โก Quick Summary
This study developed and validated a machine learning-based model to predict overall survival (OS) in patients with small cell lung cancer (SCLC) and brain metastases (BM). The Random Survival Forest (RSF) algorithm outperformed others, achieving an AUC of 0.738 for 1-year OS in the training cohort.
๐ Key Details
- ๐ Dataset: 2,392 SCLC patients with BM from the SEER database
- ๐งฉ Features used: Clinical data including chemotherapy, liver metastasis, N stage, and age
- โ๏ธ Technology: Machine learning algorithms including RSF, XGB, Enet, and ANN
- ๐ Performance: RSF achieved AUCs of 0.738 and 0.809 for 1-year and 2-year OS in the training cohort
๐ Key Takeaways
- ๐ The RSF model demonstrated the best performance among the tested algorithms.
- ๐ก Key prognostic features included chemotherapy, liver metastasis, N stage, and age.
- ๐ฅ External validation was conducted with an independent cohort of 85 patients.
- ๐ A web-based calculator was developed for real-time individualized risk prediction.
- ๐ The model showed favorable calibration and the lowest Brier scores across datasets.
- ๐ SHAP analysis provided insights into the model’s interpretability.
- ๐ This research supports personalized treatment planning for SCLC patients with BM.
๐ Background
Brain metastases are a common and severe complication in patients with small cell lung cancer, often leading to poor prognosis. Despite the critical need for effective prognostic tools, existing methods remain underdeveloped. This study aims to fill that gap by leveraging machine learning to enhance survival prediction for this vulnerable patient population.
๐๏ธ Study
The research utilized clinical data from the SEER database, focusing on 2,392 SCLC patients with brain metastases. Various machine learning algorithms, including Cox regression and Random Survival Forest, were employed to construct prognostic models. The study also included an external validation cohort to ensure the robustness of the findings.
๐ Results
The Random Survival Forest model emerged as the most effective, achieving an AUC of 0.738 for 1-year OS and 0.809 for 2-year OS in the training cohort. In the internal validation cohort, the AUCs were 0.718 and 0.748, while the external validation cohort yielded AUCs of 0.686 and 0.802. The model also demonstrated excellent calibration and the lowest Brier scores across all datasets.
๐ Impact and Implications
This study’s findings have significant implications for the management of small cell lung cancer patients with brain metastases. By providing a robust and interpretable model for predicting overall survival, healthcare professionals can make more informed decisions regarding treatment options. The availability of a web-based calculator further enhances the model’s accessibility, paving the way for personalized treatment planning.
๐ฎ Conclusion
This research highlights the potential of machine learning in improving survival predictions for SCLC patients with brain metastases. The development of the RSF-based model not only offers clinically relevant insights but also supports the integration of personalized approaches in treatment planning. Continued exploration in this field could lead to better outcomes for patients facing this challenging diagnosis.
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Interpretable Machine Learning for Survival Prediction in Small Cell Lung Cancer Patients With Brain Metastases: A Population-Based Study With External Validation.
Abstract
IntroductionBrain metastases (BM) represent a common and fatal progression in small cell lung cancer (SCLC), yet prognostic tools for this population remain underdeveloped. This study aimed to establish and externally validate a machine learning-based model to predict overall survival (OS) in SCLC patients with BM.MethodsWe extracted clinical data from 2392 SCLC patients with BM from the SEER database to construct prognostic models using Cox regression, AJCC staging, and four machine learning algorithms: Random Survival Forest (RSF), Extreme Gradient Boosting (XGB), Elastic Net (Enet), and Artificial Neural Network (ANN). Key features were selected via Lasso-Cox regression. Model performance was evaluated using time-dependent AUC, calibration curves, Brier scores, precision-recall (PR) curves, and decision curve analysis (DCA). SHAP and partial dependence plots were applied for model interpretability. External validation was conducted using an independent hospital-based cohort of 85 patients, with comparability to the SEER cohort addressed through inverse probability of treatment weighting (IPTW).ResultsAmong all models, the RSF algorithm demonstrated the best overall performance. In the training cohort, it achieved AUCs of 0.738 and 0.809 for 1-year and 2-year OS, respectively. In the internal validation cohort, AUCs were 0.718 and 0.748, and in the external validation cohort, 0.686 and 0.802, respectively. The RSF model also showed favorable calibration and the lowest Brier scores across datasets. SHAP analysis ranked chemotherapy, liver metastasis, N stage, and age as the most influential prognostic features. A web-based calculator was developed to enable real-time individualized risk prediction.ConclusionsThis study presents a robust, interpretable, and externally validated RSF-based model for predicting OS in SCLC patients with BM. The model offers clinically relevant insights and is accessible via an online tool, supporting its potential integration into personalized treatment planning.
Author: [‘Luo N’, ‘Tan S’, ‘Li X’, ‘Liu S’, ‘Xie S’, ‘Huang X’, ‘Wu D’]
Journal: Cancer Control
Citation: Luo N, et al. Interpretable Machine Learning for Survival Prediction in Small Cell Lung Cancer Patients With Brain Metastases: A Population-Based Study With External Validation. Interpretable Machine Learning for Survival Prediction in Small Cell Lung Cancer Patients With Brain Metastases: A Population-Based Study With External Validation. 2026; 33:10732748261419190. doi: 10.1177/10732748261419190