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A tool for predicting overall survival in patients with Ewing sarcoma: a multicenter retrospective study

OBJECTIVE: The aim of this study was to establish and validate a clinical prediction model for assessing the risk of metastasis and patient survival in Ewing's sarcoma (ES). METHODS: Patients diagnosed with ES from the Surveillance, Epidemiology and End Results (SEER) database for the period 20...

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Detalles Bibliográficos
Autores principales: Li, Wenle, Dong, Shengtao, Lin, Yuewei, Wu, Huitao, Chen, Mengfei, Qin, Chuan, Li, Kelin, Zhang, JunYan, Tang, Zhi-Ri, Wang, Haosheng, Huo, Kang, Xie, Xiangtao, Hu, Zhaohui, Kuang, Sirui, Yin, Chengliang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9400324/
https://www.ncbi.nlm.nih.gov/pubmed/35999524
http://dx.doi.org/10.1186/s12885-022-09796-7
Descripción
Sumario:OBJECTIVE: The aim of this study was to establish and validate a clinical prediction model for assessing the risk of metastasis and patient survival in Ewing's sarcoma (ES). METHODS: Patients diagnosed with ES from the Surveillance, Epidemiology and End Results (SEER) database for the period 2010-2016 were extracted, and the data after exclusion of vacant terms was used as the training set (n=767). Prediction models predicting patients' overall survival (OS) at 1 and 3 years were created by cox regression analysis and visualized using Nomogram and web calculator. Multicenter data from four medical institutions were used as the validation set (n=51), and the model consistency was verified using calibration plots, and receiver operating characteristic (ROC) verified the predictive ability of the model. Finally, a clinical decision curve was used to demonstrate the clinical utility of the model. RESULTS: The results of multivariate cox regression showed that age, , bone metastasis, tumor size, and chemotherapy were independent prognostic factors of ES patients. Internal and external validation results: calibration plots showed that the model had a good agreement for patient survival at 1 and 3 years; ROC showed that it possessed a good predictive ability and clinical decision curve proved that it possessed good clinical utility. CONCLUSIONS: The tool built in this paper to predict 1- and 3-year survival in ES patients (https://drwenleli0910.shinyapps.io/EwingApp/) has a good identification and predictive power. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12885-022-09796-7.