AI Article Synopsis
- The study aimed to create and validate a clinical prediction model for evaluating metastasis risk and survival rates in Ewing's sarcoma (ES) patients based on data from the SEER database.
- The researchers used Cox regression analysis on a training set of 767 patients and validated the model with another group of 51 patients from different medical institutions, confirming its effectiveness through various validation techniques.
- The findings indicated that factors like age, bone metastasis, tumor size, and chemotherapy are significant predictors of survival, and the developed tool shows strong potential for practical clinical use in predicting 1- and 3-year survival outcomes for ES patients.
Article Abstract
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.
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Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9400324 | PMC |
| http://dx.doi.org/10.1186/s12885-022-09796-7 | DOI Listing |
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