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Nomograms for estimating survival in patients with papillary thyroid cancer after surgery
Background: The aim of this study was to develop and validate nomograms to predict the survival in patients with papillary thyroid cancer (PTC). Patients and methods: Adult patients who were surgically treated for PTC were selected from the Surveillance, Epidemiology and End Results (SEER) program (...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Dove
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6497889/ https://www.ncbi.nlm.nih.gov/pubmed/31114382 http://dx.doi.org/10.2147/CMAR.S194366 |
Sumario: | Background: The aim of this study was to develop and validate nomograms to predict the survival in patients with papillary thyroid cancer (PTC). Patients and methods: Adult patients who were surgically treated for PTC were selected from the Surveillance, Epidemiology and End Results (SEER) program (2004–2013). A multivariate analysis using the Cox proportional hazards regression was performed, and nomograms for predicting 10-year overall survival (OS) and cancer-specific survival (CSS) were constructed. The discrimination and calibration plots were used to measure the accuracy of the nomograms. Results: The records of 63,219 patients with PTC were retrospectively analyzed. Nine independent factors including age, race, sex, marital status, tumor size, extrathyroidal extension, radioactive iodine, T stage, and M stage were assembled into the OS nomogram. A nomogram predicting CSS was constructed based on eight factors (age, sex, marital status, tumor size, extrathyroidal extension, T stage, N stage, and M stage). With respect to the training set, the nomograms displayed improved discrimination power compared to the TNM staging system (6th edition) in both sets. The calibration curve for the probability of survival showed agreement between the predictive nomograms and the actual observation. Conclusion: We have successfully developed prognostic nomograms to predict OS and CSS for PTC with excellent discrimination and calibration. |
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