ArticleAbdominal radiology (New York)2024
Decoding tumor stage by peritumoral and intratumoral radiomics in resectable esophageal squamous cell carcinoma.
Article in Abdominal radiology (New York), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Integration of CT radiomics and machine learning for preoperative T staging of esophageal squamous cell carcinoma.Journal of thoracic disease · 2026Article
- Interpretable machine learning integrating intra-/peritumoral CT radiomics and serum indicators for predicting poorly differentiated esophageal squamous cell carcinoma.Frontiers in oncology · 2026Article
- A nomogram integrating machine learning-derived CT radiomics and clinical characteristics for prognostic assessment in patients with locally advanced esophageal squamous cell carcinoma treated with definitive chemoradiotherapy with or without immunotherapy.Journal of translational medicine · 2025Article
- RegGAN-based contrast-free CT enhances esophageal cancer assessment: multicenter validation of automated tumor segmentation and T-staging.La Radiologia medica · 2025Article
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8 authors.
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Abstract
purposeTo evaluate the potential application of radiomics in predicting Tumor-Node-Metastasis (TNM) stage in patients with resectable esophageal squamous cell carcinoma (ESCC).
methodsThis retrospective study included 122 consecutive patients (mean age, 57 years; 27 women). Corresponding tumor of interest was identified on axial arterial-phase CT images with manual annotation. Radiomics features were extracted from intra- and peritumoral regions. Features were pruned to train LASSO regression model with 93 patients to construct a radiomics signature, whose performance was validated in a test set of 29 patients. Prognostic value of radiomics-predicted TNM stage was estimated by survival analysis in the entire cohort.
resultsThe radiomics signature incorporating one intratumoral and four peritumoral features was significantly associated with TNM stage. This signature discriminated tumor stage with an area under curve (AUC) of 0.823 in the training set, with similar performance in the test set (AUC 0.813). Recurrence-free survival (RFS) was significantly different between different radiomics-predicted TNM stage groups (Low-risk vs high-risk, log-rank P = 0.004). Univariate and multivariate Cox regression analyses revealed that radiomics-predicted TNM stage was an independent preoperative factor for RFS.
conclusionsThe proposed radiomics signature combing intratumoral and peritumoral features was predictive of TNM stage and associated with prognostication in ESCC.
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