ArticleAbdominal radiology (New York)2022
Comparison of clinical-computed tomography model with 2D and 3D radiomics models to predict occult peritoneal metastases in advanced gastric cancer.
Article in Abdominal radiology (New York), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 2 of them syntheses that pooled it.
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15 citing papers in PubMed, 2 syntheses or guidelines pooled it, 26 citations in OpenAlex.
- Using artificial intelligence and statistics for managing peritoneal metastases from gastrointestinal cancers.Briefings in functional genomics · 2025Pooled it
- Predictive performance of radiomics for peritoneal metastasis in patients with gastric cancer: a meta-analysis and radiomics quality assessment.Journal of cancer research and clinical oncology · 2023Pooled it
- Preoperative prediction of KRAS mutation status in colorectal cancer using a CT-based radiomics nomogram.The British journal of radiology · 2022Trial
- 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 Radiomics-Clinical Nomogram for Pre-Treatment Prediction of Neoadjuvant Chemotherapy Response in Locally Advanced Gastric Cancer.Diagnostics (Basel, Switzerland) · 2026Article
- Radiomics-based assessment of HER2 status and prognosis in gastric cancer: a retrospective dual-center CT study.Abdominal radiology (New York) · 2025Article
- Diagnostic value of the cardiophrenic lymph nodes in gastric peritoneum metastases.African health sciences · 2025Article
- The value of machine learning based on CT radiomics in the preoperative identification of peripheral nerve invasion in colorectal cancer: a two-center study.Insights into imaging · 2024Article
- Computed tomography radiomics identification of T1-2 and T3-4 stages of esophageal squamous cell carcinoma: two-dimensional or three-dimensional?Abdominal radiology (New York) · 2024Article
- Prediction of lymphovascular invasion of gastric cancer based on contrast-enhanced computed tomography radiomics.Frontiers in oncology · 2024Article
- Peritoneal Carcinosis: What the Radiologist Needs to Know.Diagnostics (Basel, Switzerland) · 2023Review
- Prognostic significance of serum CA125 in the overall management for patients with gastrointestinal stromal tumors.BMC gastroenterology · 2023Article
- Biparametric magnetic resonance imaging-based radiomics features for prediction of lymphovascular invasion in rectal cancer.BMC cancer · 2023Article
- Can PD-L1 expression be predicted by contrast-enhanced CT in patients with gastric adenocarcinoma? a preliminary retrospective study.Abdominal radiology (New York) · 2023Article
- CT-based radiomics nomogram for the pre-operative prediction of lymphovascular invasion in colorectal cancer: a multicenter study.The British journal of radiology · 2023Article
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Authors and funding
11 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo compare the ability of a clinical-computed tomography (CT) model vs. 2D and 3D radiomics models for predicting occult peritoneal metastasis (PM) in patients with advanced gastric cancer (AGC).
methodsIn this retrospective study, we included 49 patients with occult PM and 49 control patients (without PM) who underwent preoperative CT and subsequent surgery between January 2016 and December 2018. Clinical information and CT semantic features were collected, and CT radiomics features were extracted. A predictive clinical-CT model was created using multivariate logistic regression. The least absolute shrinkage and selection operator algorithm and logistic regression were used for constructing 2D and 3D radiomics models. These models were validated with an external cohort (n = 30). Receiver operating characteristics curve with area under the curve (AUC), sensitivity, and specificity were used to evaluate predictive performance.
resultsTumor size, mild ascites, and serum CA125 were independent factors predictive of occult PM. The clinical-CT model of these independent factors showed better diagnostic performance than 2D and 3D radiomics models. In the external validation cohort, the AUCs of different models were as follows-clinical-CT model: 0.853 (sensitivity, 66.7%; specificity, 93.3%); 2D radiomics model: 0.622 (sensitivity, 80.0%; specificity, 46.7%); and 3D radiomics model: 0.676 (sensitivity, 60.0%; specificity, 86.0%). The clinical-CT model nomogram showed good clinical predictive efficiency to assess occult PM.
conclusionThe clinical-CT model was better than the radiomics models in predicting occult PM in AGC.
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