ArticleTranslational cancer research2026
Development and external validation of an XGBoost-based prognostic model for small cell lung cancer with brain metastases.
Article in Translational cancer research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Patients with small cell lung cancer (SCLC) and brain metastases (BM) have poor and heterogeneous survival outcomes, while existing prognostic tools have shown limited discriminatory performance. We aimed to develop an extreme gradient boosting (XGBoost) based prognostic model and perform preliminary external validation. Methods: Patients with small cell lung cancer and brain metastases (SCLCBM) diagnosed between 2010 and 2020 were identified from the Surveillance, Epidemiology, and End Results (SEER) database for model development and internal testing. External validation was performed in an independent single-center Chinese cohort. Variables screened by univariable Cox regression and retained in multivariable Cox regression analyses were used as predictors in the XGBoost models. Grid search was used for hyperparameter tuning, and candidate parameter combinations were evaluated using 10-fold cross-validation. Model performance was assessed using the areas under the curves (AUCs) and confusion matrices, and SHapley Additive exPlanations (SHAP) analysis was used to interpret model predictions. Results: The study included 7,692 patients from the SEER database and 128 patients in the external validation cohort. The final models incorporated demographic, tumor-related, treatment-related, and metastatic factors, including age, sex, T and N stages, chemotherapy, radiotherapy, surgery, and extracranial metastases. AUCs for 3-, 6-, 12-, and 18-month overall survival (OS) were 0.840, 0.801, 0.738, and 0.759 in the internal testing cohort and 0.779, 0.790, 0.739, and 0.763 in the external cohort, respectively. SHAP analysis consistently ranked chemotherapy as the most influential predictor. Conclusions: The XGBoost models showed moderate discrimination across the evaluated prediction horizons in both the internal testing cohort and a small single-center external cohort. Further validation in larger, prospective, multicenter cohorts is required before clinical use.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.