ArticleEuropean radiology2017
Application of CT texture analysis in predicting histopathological characteristics of gastric cancers.
Article in European radiology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 79 papers.
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Who cites it
79 citing papers in PubMed.
- Intratumoral and peritumoral radiomics analysis for preoperative Lauren classification in gastric cancer.Cancer imaging : the official publication of the International Cancer Imaging Society · 2020Trial
- Correlation of texture analysis of paraspinal musculature on MRI with different clinical endpoints: Lumbar Stenosis Outcome Study (LSOS).European radiology · 2019Trial
- Development and validation of an interpretable CT-based scoring model for gastric cancer aggressiveness.Abdominal radiology (New York) · 2026Article
- CT semantic features of primary gastric cancer: a preoperative predictive model for peritoneal metastasis with superior efficacy to conventional direct CT assessment.BMC gastroenterology · 2026Article
- A novel multimodal combining radiomics and tumor-stroma ratio (TSR) improves diagnosis of gastric cancer peritoneal metastasis.Clinical & experimental metastasis · 2026Article
- CT-based deep learning radiomics analysis for preoperative Lauren classification in gastric cancer and explore the tumor microenvironment.European journal of radiology open · 2025Article
- A CT-Based Deep Learning Radiomics Nomogram for Early Recurrence Prediction in Pancreatic Cancer: A Multicenter Study.Annals of surgical oncology · 2025Article
- Dual-phase contrast-enhanced CT-based intratumoral and peritumoral radiomics for preoperative prediction of lymph node metastasis in gastric cancer.BMC gastroenterology · 2025Article
- Dual-phase contrast-enhanced CT-based intratumoral and peritumoral radiomics for preoperative prediction of lymphovascular invasion in gastric cancer.BMC medical imaging · 2025Article
- Review
- Preoperative prediction of perineural invasion and lymphovascular invasion with CT radiomics in gastric cancer.European journal of radiology open · 2024Article
- CT texture analysis for differentiating between peritoneal carcinomatosis and peritoneal tuberculosis: a cross-sectional study.Abdominal radiology (New York) · 2024Article
- Article
- Five machine learning-based radiomics models for preoperative prediction of histological grade in hepatocellular carcinoma.Journal of cancer research and clinical oncology · 2023Article
- A Contrast-Enhanced CT-Based Deep Learning System for Preoperative Prediction of Colorectal Cancer Staging and RAS Mutation.Cancers · 2023Article
- Development and validation of a CT radiomics and clinical feature model to predict omental metastases for locally advanced gastric cancer.Scientific reports · 2023Article
- Computed Tomography-Based Deep Learning Nomogram Can Accurately Predict Lymph Node Metastasis in Gastric Cancer.Digestive diseases and sciences · 2023Article
- Are radiomic spleen features useful for assessing the differentiation status of advanced gastric cancer?Frontiers in oncology · 2023Article
- Application of spectral CT in diagnosis, classification and prognostic monitoring of gastrointestinal cancers: progress, limitations and prospects.Frontiers in molecular biosciences · 2023Review
- The value of machine learning based radiomics model in preoperative detection of perineural invasion in gastric cancer: a two-center study.Frontiers in oncology · 2023Article
19 more citing papers are in PubMed but not listed here.
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13 authors.
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Abstract
objectivesTo explore the application of computed tomography (CT) texture analysis in predicting histopathological features of gastric cancers.
methodsPreoperative contrast-enhanced CT images and postoperative histopathological features of 107 patients (82 men, 25 women) with gastric cancers were retrospectively reviewed. CT texture analysis generated: (1) mean attenuation, (2) standard deviation, (3) max frequency, (4) mode, (5) minimum attenuation, (6) maximum attenuation, (7) the fifth, 10th, 25th, 50th, 75th and 90th percentiles, and (8) entropy. Correlations between CT texture parameters and histopathological features were analysed.
resultsMean attenuation, maximum attenuation, all percentiles and mode derived from portal venous CT images correlated significantly with differentiation degree and Lauren classification of gastric cancers (r, -0.231 ~ -0.324, 0.228 ~ 0.321, respectively). Standard deviation and entropy derived from arterial CT images also correlated significantly with Lauren classification of gastric cancers (r = -0.265, -0.222, respectively). In arterial phase analysis, standard deviation and entropy were significantly lower in gastric cancers with than those without vascular invasion; however, minimum attenuation was significantly higher in gastric cancers with than those without vascular invasion.
conclusionCT texture analysis held great potential in predicting differentiation degree, Lauren classification and vascular invasion status of gastric cancers. KEY POINTS: • CT texture analysis is noninvasive and effective for gastric cancer. • Portal venous CT images correlated significantly with differentiation degree and Lauren classification. • Standard deviation, entropy and minimum attenuation in arterial phase reflect vascular invasion.
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