Evidence map›Paper›PMID 41142613›Full record

ArticleFrontiers in oncology2025

Preoperative prognostic assessment using intratumoral and peritumoral adipose tissue radiomics derived from contrast-enhanced CT in cT3-4 gastric cancer.

Chao Lu, Donggang Pan, Xiuhong Shan, Rao Dai, Bowen Liu, Zhixuan Wang, Xiaoxiao Wang

Abstract read
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Article in Frontiers in oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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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

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2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Chao LuDepartment of Medical Imaging, Jiangsu University Affiliated People's Hospital (Zhenjiang First People's Hospital), Zhenjiang, Jiangsu, China.
Donggang PanDepartment of Medical Imaging, Jiangsu University Affiliated People's Hospital (Zhenjiang First People's Hospital), Zhenjiang, Jiangsu, China.
Xiuhong ShanDepartment of Medical Imaging, Jiangsu University Affiliated People's Hospital (Zhenjiang First People's Hospital), Zhenjiang, Jiangsu, China.
Rao DaiDepartment of Medical Imaging, Jiangsu University Affiliated People's Hospital (Zhenjiang First People's Hospital), Zhenjiang, Jiangsu, China.
Bowen LiuDepartment of Medical Imaging, Jiangsu University Affiliated People's Hospital (Zhenjiang First People's Hospital), Zhenjiang, Jiangsu, China.
Zhixuan WangDepartment of Medical Imaging, Jiangsu University Affiliated People's Hospital (Zhenjiang First People's Hospital), Zhenjiang, Jiangsu, China.
Xiaoxiao WangDepartment of Medical Imaging, Jiangsu University Affiliated People's Hospital (Zhenjiang First People's Hospital), Zhenjiang, Jiangsu, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: Exploring the value of contrast-enhanced computed tomography (CECT) based radiomics features from intratumoral and peritumoral adipose tissue (PAT) in predicting early recurrence (ER)after gastrectomy in patients with cT3-4 gastric cancer (GC). Materials and methods: This retrospective study involved patients with cT3-4 GC who underwent preoperative CECT. The radiomics features of tumor and PAT were separately extracted from the CT venous phase images using the Pyradiomics package. The radiomic score (radscore) was computed for each patient by integrating LASSO regression-selected radiomic features, weighted according to their respective coefficients. The GC location, longest diameter, maximum thickness, cT stage and cN stage determined by preoperative CT were also evaluated. Univariate and bivariate analyses using the Cox regression model were performed to evaluate factors affecting ER. The Kaplan-Meier method was used for the analysis of ER-free survival. Results: A total of 184 consecutive cT3-4 GC patients were enrolled in this study. Bivariate Cox regression analyses demonstrated that radscore and cT stage emerged as independent predictors of ER in all parameters. Radscore-based stratification showed a marked difference in the ER rates between high-risk patients (radscore ≥ -0.66) and low-risk patients (65.9% vs. 3.2%; log-rank p<0.001). Similarly, cT4 stage patients had markedly higher ER rates than cT3 stage patients (53.5% vs. 22.1%; log-rank p<0.001). Conclusion: The integrated radscore combining intratumoral and PAT features emerged as an independent prognostic predictor for ER in cT3-4 GC, offering quantitative biomarkers to optimize neoadjuvant therapy selection and postoperative surveillance intensity.

Indexed as

contrast-enhanced computed tomographygastric cancerperitumoral adipose tissueprognosisradiomics

Identifiers

PMID41142613
PMCPMC12545016

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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.