependent PM predictors included the primary tumor's maximum size, serosal invasion, thickness, enhancement, and the presence of ascites. The primary tumor model demonstrated superior performance (AUC=0.920) compared to the peritoneal model (AUC=0.822, p<0.001). No significant difference was observed between the primary tumor model and the combined model (AUC=0.936, p=0.178). The combined model exhibited the highest sensitivity at 75.0%, while all models maintained a specificity of 98.2%.
conclusionsCT semantic features of primary GC and ascites are effective in predicting PM. The primary tumor-based model surpasses conventional CT in performance, and the combined model further enhances sensitivity. This methodology improves preoperative PM assessment, may help reduce the incidence of non-therapeutic surgeries, and contributes positively to the prognosis of GC patients.
Indexed as
Identifiers
What OpenQuestion holds
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.