ArticleFrontiers in oncology2021
Development and Validation of a Clinical Prognostic Nomogram for Esophageal Adenocarcinoma Patients.
Article in Frontiers in oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Genetic characteristics and clinical-specific survival prediction in elderly patients with gallbladder cancer: a genetic and population-based study.Frontiers in endocrinology · 2023Trial
- Nomogram for prognosis of patients with esophageal squamous cell cancer after minimally invasive esophagectomy established based on non-textbook outcome.Surgical endoscopy · 2022Trial
- Development and validation of a metabolic-inflammatory-nutritional prognostic model for esophageal squamous carcinoma.BMC cancer · 2026Article
- Nomograms for prognosis prediction in esophageal adenocarcinoma: realities and challenges.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2025Review
- A nomogram for predicting cancer-specific survival in patients with locally advanced unresectable esophageal cancer: development and validation study.Frontiers in immunology · 2025Article
- Prognosis prediction in esophageal signet-ring-cell carcinoma: a competing risk analysis.BMC gastroenterology · 2023Article
- Establishment and validation of a nomogram model for predicting postoperative recurrence-free survival in stage IA3 lung adenocarcinoma: a retrospective cohort study.Translational lung cancer research · 2022Article
- Construction, Validation, and Visualization of Two Web-Based Nomograms for Predicting Overall Survival and Cancer-Specific Survival in Elderly Patients with Primary Osseous Spinal Neoplasms.Journal of oncology · 2022Article
- A novel risk classification system based on the eighth edition of TNM frameworks for esophageal adenocarcinoma patients: A deep learning approach.Frontiers in oncology · 2022Article
- A clinical prognostic model for patients with esophageal squamous cell carcinoma based on circulating tumor DNA mutation features.Frontiers in oncology · 2022Article
- Development and validation of a deep learning model to predict survival of patients with esophageal cancer.Frontiers in oncology · 2022Article
- Construction and Validation of Prognosis Nomogram for Metastatic Lung Squamous Cell Carcinoma: A Population-Based Study.Technology in cancer research & treatmentArticle
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Authors and funding
7 authors.
Funding
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Abstract
backgroundClinical staging is essential for clinical decisions but remains imprecise. We purposed to construct a novel survival prediction model for improving clinical staging system (cTNM) for patients with esophageal adenocarcioma (EAC).
methodsA total of 4180 patients diagnosed with EAC were extracted from the Surveillance, Epidemiology, and End Results (SEER) database and included as the training cohort. Significant prognostic variables were identified for nomogram model development using multivariable Cox regression. The model was validated internally by bootstrap resampling, and then subjected to external validation with a separate cohort of 886 patients from 2 institutions in China. The prognostic performance was measured by concordance index (C-index), Akaike information criterion (AIC) and calibration plots. Different risk groups were stratified by the nomogram scores.
resultsA total of six variables were determined related with survival and entered into the nomogram construction. The calibration curves showed satisfied agreement between nomogram-predicted survival and actual observed survival for 1-, 3-, and 5-year overall survival. By calculating the AIC and C-index values, our nomogram presented superior discriminative and risk-stratifying ability than current TNM staging system. Significant distinctions in survival curves were observed between different risk subgroups stratified by nomogram scores.
conclusionThe established and validated nomogram presented better risk-stratifying ability than current clinical staging system, and could provide a convenient and reliable tool for individual survival prediction and treatment strategy making.
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