Imaging Techniques for Esophageal Cancer Staging
Summary
Esophageal cancer staging relies on a combination of anatomical and functional imaging to determine tumour depth (T stage), regional lymph node involvement (N stage) and distant metastases (M stage). Cross-sectional computed tomography (CT) remains a first-line modality for assessing tumour extent and detecting distant spread, while magnetic resonance imaging (MRI), particularly diffusion-weighted imaging (DWI), offers superior soft-tissue contrast and quantitative assessments of cellular density. Positron emission tomography combined with CT (PET/CT) provides metabolic mapping of 18F-fluorodeoxyglucose uptake, enhancing detection of occult nodal and distant disease. Endoscopic ultrasonography (EUS) continues to be the most accurate tool for local T and N staging but is operator-dependent and invasive. Recent advances in radiomics and deep learning have enabled automated extraction of high-dimensional quantitative features from CT, PET/CT and endoscopic images, improving non-invasive prediction of invasion depth and nodal metastasis. Hybrid PET/MR platforms and artificial intelligence–driven endoscopic image analysis are emerging to refine staging accuracy, support personalised treatment planning—including endoscopic resection, neoadjuvant therapy and radiotherapy target delineation—and ultimately reduce procedural morbidity.
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Imaging Techniques for Esophageal Cancer Staging publication trend
The graph below shows the total number of articles in imaging techniques for esophageal cancer staging across all publications each year (not limited to Nature Index journals).
Technical terms
Radiomics: Extraction and analysis of quantitative features from medical images to characterise tumour heterogeneity and phenotype.
Deep learning: Subset of artificial intelligence using multilayered neural networks to automatically learn complex patterns from imaging data.
Endoscopic ultrasonography (EUS): Minimally invasive procedure combining endoscopy with high-frequency ultrasound to visualise oesophageal wall layers and surrounding lymph nodes.
Diffusion-weighted imaging (DWI): MRI sequence sensitive to water molecule motion, providing contrast based on tissue cellularity and enabling precise tumour boundary delineation.
References
- 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 (2024).
- Preoperative prediction of clinical and pathological stages for patients with esophageal cancer using PET/CT radiomics. Insights into Imaging (2023).
- Review and prospects of new progress in intelligent imaging research on lymph node metastasis in esophageal carcinoma. Meta-Radiology (2024).
- Improved longitudinal length accuracy of gross tumor volume delineation with diffusion weighted magnetic resonance imaging for esophageal squamous cell carcinoma. Radiation Oncology (2013).
- Clinical Utility of Positron Emission Tomography Magnetic Resonance Imaging (PET-MRI) in Gastrointestinal Cancers. Diagnostics (2016).
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