AI Article Synopsis
- The study aimed to create a prognostic model for predicting 5- and 10-year survival rates in patients with ependymoma (EPN) using data from the SEER database.
- Seven key survival factors (age, gender, morphology, location, size, laterality, and resection) were identified through LASSO regression.
- The resulting nomogram showed moderate accuracy for predicting outcomes, which can help clinicians tailor treatment plans for EPN patients.
Article Abstract
Background: The prognostic factors for survival in patients with ependymoma (EPN) remain controversial. The aim of this study was to establish a prognostic model for 5- and 10-year survival probability nomograms for patients with EPN.
Methods: Clinical data from the Surveillance, Epidemiology, and End Results (SEER) database were used for patients diagnosed with ependymoma between 2000 and 2018 and were randomized 7:3 into a development set and a validation set. Factors significantly associated with prognosis were screened out using the least absolute shrinkage and selection operator (LASSO) regression. The calibration chart and consistency index (C-index) are used to evaluate the discrimination and consistency of the prediction model. Decision curve analysis (DCA) was used to further evaluate the established model. Finally, prognostic factors selected by LASSO regression were evaluated using Kaplan-Meier (KM) survival curves.
Results: A total of 3820 patients were included in the prognostic model. Seven survival predictors were obtained by LASSO regression screening, including age, gender, morphology, location, size, laterality, and resection. The prognostic model of the nomogram showed moderate discriminative ability in the development group and the validation group, with a C-index of 0.642 and 0.615, respectively. In the development set and validation set survival curves, the prognosis index of high risk was less effective than low risk (p < 0.001).
Conclusions: Our nomograms may play an important role in predicting 5 and 10-year outcomes for patients with ependymoma. This will help assist clinicians in personalized medicine.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8419756 | PMC |
| http://dx.doi.org/10.1002/cam4.4151 | DOI Listing |
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