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Development and Validation of a Prognostic Model to Predict the Prognosis of Patients With Retroperitoneal Liposarcoma: A Large International Population-Based Cohort Study
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Abstract
Background
Retroperitoneal liposarcomas (RPLs), sarcoma of mesenchymal origin, are the most common soft tissue sarcomas (STS) of the retroperitoneum. Given the rarity of RPLs, the prognostic values of clinicopathological features in the patients remain unclear. The nomogram can provide a visual interface to aid in calculating the predicted probability that a patient will achieve a particular clinical endpoint and communication with patients.
Methods
We included a total of 1,392 RPLs patients diagnosed between 2004 and 2015 from the Surveillance, Epidemiology, and End Results (SEER) database. For nomogram construction and validation, patients in the SEER database were divided randomly into the training cohort and internal validation cohort at a ratio of 7:3, while 65 patients with RPLs from our center between 2010 and 2016 served as the external validation cohort. The OS curves were drawn using the Kaplan–Meier method and assessed using the log-rank test. Moreover, Fine and Gray’s competing-risk regression models were conducted to assess CSS. Univariate and multivariate analyses were performed to select the prognostic factors for survival time. We constructed a predictive nomogram based on the results of the multivariate analyses.
Results
Through univariate and multivariate analyses, it is found that age, histological grade, classification, SEER stage, surgery constitute significant risk factors for OS, and age, classification, SEER stage, AJCC M stage, surgery, and tumor size constitute risk factors for CSS. We found that the nomogram provided a good assessment of OS and CSS at 1, 3, and 5 years in patients with RPLs (1-year OS: (training cohort: AUC = 0.755 ( 95% CI, 0.714, 0.796); internal validation cohort: AUC = 0.754 ( 95% CI, 0.681, 0.827); external validation cohort: AUC = 0.793 ( 95% CI, 0.651, 0.935)); 3-year OS: (training cohort: AUC = 0.782 ( 95% CI, 0.752, 0.811); internal validation cohort: AUC = 0.788 ( 95% CI, 0.736, 0.841); external validation cohort: AUC = 0.863 ( 95% CI, 0.773, 0.954)); 5-year OS: (training cohort: AUC = 0.780 ( 95% CI, 0.752, 0.808); internal validation cohort: AUC = 0.783 ( 95% CI, 0.732, 0.834); external validation cohort: AUC = 0.854 ( 95% CI, 0.762, 0.945)); 1-year CSS: (training cohort: AUC = 0.769 ( 95% CI, 0.717, 0.821); internal validation cohort: AUC = 0.753 ( 95% CI, 0.668, 0.838); external validation cohort: AUC = 0.799 ( 95% CI, 0.616, 0.981)); 3-year CSS: (training cohort: AUC = 0.777 ( 95% CI, 0.742, 0.811); internal validation cohort: AUC = 0.787 ( 95% CI, 0.726, 0.849); external validation cohort: AUC = 0.808 ( 95% CI, 0.673, 0.943)); 5-year CSS: (training cohort: AUC = 0.773 ( 95% CI, 0.741, 0.805); internal validation cohort: AUC = 0.768 ( 95% CI, 0.709, 0.827); external validation cohort: AUC = 0.829 ( 95% CI, 0.712, 0.945))). The calibration plots for the training, internal validation, and external validation cohorts at 1-, 3-, and 5-year OS and CSS indicated that the predicted survival rates closely correspond to the actual survival rates.
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Most cited references52
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