ArticleScientific reports2025
CT-radiomics combined with inflammatory indicators for prediction of progression free survival of resectable esophageal squamous cell carcinoma.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Prediction of anastomotic leakage after esophagectomy for esophageal cancer: a nomogram study integrating systemic inflammation indices and clinical factors.Frontiers in oncology · 2026Article
- A LASSO-based nomogram for predicting sarcopenia-related nutritional risk in esophageal cancer patients: model development, validation, and an interactive clinical decision tool.Frontiers in oncology · 2026Article
- Whole grains temper immune-mediated inflammation in Chinese esophageal squamous-cell carcinoma survivors: a multicenter questionnaire study.Frontiers in immunology · 2025Article
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4 authors.
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Abstract
To develop a nomogram model which combined clinical inflammatory indicators and CT radiomics features to predict progression free survival (PFS) in esophageal squamous cell carcinoma (ESCC) after radical operation. 258 ESCC patients receiving surgical operation treatment were retrospectively collected from July 2017 to March 2019. Clinical data, laboratory results, pathology results, pre-operative CT data, and survival outcomes were analyzed. Using cox proportional hazards regression model to assess the relationship between relevant clinicopathological factors and PFS. C-index and calibration curve were used to evaluate the nomogram model. Survival curves were obtained using the Kaplan-Meier and comparisons were made by using the log-rank test. The inflammatory model, radiomics model and nomogram model all have good predictive efficacy for predicting PFS of ESCC patients in both training and test set. Significant differences were found between the nomogram model and inflammatory model and the radiomics model (DeLong test, Z = 3.869 and 3.195, P < 0.001, P = 0.001). Decision curve analysis (DCA) results revealed the net benefit of nomogram model was better than that of inflammatory model and radiomics model. Kaplan-Meier results showed significant difference in PFS between high-risk and low-risk group in Radscore and nomogram model (P < 0.001), and the high-risk group was prone to postoperative recurrence and poor PFS. The nomogram model developed by combining inflammatory indicators and radiomics features, which is helpful for risk stratification and follow-up work, and improving ESCC patients' prognosis.
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