ArticleBMC cancer2021
Prediction of serosal invasion in gastric cancer: development and validation of multivariate models integrating preoperative clinicopathological features and radiographic findings based on late arterial phase CT images.
Article in BMC cancer, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The application of random forest-based models in prognostication of gastrointestinal tract malignancies: a systematic review.Frontiers in artificial intelligence · 2025Pooled it
- Preoperative prediction of serosal invasion in gastric cancer using multiphase contrast-enhanced CT radiomics.Frontiers in oncology · 2026Article
- The value of deep learning and radiomics models in predicting preoperative serosal invasion in gastric cancer: a dual-center study.Abdominal radiology (New York) · 2025Article
- Current and evolving practices of carbohydrate antigens in gastric cancer: a narrative review.Translational cancer research · 2025Review
- Imaging features of epidermoid cysts in intrapancreatic spleen: a single center study of 24 patients.BMC gastroenterology · 2025Article
- Can the Pathological Response in Patients with Locally Advanced Gastric Cancer Receiving Neoadjuvant Treatment Be Predicted by the CEA/Albumin and CRP/Albumin Ratios?Journal of clinical medicine · 2024Article
- Value of dual-layer spectral detector CT in predicting lymph node metastasis of non-small cell lung cancer.Quantitative imaging in medicine and surgery · 2024Article
- Article
- Development and validation of a CT radiomics and clinical feature model to predict omental metastases for locally advanced gastric cancer.Scientific reports · 2023Article
- Precise prediction of the sensitivity of platinum chemotherapy in SCLC: Establishing and verifying the feasibility of a CT-based radiomics nomogram.Frontiers in oncology · 2023Article
- Development and validation of multivariate models integrating preoperative clinicopathological and radiographic findings to predict HER2 status in gastric cancer.Scientific reports · 2022Article
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6 authors.
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Abstract
backgroundTo develop and validate multivariate models integrating endoscopic biopsy, tumor markers, and CT findings based on late arterial phase (LAP) to predict serosal invasion in gastric cancer (GC).
methodsThe preoperative differentiation degree, tumor markers, CT morphological characteristics, and CT value-related and texture parameters of 154 patients with GC were analyzed retrospectively. Multivariate models based on regression analysis and machine learning algorithms were performed to improve the diagnostic efficacy.
resultsThe differentiation degree, carbohydrate antigen (CA) 199, CA724, CA242, and multiple CT findings based on LAP differed significantly between T1-3 and T4 GCs in the primary cohort (all P < 0.05). Multivariate models based on regression analysis and random forest achieved AUCs of 0.849 and 0.865 in the primary cohort, respectively.
conclusionWe developed and validated multivariate models integrating endoscopic biopsy, tumor markers, CT morphological characteristics, and CT value-related and texture parameters to predict serosal invasion in GCs and achieved favorable performance.
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