ArticleWorld journal of gastroenterology2022
Application of computed tomography-based radiomics in differential diagnosis of adenocarcinoma and squamous cell carcinoma at the esophagogastric junction.
Article in World journal of gastroenterology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- CT-based radiomics for predicting lymph node metastasis in esophageal cancer: a systematic review and meta-analysis.Frontiers in oncology · 2024Pooled it
- Exploring the origins of esophagogastric junction cancer through omics: A focus on the Siewert II subclassification.Surgery today · 2026Review
- AI in esophageal cancer: advances, barriers to clinical translation, and perspectives for digital health.Journal of translational medicine · 2026Review
- Machine learning survival prediction in esophageal cancer using radiomics and body composition from pretreatment and follow-up T12-level computed tomography.World journal of gastrointestinal oncology · 2025Article
- Machine learning-based prediction of gastroesophageal junction cancer using electronic medical records.Clinical and experimental medicine · 2025Article
- Machine Learning and Deep Learning Hybrid Approach Based on Muscle Imaging Features for Diagnosis of Esophageal Cancer.Diagnostics (Basel, Switzerland) · 2025Article
- A novel framework for esophageal cancer grading: combining CT imaging, radiomics, reproducibility, and deep learning insights.BMC gastroenterology · 2025Article
- Article
- Deep Learning Radiomics Analysis of CT Imaging for Differentiating Between Crohn's Disease and Intestinal Tuberculosis.Journal of imaging informatics in medicine · 2024Article
- Review
- The [Cancers · 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
- Deep learning based radiomics for gastrointestinal cancer diagnosis and treatment: A minireview.World journal of gastroenterology · 2022Review
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Authors and funding
10 authors.
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Abstract
backgroundThe biological behavior of carcinoma of the esophagogastric junction (CEGJ) is different from that of gastric or esophageal cancer. Differentiating squamous cell carcinoma of the esophagogastric junction (SCCEG) from adenocarcinoma of the esophagogastric junction (AEG) can indicate Siewert stage and whether the surgical route for patients with CEGJ is transthoracic or transabdominal, as well as aid in determining the extent of lymph node dissection. With the development of neoadjuvant therapy, preoperative determination of pathological type can help in the selection of neoadjuvant radiotherapy and chemotherapy regimens.
aimTo establish and evaluate computed tomography (CT)-based multiscale and multiphase radiomics models to distinguish SCCEG and AEG preoperatively.
methodsWe retrospectively analyzed the preoperative contrasted-enhanced CT imaging data of single-center patients with pathologically confirmed SCCEG (
resultsThe 2D-venous model (5 features, AUC: 0.849) performed better than 2D-arterial (5 features, AUC: 0.808). The 2D-arterial-venous combined model could further enhance the performance (AUC: 0.869). The 3D-venous model (7 features, AUC: 0.877) performed better than 3D-arterial (10 features, AUC: 0.876). And the 3D-arterial-venous combined model (AUC: 0.904) outperformed other single-phase-based models. The venous model showed a positive improvement compared with the arterial model (NRI > 0, IDI > 0), and the 3D-venous and combined models showed a significant positive improvement compared with the 2D-venous and combined models (
conclusionThe combined arterial-venous CT radiomics model based on 3D segmentation can improve the performance in differentiating EGJ squamous cell carcinoma from adenocarcinoma.
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