ArticleFrontiers in surgery2022
Risk factors for distant metastasis and prognosis in stage T1 esophageal cancer: A population-based study.
Article in Frontiers in surgery, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper 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
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4 authors.
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Abstract
Purpose: Stage T1 esophageal cancer (EC) with distant metastasis (DM) is rare and poorly understood. In this study, we aimed to construct and validate a novel nomogram for predicting the probability of DM in T1 EC patients. Methods: A total of 1,663 eligible T1 EC patients were enrolled from the Surveillance, Epidemiology, and End Results (SEER) database between 2004 and 2015. The patients were randomly divided into training and validation cohorts. Univariate and multivariate logistic analyses in the training cohort were used to identify risk factors related to DM, and then these risk factors were applied to construct the nomogram. Receiver operating characteristic (ROC) curves, the area under the curve (AUC), calibration plots, the Hosmer-Lemeshow (HL) test, and decision curve analysis (DCA) were used to evaluate the nomogram. Results: Among the 1,663 patients identified, 143 (8.6%) had DM. Five risk factors (tumor location, lymph node status, tumor length, T1 subtype, and grade) were significant predictors of DM. The AUC values were 0.828 and 0.851 in the training cohort and validation cohort, respectively, revealing good discrimination. The calibration plots in the training cohort and validation cohort both showed good consistency. DCA showed that the nomogram was clinically effective. In addition, the nomogram has a good risk stratification ability to identify patients with different risks according to the nomogram score. In terms of survival analysis, univariate and multivariate Cox analyses showed that age, race, tumor length, grade, lymph node status, M stage and treatment were significant prognostic factors for overall survival (OS). For cancer-specific survival (CSS), the independent prognostic factors were age, tumor length, histology, grade, lymph node status, M stage and treatment. Conclusion: The nomogram could effectively predict the probability of DM in T1 EC patients. It can aid clinicians in detecting high-risk patients and making individual clinical decisions.
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