Computed Tomography-Based Radiomics Nomogram for Predicting the Postoperative Prognosis of Esophageal Squamous Cell Carcinoma: A Multicenter Study

Published:March 14, 2022DOI:


      The aim of this study was to evaluate and identify the predictive value of combining CT radiomics features and clinical features to determine recurrence-free survival (RFS) and overall survival (OS) after surgery in patients with esophageal squamous cell carcinoma (ESCC).

      Materials and Methods

      A total of 372 patients with surgically and pathologically confirmed ESCC from 2 institutions were retrospectively included. All patients from institution 1 were randomized at a 7:3 ratio into a training cohort (n=206) and an internal validation cohort (n=88), and patients from institution 2 were used as an external validation cohort (n=78). The association between the radiomics features and RFS and OS was assessed in the training cohort and verified in the validation cohort. Furthermore, the performance of the radiomics nomogram was evaluated by combining the radiomics score (rad-score) and clinical risk factors.


      The radiomics nomogram that combined radiomics features and clinical risk factors was better than the clinical nomogram and radiomics model alone at predicting RFS and OS in ESCC patients. All calibration curves showed significant consistency between predicted survival and actual survival.


      Radiomics features could be used to stratify patients with ESCC following radical resection into high- and low-risk groups. Furthermore, the radiomics nomograms provided better predictive accuracy than other predictive models and might serve as a therapeutic decision-making reference for clinicians and be used to monitor the risks of recurrence and death.

      Key Words


      AIC (Akaike information criterion), CI (Confidence interval), C-index (Harrell's concordance index), CT (Computed tomography), OS (Overall survival), ESCC (Esophageal squamous cell carcinoma), HR (Hazard ratio), ICC (Inter- and intraclass correlation coefficient), LASSO (Least absolute shrinkage and selection operator), LNR (Lymph node ratio), LoG (Laplace of Gaussian), LVI (Lymphovascular invasion), mRMR (The minimum redundancy maximal relevance), Rad-score (Radiomics score), RFS (Recurrence-free survival), ROI (Region of interest)
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