A novel nomogram for predicting cancer-specific survival in women with uterine sarcoma: a large population-based study

Abstract

Background: Uterine sarcoma (US) is a rare malignant uterine tumor with aggressive behavior and rapid progression. The purpose of this study was to constructa comprehensive nomogram to predict cancer-specific survival (CSS) of patients with US-based on the Surveillance, Epidemiology, and End Results (SEER) database.

Methods: A retrospective population-based study was conducted using data from patients with US between 2010 and 2015 from the SEER database. They were randomly divided into a training cohort and a validation cohort ata 7-to-3 ratio. Multivariate Cox analysis was performed to identify independent prognostic factors. Subsequently, a nomogram was established to predict patient CSS. The discrimination and calibration of the nomogram were evaluated by the concordance index (C-index) and the area under the curve (AUC). Finally, net reclassification improvement (NRI), integrated discrimination improvement (IDI), calibration plotting, and decision-curve analysis (DCA) were used to evaluate the benefits of the new prediction model.

Results: A total of 3861 patients with US were included in our study. As revealed in multivariate Cox analysis, age at diagnosis, race, marital status, insurance record, tumor size, pathology grade, histological type, SEER stage, AJCC stage, surgery status, radiotherapy status, and chemotherapy status were found to be independent prognostic factors. In our nomogram, pathology grade had strongest correlation with CSS, followed by age at diagnosis and surgery status. Compared to the AJCC staging system, the new nomogram showed better predictive discrimination with a higher C-index in the training and validation cohorts (0.796 and 0.767 vs. 0.706 and 0.713, respectively). Furthermore, the AUC value, calibration plotting, NRI, IDI, and DCA also demonstrated better performance than the traditional system.

Conclusion: Our study validated the first comprehensive nomogram for US, which could provide more accurate and individualized survival predictions for US patients in clinical practice.

Keywords: Cancer-specific survival; Nomogram; SEER database; Uterine sarcoma.

Fig. 1

Patient selection flowchart. SEER, Surveillance, Epidemiology, and End Result Program; ICD-O-3, International Classification of Disease for Oncology, Third Edition

Fig. 2

The nomogram for predicting 1-, 3- and 5-year survival of US. AJCC, 7th AJCC tumor stage

Fig. 3

ROC curve analyses were generated to test the performance evaluation between the new model and the traditional AJCC model, by the AUC. A, B and C came from the training set, and D, E, and F came from the validation set

Fig. 4

Calibration curves for 1-, 3- and 5-year CSS depict the calibration of each model in terms of the agreement between the predicted probabilities and observed outcomes of the training cohort (A, C, E) and validation cohort (B, D, F)

Fig. 5

Decision curve analysis of the training (A, B, C) and validation cohorts (D, E, F)