Evidence map›Paper›PMID 41491826›Full record

ArticleWorld journal of surgical oncology2026

Development and validation of a novel preoperative computed tomography staging model integrating Immune, Inflammatory, and nutritional biomarkers for prognostic prediction in gastric adenocarcinoma patients undergoing radical resection: a multicenter study.

Xiaolong Gu, Chaoyang Zhang, Panying Zhang, Guobin Wu, Yang Meng, Chongfei Ma, Yang Li, Zhidong Zhang

Abstract readMulticenter StudyValidation Study
In one paragraph

Article in World journal of surgical oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Xiaolong GuDepartment of Radiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, 050011, China.
Chaoyang ZhangThe Third Department of Surgery, The Fourth Hospital of Hebei Medical University, No. 12 Jiankang Rd. Changan District, Shijiazhuang, Hebei, 050011, China.
Panying ZhangThe Department of Urology of Hebei General Hospital, Shijiazhuang, Hebei, 050051, China.
Guobin WuThe Fifth Department of Surgery of Shijiazhuang People's Hospital, Shijiazhuang, Hebei, 050011, China.
Yang MengThe Fifth Department of Surgery of Shijiazhuang People's Hospital, Shijiazhuang, Hebei, 050011, China.
Chongfei MaDepartment of Radiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, 050011, China.
Yang LiDepartment of Radiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, 050011, China.
Zhidong ZhangThe Third Department of Surgery, The Fourth Hospital of Hebei Medical University, No. 12 Jiankang Rd. Changan District, Shijiazhuang, Hebei, 050011, China. zhang_zhi_dong@hebmu.edu.cn.

Funding

medical science research project of Hebei province 20221352S&T Program of Hebei 22377702D
6 · The paper itself

Abstract

backgroundPatients undergoing radical gastrectomy demonstrate considerable variability in their prognoses, underscoring the urgent need for reliable biomarkers to inform personalized therapeutic strategies. This study seeks to develop and validate a novel prognostic model by combining preoperative immune, inflammatory, and nutritional biomarkers with computed tomography (CT) imaging features, thereby facilitating the prediction of outcomes in patients with gastric cancer who have undergone radical resection and aiding in the formulation of personalized clinical treatment strategies.

methodsThis retrospective study analyzed consecutive patients with a preoperative diagnosis of gastric cancer who underwent radical gastrectomy at two participating centers between January 2015 and December 2016. Based on predefined inclusion and exclusion criteria, eligible patients were randomly allocated to either a training cohort, which was used for model development and internal estimation of parameters, or a validation cohort, which served for independent testing of the model’s predictive performance. We assessed a range of preoperative hematological parameters and CT imaging features. Factors associated with overall survival (OS) were identified using least absolute shrinkage and selection operator (LASSO) regression analysis, and a prognostic model was subsequently constructed.

resultsA total of 393 patients were enrolled in the study and randomly allocated to the training and validation cohorts in a 7:3 ratio. The final prognostic model incorporated eight hematological indicators: white blood cell (WBC) count, hemoglobin (HB), total protein (TP), creatinine (Cr), neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), prognostic nutritional index (PNI), and systemic immune-inflammatory index (SII), in addition to CT staging characteristics. Time-dependent receiver operating characteristic (ROC) analysis of the risk scores yielded areas under the curve (AUCs) of 0.715, 0.740, and 0.736 for the training set, and 0.597, 0.631, and 0.657 for the validation set at 1, 3, and 5 years, respectively. High-risk patients had a substantially worse overall survival rate than low-risk patients, according to Kaplan-Meier analysis.

conclusionThe immune, inflammatory, and nutritional-CT (IIN-CT) model, which integrates preoperative immune, inflammatory, and nutritional biomarkers with CT imaging features, significantly improves the accuracy of preoperative prognostic predictions in gastric cancer patients.

Indexed as

AdenocarcinomaBiomarkers, TumorGastrectomyInflammationStomach NeoplasmsTomography, X-Ray ComputedAgedFemaleFollow-Up StudiesHumansMaleMiddle AgedNeoplasm StagingNutritional StatusPreoperative CarePrognosisBiomarkers, TumorBiomarkersCT imagingGastric adenocarcinomaImmune-inflammation-nutrition indexPreoperative evaluationPrognostic model

Identifiers

PMID41491826
PMCPMC12870278

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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.