ArticleBosnian journal of basic medical sciences2021
Construction and verification of prognostic nomogram for early-onset esophageal cancer.
Article in Bosnian journal of basic medical sciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Therapeutic outcomes of different treatment approaches for T1a-M3/T1b esophageal cancer and development of a prognostic prediction model.NPJ digital medicine · 2025Article
- A novel prognostic nomogram to predict survival of patients with esophageal squamous cell carcinoma after definitive chemoradiotherapy.Journal of thoracic disease · 2025Article
- Survival outcomes and prognostic factors of early-onset and late-onset metastatic esophageal cancer: a population-based study.Scientific reports · 2025Article
- 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
- Article
- A nomogram for predicting cancer-specific survival in patients with locally advanced unresectable esophageal cancer: development and validation study.Frontiers in immunology · 2025Article
- Comparison of machine learning methods for Predicting 3-Year survival in elderly esophageal squamous cancer patients based on oxidative stress.BMC cancer · 2024Article
- Use machine learning to predict pulmonary metastasis of esophageal cancer: a population-based study.Journal of cancer research and clinical oncology · 2024Article
- Prediction of hepatic metastasis in esophageal cancer based on machine learning.Scientific reports · 2024Article
- Comparison of the prognosis of medullary breast carcinoma and invasive ductal carcinoma: a SEER-based study.Translational cancer research · 2024Article
- A Long-term Survival Risk Prediction Model for Patients with Superficial Esophageal Squamous Cell Carcinoma.Journal of Cancer · 2024Article
- Construction and validation of a novel prognostic nomogram for predicting overall survival in lung adenocarcinoma patients with different patterns of metastasis.Journal of cancer research and clinical oncology · 2023Article
- [Efficacy of machine learning modelsNan fang yi ke da xue xue bao = Journal of Southern Medical University · 2023Article
- Construction and validation of prognostic nomogram for metaplastic breast cancer.Bosnian journal of basic medical sciences · 2022Article
- Prognostic prediction by a novel integrative inflammatory and nutritional score based on least absolute shrinkage and selection operator in esophageal squamous cell carcinoma.Frontiers in nutrition · 2022Article
- The Evaluation of a SEER-Based Nomogram in Predicting the Survival of Patients Treated with Neoadjuvant Therapy Followed by Esophagectomy.Frontiers in surgery · 2022Article
- Development and validation of a machine learning model for survival risk stratification after esophageal cancer surgery.Frontiers in oncology · 2022Article
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Authors and funding
10 authors.
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No grant is acknowledged in the PubMed record.
Abstract
This study aimed to build up nomogram models to evaluate overall survival (OS) and cancer-specific survival (CSS) in early-onset esophageal cancer (EOEC). Patients diagnosed with esophageal cancer (EC) from 2004 to 2015 were extracted from the Surveillance Epidemiology and End Results (SEER) database. Clinicopathological characteristics of younger versus older patients were compared, and survival analysis was performed in both groups. Independent related factors influencing the prognosis of EOEC were identified by univariate and multivariate Cox analysis, which were incorporated to construct a nomogram. The predictive capability of the nomogram was estimated by the concordance index (C-index), calibration plot, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA). A total of 534 younger and 17,243 older patients were available from the SEER database. Younger patients were randomly segmented into a training set (n = 266) and a validation set (n = 268). In terms of the training set, the C-index of the OS nomogram was 0.740 (95% CI: 0.707-0.773), and that of the CSS nomogram was 0.752 (95% CI: 0.719-0.785). In view of the validation set, the C-index of OS and CSS were 0.706 (95% CI: 0.671-0.741) and 0.723 (95% CI: 0.690-0.756), respectively. Calibration curves demonstrated the consistent degree of fit between actual and predicted values in nomogram models. From the perspective of DCA, the nomogram models were more beneficial than the tumor-node-metastasis (TNM) and the SEER stage for EOEC. In brief, the nomogram model can be considered as an individualized quantitative tool to predict the prognosis of EOEC patients to assist clinicians in making treatment decisions.
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