ArticleBMC cancer2025
A preoperative pathological staging prediction model for esophageal cancer based on CT radiomics.
Article in BMC cancer, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 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
- Fusion of 2.5D deep transfer learning and radiomics for predicting benign and malignant Lung Imaging Reporting and Data System (Lung-RADS) 3 and 4A nodules.Journal of thoracic disease · 2025Article
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16 authors.
Funding
Abstract
backgroundAccurate and comprehensive preoperative staging is one of the most important prognostic factors for the management of esophageal cancer (EC). We aimed to develop and validate predictive models using radiomics from preoperative contrast-enhanced Computed Tomography (CT) images to assess pathological staging in EC patients.
methodsThis study retrospectively included 161 patients who underwent esophagectomy at Sichuan Cancer Hospital from July 2018 to February 2023. Pathological staging outcomes encompassed overall TNM staging, T and N staging, and tumor progressions (vascular invasion and perineural invasion). Radiomics features were extracted from segmented regions of tumors. A radiomic signature (Rad-signature) for each outcome was developed using a fivefold cross-validation least absolute shrinkage and selection operator (LASSO) regression model within the training cohort and subsequently validated in the test cohort for predictive accuracy.
resultsOut of the 851 radiomics features extracted, two were selected to formulate the Rad-signature for each staging outcome. These signatures showed a significant correlation with their respective outcomes in both the training set and the testing set. Furthermore, the Rad-signature exhibited favorable predictive performance for advanced pTNM staging, advanced pT staging, vascular invasion and perineural invasion, with AUC of 0.721 [95%CI, 0.570-0.872], 0.900 [95%CI 0.805-0.995], 0.824 [0.686-0.961], and 0.737 [0.586-0.887], respectively. However, the predictive performance of the Rad-signature for pN staging is moderate (AUC = 0.693 [0.534-0.852]), indicating needs for additional data modalities.
conclusionsThis study established a non-invasive preoperative radiomics model that demonstrated good predictive performance in determining the pTNM staging, pT staging, vascular invasion, and perineural invasion for EC patients. These results could inform personalized treatment strategies and improve outcomes for EC patients.
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