SynthesisFrontiers in oncology2022
18F-FDG PET Radiomics as Predictor of Treatment Response in Oesophageal Cancer: A Systematic Review and Meta-Analysis.
Synthesis in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 2 of them syntheses that pooled it.
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Who cites it
9 citing papers in PubMed, 2 syntheses or guidelines pooled it, 15 citations in OpenAlex.
- CT-based radiomics for predicting lymph node metastasis in esophageal cancer: a systematic review and meta-analysis.Frontiers in oncology · 2024Pooled it
- 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.Diseases of the esophagus : official journal of the International Society for Diseases of the Esophagus · 2023Pooled it
- Radiomics applications in the modern management of esophageal squamous cell carcinoma.Medical oncology (Northwood, London, England) · 2025Review
- Multi-modal radiomics features to predict overall survival of locally advanced esophageal cancer after definitive chemoradiotherapy.BMC cancer · 2025Article
- Gastro-Esophageal Cancer: Can Radiomic Parameters from BaselineDiagnostics (Basel, Switzerland) · 2024Article
- Radiomics-clinical nomogram for preoperative lymph node metastasis prediction in esophageal carcinoma.The British journal of radiology · 2024Article
- An overview of meta-analyses on radiomics: more evidence is needed to support clinical translation.Insights into imaging · 2023Article
- Radiomics features of computed tomography and magnetic resonance imaging for predicting response to transarterial chemoembolization in hepatocellular carcinoma: a meta-analysis.Frontiers in oncology · 2023Review
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Corrections and comments
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Authors and funding
10 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The best treatment strategy for oesophageal cancer patients achieving a complete clinical response after neoadjuvant chemoradiation is a burning topic. The available diagnostic tools, such as 18F-FDG PET/CT performed routinely, cannot accurately evaluate the presence or absence of the residual tumour. The emerging field of radiomics may encounter the critical challenge of personalised treatment. Radiomics is based on medical image analysis, executed by extracting information from many image features; it has been shown to provide valuable information for predicting treatment responses in oesophageal cancer. This systematic review with a meta-analysis aims to provide current evidence of 18F-FDG PET-based radiomics in predicting response treatments following neoadjuvant chemoradiotherapy in oesophageal cancer. A comprehensive literature review identified 1160 studies, of which five were finally included in the study. Our findings provided that pooled Area Under the Curve (AUC) of the five selected studies was relatively high at 0.821 (95% CI: 0.737-0.904) and not influenced by the sample size of the studies. Radiomics models exhibited a good performance in predicting pathological complete responses (pCRs). This review further strengthens the great potential of 18F-FDG PET-based radiomics to predict pCRs in oesophageal cancer patients who underwent neoadjuvant chemoradiotherapy. Additionally, our review imparts additional support to prospective studies on 18F-FDG PET radiomics for a tailored treatment strategy of oesophageal cancer patients. Systematic Review Registration: https://www.crd.york.ac.uk/prospero/, identifier CRD42021274636.
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Registered trials
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