ArticleFrontiers in oncology2021
A Population-Based Study: How to Identify High-Risk T1-2 Esophageal Cancer 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 5 papers.
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Who cites it
5 citing papers in PubMed.
- Machine learning to predict distant metastasis risk and prognostic analysis in T1-2 esophageal cancer: a study based on the SEER database.Journal of thoracic disease · 2026Article
- Fivefold Cross-Validation Approach in Evaluating the Robustness of Machine Learning Models for Prediction of Esophageal Cancer.Indian journal of community medicine : official publication of Indian Association of Preventive & Social Medicine · 2026Article
- Construction and validation of a risk-scoring model to predict lymph node metastasis in T1b-T2 esophageal cancer.Surgical endoscopy · 2024Article
- Systematic review and meta-analysis of endoscopic ultrasonography in staging diagnosis of esophageal cancer after neoadjuvant radiotherapy and chemotherapy.Journal of gastrointestinal oncology · 2022Article
- Nomogram models for the prognosis of cervical cancer: A SEER-based study.Frontiers in oncology · 2022Article
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Authors and funding
9 authors.
Funding
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Abstract
backgroundDue to individualized conditions of lymph node metastasis (LNM) and distant metastasis (DM), the following therapeutic strategy and diagnosis of T1-2 esophageal cancer (ESCA) patients are varied. A prediction model for identifying risk factors for LNM, DM, and overall survival (OS) of high-risk T1-2 ESCA patients is of great significance to clinical practice.
methodsA total of 1,747 T1-2 ESCA patients screened from the surveillance, epidemiology, and end results (SEER) database were retrospectively analyzed for their clinical data. Univariate and multivariate logistic regression models were established to screen out risk factors for LNM and DM of T1-2 ESCA patients, while those of OS were screened out using the Cox regression analysis. The identified risk factors for LNM, DM, and OS were then subjected to the establishment of three nomograms, respectively. The accuracy of the nomograms was evaluated by depicting the calibration curve, and the predictive value and clinical utility were evaluated by depicting the clinical impact curve (CIC) and decision curve analysis (DCA), respectively.
resultsThe age, race, tumor grade, tumor size, and T-stage were significant factors for predicting LNM of T1-2 ESCA patients (
conclusionsThe newly constructed nomograms can objectively and accurately predict the LNM, DM, and OS of T1-2 ESCA patients, which contribute to the individualized decision making before clinical management.
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