KEY TAKEAWAYS
- The study aimed to develop and validate a survival prediction model for patients with NSCLC.
- The results showed that the LASSO model effectively predicts survival in advanced NSCLC and aids treatment decisions.
Lung cancer continues to be a major global health issue, with non-small cell lung cancer (NSCLC) as the most common subtype. Despite treatment advancements, the prognosis for patients with advanced NSCLC remains poor, highlighting the need for accurate prognostic assessment models.
Yimeng Guo and the team aimed to develop and validate a survival prediction model for patients with NSCLC.
The study randomly assigned 523 patients into a training dataset (n=313) and a validation dataset (n=210). Initial variable selection was performed using univariate Cox regression, LASSO regression, and random survival forest (RSF) analysis. Multivariate Cox regression was then applied to the variables selected by each method to build the final predictive models.
The optimal model was chosen based on the highest bootstrap C-index in the validation dataset. Predictive performance was further assessed using time-dependent receiver operating characteristic (Time-ROC) curves, calibration plots, and decision curve analysis (DCA).
The results showed that the LASSO regression model, incorporating N stage, neutrophil-lymphocyte ratio (NLR), D-dimer, neuron-specific enolase (NSE), squamous cell carcinoma antigen (SCC), driver alterations, and first-line treatment, achieved the highest bootstrap C-index of 0.668 (95% CI: 0.626-0.722) among the 3 models tested.
This model demonstrated strong discrimination in the validation dataset, with area under the ROC curve (AUC) values of 0.707 (95% CI: 0.633-0.781) for 1-year survival, 0.691 (95% CI: 0.616-0.765) for 2-year survival, and 0.696 (95% CI: 0.611-0.781) for 3-year survival predictions.
Calibration plots showed good agreement between predicted and observed survival probabilities, and decision curve analysis indicated that the model offers clinical benefit across various decision thresholds.
The study concluded that the LASSO regression model demonstrated strong performance in predicting survival outcomes for patients with advanced NSCLC. This model can help clinicians make more informed treatment decisions and serves as a valuable tool for patient risk stratification and personalized management.
This work was supported by The Project of the Central Government Guiding Local in Shanxi Province (grant number YDZJSX2022B012).
Source: https://pubmed.ncbi.nlm.nih.gov/39156899/
Guo Y, Li L, Zheng K, et al. (2024). “Development and validation of a survival prediction model for patients with advanced non-small cell lung cancer based on LASSO regression.” Front Immunol. 2024;15:1431150. Published 2024 Aug 2. doi:10.3389/fimmu.2024.1431150
