ArticleInsights into imaging2023
Preoperative prediction of clinical and pathological stages for patients with esophageal cancer using PET/CT radiomics.
Article in Insights into imaging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed, 20 citations in OpenAlex.
- CT-based Extranodal Extension for Nodal Staging and Prognostic Stratification in Surgically Resectable Esophageal Squamous Cell Carcinoma.Radiology. Imaging cancer · 2026Observational
- Integration of CT radiomics and machine learning for preoperative T staging of esophageal squamous cell carcinoma.Journal of thoracic disease · 2026Article
- Updated evidence on the accuracy ofQuantitative imaging in medicine and surgery · 2026Review
- MR-Guided Radiotherapy in Oesophageal Cancer: From Principles to Practice-A Narrative Review.Current oncology (Toronto, Ont.) · 2026Review
- Predicting esophageal cancer response to neoadjuvant therapy with magnetic resonance imaging radiomics.World journal of gastrointestinal oncology · 2025Article
- Magnetic resonance imaging-based radiomics signature for predicting preoperative staging of esophageal cancer.World journal of radiology · 2025Article
- Machine Learning and Deep Learning Hybrid Approach Based on Muscle Imaging Features for Diagnosis of Esophageal Cancer.Diagnostics (Basel, Switzerland) · 2025Article
- Unraveling the power of radiomics: prediction and exploration of lymph node metastasis in stage T1/2 esophageal squamous cell carcinoma.NPJ precision oncology · 2025Article
- Article
- Radiomics applications in the modern management of esophageal squamous cell carcinoma.Medical oncology (Northwood, London, England) · 2025Review
- ChatGPT as an effective tool for quality evaluation of radiomics research.European radiology · 2025Article
- A preoperative pathological staging prediction model for esophageal cancer based on CT radiomics.BMC cancer · 2025Article
- IntegratingBMC cancer · 2024Article
- Supraclavicular lymph node metastasis should not be defined as regional lymph node metastasis in cervical and upper thoracic esophageal squamous cell carcinoma.Oncology letters · 2024Article
- Application of predictive model based on CT radiomics and machine learning in diagnosis for occult locally advanced esophageal squamous cell carcinoma before treatment: A two-center study.Translational oncology · 2024Article
- Review
- More than meets the eye: 2-[European journal of nuclear medicine and molecular imaging · 2024Article
- Machine learning-based MRI radiomics for assessing the level of tumor infiltrating lymphocytes in oral tongue squamous cell carcinoma: a pilot study.BMC medical imaging · 2024Article
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Authors and funding
13 authors at 3 institutions in 1 country.
Funding
Abstract
backgroundPreoperative stratification is critical for the management of patients with esophageal cancer (EC). To investigate the feasibility and accuracy of PET-CT-based radiomics in preoperative prediction of clinical and pathological stages for patients with EC.
methodsHistologically confirmed 100 EC patients with preoperative PET-CT images were enrolled retrospectively and randomly divided into training and validation cohorts at a ratio of 7:3. The maximum relevance minimum redundancy (mRMR) was applied to select optimal radiomics features from PET, CT, and fused PET-CT images, respectively. Logistic regression (LR) was applied to classify the T stage (T
resultsSeven, 10, and 7 CT features; 7, 8, and 7 PET features; and 3, 6, and 3 fused PET/CT features were selected using mRMR for the prediction of T stage, LNM, and pstage, respectively. The area under curves (AUCs) for T stage, LNM, and pstage prediction in the validation cohorts were 0.846, 0.756, 0.665, and 0.815; 0.769, 0.760, 0.665, and 0.824; and 0.727, 0.785, 0.689, and 0.837 for models of CT_LR_Score, PET_ LR_Score, Fused_ LR_Score, and CT + PET_ LR_Score, respectively.
conclusionsAccurate prediction ability was observed with combined PET and CT radiomics in the prediction of T stage, LNM, and pstage for EC patients. CRITICAL RELEVANCE STATEMENT: PET/CT radiomics is feasible and promising to stratify stages for esophageal cancer preoperatively. KEY POINTS: • PET-CT radiomics achieved the best performance for Node and pathological stage prediction. • CT radiomics achieved the best AUC for T stage prediction. • PET-CT radiomics is feasible and promising to stratify stages for EC preoperatively.
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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.