ArticleNeurosurgical review2024
Risk factors, prognostic factors, and nomograms for synchronous brain metastases of solid tumors: a population-based study.
Article in Neurosurgical review, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Predicting Survival Rates in Brain Metastases Patients from Non-Small Cell Lung Cancer Using Radiomic Signatures Associated with Tumor Immune Heterogeneity.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
Corrections and comments
- Erratum issued
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In previous literatures, we found that similar studies on the short-term prognosis of synchronous brain metastases (S-BM) from other systems are rare. Our aim was to evaluate the early mortality rate of patients with S-BM from the Surveillance, Epidemiology, and End Result (SEER) database and explore the risk factors for early mortality (≤ 1 year). We used Kaplan-Meier (KM) curves to evaluate early mortality in patients with S-BM from the SEER database. Logistic regression analyses were used to identify significant independent prognostic factors in patients with a follow-up time > 12 months. And the meaningful factors were used to construct a nomogram of overall early death. The receiver operating characteristic (ROC) curve was used to test the predictive ability of the model, while the decision curve analysis (DCA) curve was used to validate the clinical application ability of the model. A total of 47,284 patients were used for univariate and multivariate logistic regression analysis to screen variables to constructing a nomogram. In the all-cause early mortality specific model, the area under the ROC (AUC) curve of the training set was 0.764 (95% confidence interval (CI): 0.758-0.769), and the AUC of the validation set was 0.761 (95% CI: 0.752-0.770). The DCA calibration curves of the training set and validation set indicate that the 1-year early mortality rate predicted by this model is consistent with the actual situation. We found that the 1-year early mortality rate was 76.4%. We constructed a validated nomogram using these covariates to effectively predict 1-year early mortality in patients with S-BM. This nomogram can help clinical workers screen high-risk patients to develop more reasonable treatment plans.
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Registered trials
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