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- W3170692783 abstract "Abstract Background: To develop a comprehensive nomogram for predicting the cancer-specific survival (CSS) for uterine sarcoma (US). Methods: 3861 patients of US between 2010 to 2015 were identified for this study from the Surveillance, Epidemiology, and End Results (SEER) database. They were randomly divided into a training cohort (n = 2702) and a validation cohort (n = 1159) in a 7-to-3 ratio by R software. Multivariate Cox regression analysis was performed to select predictive variables and then to identify independent prognostic factors. The concordance index (C-index), the area under the time-dependent receiver operating characteristics curve (AUC), the net reclassification improvement (NRI), the integrated discrimination improvement (IDI), calibration plotting, and decision-curve analysis (DCA) were used to compare the new survival nomogram with the AJCC 7th edition prognosis model. Results: We have established a nomogram for determining the 1-, 3-, and 5-year CSS probabilities of US patients. In this nomogram, pathology grade has the highest risk on CSS in US, followed by the age at diagnosis, then surgery status. The C-index for the nomogram (0.796, 0.767 for the training and validation cohort, respectively) was higher than those for the AJCC staging system (0.706 and 0.713, respectively). Furthermore, AUC value, NRI, IDI, calibration plotting, and DCA showed that this nomogram exhibited better performance than the AJCC staging system alone. Conclusion: Our study validated the first comprehensive nomogram for US which could provide more accurately and individualized survival predictions for US patients in clinical practice." @default.
- W3170692783 created "2021-06-22" @default.
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- W3170692783 date "2021-06-14" @default.
- W3170692783 modified "2023-09-25" @default.
- W3170692783 title "A Novel Nomogram for Predicting Cancer-Specific Survival in Women with Uterine Sarcoma: A Large Population-Based Study" @default.
- W3170692783 doi "https://doi.org/10.21203/rs.3.rs-561996/v1" @default.
- W3170692783 hasPublicationYear "2021" @default.
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