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09 April 2020 : Clinical Research  

Construction and Validation of Nomograms for Predicting the Prognosis of Uterine Leiomyosarcoma: A Population-Based Study

Yue Meng1ABCDEF, Yuebo Yang1ACDF, Yu Zhang1ACDF, Xiaomao Li1ADG*

DOI: 10.12659/MSM.922739

Med Sci Monit 2020; 26:e922739

Abstract

BACKGROUND: Uterine leiomyosarcoma (uLMS) is a rare female malignancy with poor survival rates. The objective of this study was to construct prognostic nomograms for predicting the prognosis of women with uLMS.

MATERIAL AND METHODS: Patients with uLMS diagnosed between 2004 and 2015 were identified in the Surveillance, Epidemiology, and End Results (SEER) database. The essential clinical predictors were identified via univariate and multivariate Cox analysis models. Nomograms were constructed to predict the 3- and 5-year cancer-specific survival (CSS) and overall survival (OS) rates. Concordance index (C-index) and calibration plots were constructed to validate the predictive performance of nomograms.

RESULTS: We enrolled 1448 patients with uLMS from the SEER database, with 1016 categorized into a training set and 432 categorized into a validation set. In multivariate analysis of the training set, predictors including age, disease stage, histological grade, tumor size, and surgery type were found to be associated with OS and CSS. Race and chemotherapy were only associated with OS. Construction of nomograms based on these predictors was performed to evaluate the prognosis of uLMS patients. The C-index and calibration curves also showed the satisfactory performance of these nomograms for prediction of prognosis.

CONCLUSIONS: The developed nomograms are useful tools for precisely analyzing the prognosis of uLMS patients, which could help clinicians in making personalized survival predictions and assessing individualized clinical options.

Keywords: Leiomyosarcoma, nomograms, SEER Program, Uterine Neoplasms, Aged, 80 and over, Multivariate Analysis, Survival Rate

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Medical Science Monitor eISSN: 1643-3750
Medical Science Monitor eISSN: 1643-3750