ArticleNPJ precision oncology2025
Unraveling the power of radiomics: prediction and exploration of lymph node metastasis in stage T1/2 esophageal squamous cell carcinoma.
Article in NPJ precision oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Development and internal validation of a multivariable MRI-based radiomics prediction model for preoperative cervical lymph node metastasis in thyroid cancer.Gland surgery · 2026Article
- Intratumoral and peritumoral CT radiomics combined with clinical and hematologic inflammatory markers for predicting lymph node metastasis in esophageal squamous cell carcinoma: a retrospective single-center study.La Radiologia medica · 2026Article
- Integration of radiomics, deep learning, transcriptomics, and metabolomics reveals prognostic risk stratification and underlying biological mechanisms in colorectal cancer.NPJ precision oncology · 2026Article
- Review
- EV-derived miR-1246 promotes lymph node metastasis in esophageal squamous cell carcinoma through targeting the PPP2CB/NF-κB axis.Frontiers in immunology · 2026Article
- A prognostic nutritional index-based nomogram for predicting postoperative survival in stages I-III rectal cancer patients.Frontiers in nutrition · 2025Article
- From endoscopy to surgery: a translational perspective on early esophageal cancer management.Frontiers in medicine · 2025Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Accurate assessment of lymph node metastasis (LNM) in T1/2-stage esophageal squamous cell carcinoma (ESCC) is critical for treatment planning but remains challenging due to diagnostic inaccuracies and unclear metastatic mechanisms. This study aimed to predict LNM in T1/2-stage ESCC using machine learning-based radiomics and elucidate its biological underpinnings. We retrospectively analyzed 374 surgically treated ESCC patients from two centers, employing six machine-learning algorithms to derive an optimal radiomics score. Key pathways and genes linked to LNM were investigated via bioinformatics and experimental validation. The decision tree (DT)-based radiomics model demonstrated superior predictive performance, with AUCs of 0.933 (training), 0.887 (validation), and 0.845 (test). Bioinformatics analysis implicated tumor-lymphatic invasion pathways, with EFNA1 emerging as a potential key regulator. These findings highlight the clinical utility of radiomics for LNM prediction in early-stage ESCC and provide insights into its molecular mechanisms.
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Registered trials
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