ArticleAbdominal radiology (New York)2024
Combination of clinical and spectral-CT iodine concentration for predicting liver metastasis in gastric cancer: a preliminary study.
Article in Abdominal radiology (New York), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Whole-volume histogram analysis of spectral-computed tomography iodine maps characterizes HER2 expression in gastric cancer.World journal of gastroenterology · 2024Trial
- Imaging characteristics and discrimination model development for early gastric cancer using multi-spectral CT.Surgical endoscopy · 2026Article
- A nomogram based on dual-layer spectral detector CT-derived 40-KeV virtual monoenergetic images and iodine maps for preoperative prediction of regional lymph node metastasis in colorectal cancer.BMC medical imaging · 2026Article
- Combining radiomics and deep learning to predict liver metastasis of gastric cancer on CT image.Frontiers in oncology · 2025Article
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Authors and funding
7 authors.
Funding
Abstract
purposeThis study aimed to determine the diagnostic efficacy of various indicators and models for the prediction of gastric cancer with liver metastasis.
methodsClinical and spectral computed tomography (CT) data from 80 patients with gastric adenocarcinoma who underwent surgical resection were retrospectively analyzed. Patients were divided into metastatic and non-metastatic groups based on whether or not to occur liver metastasis, and the region of interest (ROI) was measured manually on each phase iodine map at the largest level of the tumor. Iodine concentration (IC), normalized iodine concentration (nIC), and clinical data of the primary gastric lesions were analyzed. Logistic regression analysis was used to construct the clinical indicator (CI) and clinical indicator-spectral CT iodine concentration (CI-Spectral CT-IC) Models, which contained all of the parameters with statistically significant differences between the groups. Receiver operating characteristic (ROC) curves were constructed to evaluate the accuracy of the models.
resultsThe metastatic group showed significantly higher levels of Cancer antigen125 (CA125), carcinoembryonic antigen (CEA), IC, and nIC in the arterial phase, venous phase, and delayed phase than the non-metastatic group (all p < 0.05). Normalized iodine concentration Venous Phase (nICVP) exhibited a favorable performance among all IC and nIC parameters for forecasting gastric cancer with liver metastasis (area under the curve (AUC), 0.846). The combination model of clinical data with significant differences and nICVP showed the best diagnostic accuracy for predicting liver metastasis from gastric cancer, with an AUC of 0.897.
conclusionnICVP showed the best diagnostic efficacy for predicting gastric cancer with liver metastasis. Clinical Indicators-normalized ICVP model can improve the prediction accuracy for this condition.
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