ArticleAbdominal radiology (New York)2025
Spectral CT radiomics features of the tumor and perigastric adipose tissue can predict lymph node metastasis in gastric cancer.
Article in Abdominal radiology (New York), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 7 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Precision or Paradox? AI-Driven Adiposity Imaging in Women With Overweight and Obesity: A Systematic Review and Meta-Analysis.Obesity reviews : an official journal of the International Association for the Study of Obesity · 2026Pooled it
- Preoperative CT-based radiomics of suprapancreatic adipose tissue for predicting high-difficulty lymph node dissection in gastric cancer.Abdominal radiology (New York) · 2026Article
- Research progress of the clinical application of dual-layer spectral computed tomography in gastrointestinal malignancies.World journal of radiology · 2026Review
- Dual-layer spectral detector computed tomography multiparameter machine learning model for prediction of lymph node metastases in esophageal squamous cell carcinoma.Cancer imaging : the official publication of the International Cancer Imaging Society · 2026Article
- Machine learning model based on dual-layer detector spectral CT radiomics features for differentiating luminal and non-luminal breast cancer.Frontiers in oncology · 2026Article
- A CT-based radiomics nomogram incorporating adipose tissue to differentiate invasive adenocarcinomas among part-solid pulmonary nodules.BMC cancer · 2025Article
- Computed tomography 3D reconstruction and texture analysis for evaluating the efficacy of neoadjuvant chemotherapy in advanced gastric cancer.World journal of gastrointestinal surgery · 2025Article
Corrections and comments
- Erratum issued
Authors and funding
8 authors.
Funding
Abstract
objectivesTo develop a nomogram based on the radiomics features of tumour and perigastric adipose tissue adjacent to the tumor in dual-layer spectral detector computed tomography (DLCT) for lymph node metastasis (LNM) prediction in gastric cancer (GC).
methodsA retrospective analysis was conducted on 175 patients with gastric adenocarcinoma. They were divided into training cohort (n = 125) and validation cohort (n = 50). The radiomics features from the tumour and perigastric fat based on DLCT spectral images were extracted to construct radiomics models for LNM prediction using Lasso-GLM method. Preoperative clinicopathological features, DLCT routine parameters, and the optimal radiomics models were analyzed to establish the clinical-DLCT model, clinical-DLCT-radiomics model and a nomogram. All models were internally validated using the Bootstrap method and evaluated using receiver operating characteristic (ROC) curve.
resultsThe area under the ROC curve (AUC) values of optimal radiomics models based on tumour (Model 1) and perigastric fat (Model 2) were 0.923 and 0.822 in training cohort, 0.821 and 0.767 in validation cohort. The clinical-DLCT model based on Nct and ECV
conclusionsThe nomogram based on Nct, ECV
Indexed as
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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 Socratic graph.