SynthesisDiseases of the esophagus : official journal of the International Society for Diseases of the Esophagus2023
Performance of radiomics-based artificial intelligence systems in the diagnosis and prediction of treatment response and survival in esophageal cancer: a systematic review and meta-analysis of diagnostic accuracy.
Synthesis in Diseases of the esophagus : official journal of the International Society for Diseases of the Esophagus, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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15 citing papers in PubMed, 18 citations in OpenAlex.
- Automated detection and characterization of small cell lung cancer liver metastasis on computed tomography.Diagnostic and interventional radiology (Ankara, Turkey) · 2026Article
- AI in esophageal cancer: advances, barriers to clinical translation, and perspectives for digital health.Journal of translational medicine · 2026Review
- Comparative Analysis of F-18 FDG PET-Based Metabolic Parameters in the Differential Diagnosis of Esophageal Squamous Cell Carcinoma and Adenocarcinoma.World journal of nuclear medicine · 2026Article
- Development and validation of a CT radiomics nomogram for preoperative prediction of right recurrent laryngeal nerve lymph node metastasis in thoracic esophageal squamous cell carcinoma.Journal of thoracic disease · 2026Article
- Clinical-Radiomics Signature Predicts Pathologic Complete Response After Neoadjuvant Therapy in Oesophageal Squamous Cell Carcinoma.Interdisciplinary cardiovascular and thoracic surgery · 2026Article
- Integration of habitat radiomics and traditional radiomic features for predicting pathological complete response in esophageal squamous cell carcinoma following neoadjuvant immunotherapy and chemotherapy: a multicenter comparative study.Journal of translational medicine · 2026Article
- Artificial intelligence in surgical decision-making across the perioperative continuum: a scoping review.Frontiers in digital health · 2026Review
- Article
- Applications of artificial intelligence in postoperative surveillance and management of esophageal squamous cell carcinoma.Frontiers in artificial intelligence · 2026Review
- Explanation and Elaboration with Examples for METRICS (METRICS-E3): an initiative from the EuSoMII Radiomics Auditing Group.Insights into imaging · 2025Article
- 18F-FDG PET/CT-based deep radiomic models for enhancing chemotherapy response prediction in breast cancer.Medical oncology (Northwood, London, England) · 2025Article
- Research status and progress of deep learning in automatic esophageal cancer detection.World journal of gastrointestinal oncology · 2025Review
- PSMA PET/CT for prostate cancer diagnosis: current applications and future directions.Journal of cancer research and clinical oncology · 2025Review
- Review
- Radiomics-clinical nomogram for preoperative lymph node metastasis prediction in esophageal carcinoma.The British journal of radiology · 2024Article
Corrections and comments
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Authors and funding
4 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Radiomics can interpret radiological images with more detail and in less time compared to the human eye. Some challenges in managing esophageal cancer can be addressed by incorporating radiomics into image interpretation, treatment planning, and predicting response and survival. This systematic review and meta-analysis provides a summary of the evidence of radiomics in esophageal cancer. The systematic review was carried out using Pubmed, MEDLINE, and Ovid EMBASE databases-articles describing radiomics in esophageal cancer were included. A meta-analysis was also performed; 50 studies were included. For the assessment of treatment response using 18F-FDG PET/computed tomography (CT) scans, seven studies (443 patients) were included in the meta-analysis. The pooled sensitivity and specificity were 86.5% (81.1-90.6) and 87.1% (78.0-92.8). For the assessment of treatment response using CT scans, five studies (625 patients) were included in the meta-analysis, with a pooled sensitivity and specificity of 86.7% (81.4-90.7) and 76.1% (69.9-81.4). The remaining 37 studies formed the qualitative review, discussing radiomics in diagnosis, radiotherapy planning, and survival prediction. This review explores the wide-ranging possibilities of radiomics in esophageal cancer management. The sensitivities of 18F-FDG PET/CT scans and CT scans are comparable, but 18F-FDG PET/CT scans have improved specificity for AI-based prediction of treatment response. Models integrating clinical and radiomic features facilitate diagnosis and survival prediction. More research is required into comparing models and conducting large-scale studies to build a robust evidence base.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.