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Updated: May 27, 2025
Author Spotlight: Advancing Early Detection and Treatment of Gastrointestinal Tumors
Published on: February 16, 2024
A preoperative pathological staging prediction model for esophageal cancer based on CT radiomics
Haojun Li1, Shuoming Liang1,2, Mengxuan Cui3
1Department of Thoracic Surgery, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, University of Electronic Science and Technology of China, Chengdu, China.
Background:
Accurate 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.
Methods:
This 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.
Results:
Out 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.
Conclusions:
This 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.
