ArticleInsights into imaging2023
Preoperative prediction of Lauren classification in gastric cancer: a radiomics model based on dual-energy CT iodine map.
Article in Insights into imaging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Trends and hot spots in research on the prognostic value of gastric cancer biomarkers in the context of the Lauren classification: a bibliometric analysis.Frontiers in medicine · 2025Pooled it
- Dual-layer spectral detector computed tomography of peritumoral fat for preoperative prediction of tumor budding in colorectal adenocarcinoma.Diagnostic and interventional radiology (Ankara, Turkey) · 2026Article
- Lauren classification-based combined model integrating clinicopathological and spectral CT features for disease-free survival prediction in gastric cancer.Cancer imaging : the official publication of the International Cancer Imaging Society · 2026Article
- The added value of intratumoral and peritumoral radiomic scores based on iodine density map for preoperative prediction of lymphovascular invasion in patients with gastric adenocarcinoma: a two-center study.Abdominal radiology (New York) · 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
- GXYLT2 serves as a prognostic biomarker and is associated with β-catenin activation and gastric cancer aggressiveness.Genes & diseases · 2026Article
- Multi-DECT image-based radiomics with interpretable machine learning for preoperative prediction of tumor budding grade and prognosis in colorectal cancer: a dual-center study.Abdominal radiology (New York) · 2026Article
- Radiomics analysis of dual-energy CT-derived iodine maps for predicting histopathologic grading of pancreatic ductal adenocarcinoma: a two-center study.Frontiers in medicine · 2026Article
- Research progress on the application of dual energy CT in gastric diseases.Frontiers in medicine · 2026Review
- CT-based deep learning radiomics analysis for preoperative Lauren classification in gastric cancer and explore the tumor microenvironment.European journal of radiology open · 2025Article
- Article
- Spectral CT-based nomogram for preoperative prediction of Lauren classification in locally advanced gastric cancer: a prospective study.European radiology · 2025Article
- ChatGPT as an effective tool for quality evaluation of radiomics research.European radiology · 2025Article
- Preoperative prediction of the Lauren classification in gastric cancer using automated nnU-Net and radiomics: a multicenter study.Insights into imaging · 2025Article
- Radiomics analysis of dual-layer spectral-detector CT-derived iodine maps for predicting tumor deposits in colorectal cancer.European radiology · 2025Article
- Prediction of Ki-67 expression in gastric gastrointestinal stromal tumors using histogram analysis of monochromatic and iodine images derived from spectral CT.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Article
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8 authors.
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Abstract
objectiveTo investigate the value of a radiomics model based on dual-energy computed tomography (DECT) venous-phase iodine map (IM) and 120 kVp equivalent mixed images (MIX) in predicting the Lauren classification of gastric cancer.
methodsA retrospective analysis of 240 patients undergoing preoperative DECT and postoperative pathologically confirmed gastric cancer was done. Training sets (n = 168) and testing sets (n = 72) were randomly assigned with a ratio of 7:3. Patients are divided into intestinal and non-intestinal groups. Traditional features were analyzed by two radiologists, using logistic regression to determine independent predictors for building clinical models. Using the Radiomics software, radiomics features were extracted from the IM and MIX images. ICC and Boruta algorithm were used for dimensionality reduction, and a random forest algorithm was applied to construct the radiomics model. ROC and DCA were used to evaluate the model performance.
resultsGender and maximum tumor thickness were independent predictors of Lauren classification and were used to build a clinical model. Separately establish IM-radiomics (R-IM), mixed radiomics (R-MIX), and combined IM + MIX image radiomics (R-COMB) models. In the training set, each radiomics model performed better than the clinical model, and the R-COMB model showed the best prediction performance (AUC: 0.855). In the testing set also, the R-COMB model had better prediction performance than the clinical model (AUC: 0.802).
conclusionThe R-COMB radiomics model based on DECT-IM and 120 kVp equivalent MIX images can effectively be used for preoperative noninvasive prediction of the Lauren classification of gastric cancer. CRITICAL RELEVANCE STATEMENT: The radiomics model based on dual-energy CT can be used for Lauren classification prediction of preoperative gastric cancer and help clinicians formulate individualized treatment plans and assess prognosis.
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