ArticleFrontiers in oncology2021
Computed Tomography-Based Radiomics in Predicting T Stage and Length of Esophageal Squamous Cell Carcinoma.
Article in Frontiers in oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed.
- AI in esophageal cancer: advances, barriers to clinical translation, and perspectives for digital health.Journal of translational medicine · 2026Review
- Integration of CT radiomics and machine learning for preoperative T staging of esophageal squamous cell carcinoma.Journal of thoracic disease · 2026Article
- Macrophage efferocytosis mediated by the TP63-RAC2 pathway promotes immunosuppressive remodeling in esophageal cancer.Cell reports. Medicine · 2026Article
- MR-Guided Radiotherapy in Oesophageal Cancer: From Principles to Practice-A Narrative Review.Current oncology (Toronto, Ont.) · 2026Review
- RegGAN-based contrast-free CT enhances esophageal cancer assessment: multicenter validation of automated tumor segmentation and T-staging.La Radiologia medica · 2025Article
- Machine Learning and Deep Learning Hybrid Approach Based on Muscle Imaging Features for Diagnosis of Esophageal Cancer.Diagnostics (Basel, Switzerland) · 2025Article
- Radiomics applications in the modern management of esophageal squamous cell carcinoma.Medical oncology (Northwood, London, England) · 2025Review
- A novel framework for esophageal cancer grading: combining CT imaging, radiomics, reproducibility, and deep learning insights.BMC gastroenterology · 2025Article
- Multi-modal radiomics features to predict overall survival of locally advanced esophageal cancer after definitive chemoradiotherapy.BMC cancer · 2025Article
- Value of Computed Tomography Scan for Detecting Lymph Node Metastasis in Early Esophageal Squamous Cell Carcinoma.Annals of surgical oncology · 2025Article
- A preoperative pathological staging prediction model for esophageal cancer based on CT radiomics.BMC cancer · 2025Article
- Development and Validation of a Computed Tomography-Based Model for Noninvasive Prediction of the T Stage in Gastric Cancer: Multicenter Retrospective Study.Journal of medical Internet research · 2024Article
- Establishment and validation of novel predictive models to predict bone metastasis in newly diagnosed prostate adenocarcinoma based on single-photon emission computed tomography radiomics.Annals of nuclear medicine · 2024Article
- Review
- Radiomics-clinical nomogram for preoperative lymph node metastasis prediction in esophageal carcinoma.The British journal of radiology · 2024Article
- Deep learning prediction of esophageal squamous cell carcinoma invasion depth from arterial phase enhanced CT images: a binary classification approach.BMC medical informatics and decision making · 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
- CT radiomics in the identification of preoperative understaging in patients with clinical stage T1-2N0 esophageal squamous cell carcinoma.Quantitative imaging in medicine and surgery · 2023Article
- Preoperative prediction of clinical and pathological stages for patients with esophageal cancer using PET/CT radiomics.Insights into imaging · 2023Article
- Could tumour volume and major and minor axis based on CTA statistical anatomy improve the pre-operative T-stage in oesophageal cancer?Cancer medicine · 2023Article
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10 authors.
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Abstract
backgroundBecause of the superficial and infiltrative spreading patterns of esophageal squamous cell carcinoma (ESCC), an accurate assessment of tumor extent is challenging using imaging-based clinical staging. Radiomics features extracted from pretreatment computed tomography (CT) or magnetic resonance imaging have shown promise in identifying tumor characteristics. Accurate staging is essential for planning cancer treatment, especially for deciding whether to offer surgery or radiotherapy (chemotherapy) in patients with locally advanced ESCC. Thus, this study aimed to evaluate the predictive potential of contrast-enhanced CT-based radiomics as a non-invasive approach for estimating pathological tumor extent in ESCC patients.
methodsPatients who underwent esophagectomy between October 2011 and September 2017 were retrospectively studied and included 116 patients with pathologically confirmed ESCC. Contrast-enhanced CT from the neck to the abdomen was performed in all patients during the 2 weeks before the operation. Radiomics features were extracted from segmentations, which were contoured by radiologists. Cluster analysis was performed to obtain clusters with similar radiomics characteristics, and chi-squared tests were used to assess differences in clinicopathological features and survival among clusters. Furthermore, a least absolute shrinkage and selection operator was performed to select radiomics features and construct a radiomics model. Receiver operating characteristic analysis was used to evaluate the predictive ability of the radiomics signatures.
resultsAll 116 ESCC patients were divided into two groups according to the cluster analysis. The chi-squared test showed that cluster-based radiomics features were significantly correlated with T stage (
conclusionsCT contrast radiomics is a simple and non-invasive method that shows promise for predicting pathological T stage and tumor length preoperatively in ESCC patients and may aid in the accurate assessments of patients in combination with the existing examinations.
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