ArticleInsights into imaging2024
CT-based radiomics combined with hematologic parameters for survival prediction in locally advanced esophageal cancer patients receiving definitive chemoradiotherapy.
Article in Insights into imaging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 6 citations in OpenAlex.
- An explainable prognostic model after vascularized bone grafting for hip preservation based on CT radiomics combined with SHAP.Physical and engineering sciences in medicine · 2026Article
- Radiomics applications in the modern management of esophageal squamous cell carcinoma.Medical oncology (Northwood, London, England) · 2025Review
- ChatGPT as an effective tool for quality evaluation of radiomics research.European radiology · 2025Article
- Radiomics and prognostic nutritional index for predicting postoperative survival in esophageal carcinoma.European journal of medical research · 2025Article
- Predictive Radiomics-Based Model for Recurrence-Free Survival After Curative Resection in Patients with Hepatocellular Carcinoma.Journal of hepatocellular carcinoma · 2025Article
- Developing and Validating a Risk Model for Severe Bone Marrow Suppression in Esophageal Cancer Treated with Radiotherapy or Chemoradiotherapy: A Retrospective Cohort Study.Technology in cancer research & treatmentArticle
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Authors and funding
7 authors at 4 institutions in 1 country.
Funding
Abstract
objectivesThe purpose of this study was to investigate the prognostic significance of radiomics in conjunction with hematological parameters in relation to the overall survival (OS) of individuals diagnosed with esophageal squamous cell carcinoma (ESCC) following definitive chemoradiotherapy (dCRT).
methodsIn this retrospective analysis, a total of 122 patients with locally advanced ESCC were included. These patients were randomly assigned to either the training cohort (n = 85) or the validation cohort (n = 37). In the training group, the least absolute shrinkage and selection operator (LASSO) regression was utilized to choose the best radiomic features for calculating the Rad-score. To develop a nomogram model, both univariate and multivariate analyses were conducted to identify the clinical factors and hematologic parameters that could predict the OS. The performance of the predictive model was evaluated using the C-index, while the accuracy was assessed through the calibration curve.
resultsThe Rad-score was calculated by selecting 10 radiomic features through LASSO regression. OS was predicted independently by neutrophil-to-monocyte ratio (NMR) and Rad-score according to the results of multivariate analysis. Patients who had a Rad-score > 0.47 and an NMR > 9.76 were at a significant risk of mortality. A nomogram was constructed using the findings from the multivariate analysis. In the training cohort, the nomogram had a C-index of 0.619, while in the validation cohort, it was 0.573. The model's accuracy was demonstrated by the calibration curve, which was excellent.
conclusionA prognostic model utilizing radiomics and hematologic parameters was developed, enabling the prediction of OS in patients with ESCC following dCRT. CRITICAL RELEVANCE STATEMENT: Patients with esophageal cancer who underwent definitive chemoradiotherapy may benefit from including CT radiomics in the nomogram model. KEY POINTS: • Predicting the prognosis of ESCC patients before treatment is particularly important. • Patients with a Rad-score > 0.47 and neutrophil-to-monocyte ratio > 9.76 had a high risk of mortality. • CT-based radiomics nomogram model could be used to predict the survival of patients.
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