Incremental prognostic value of immune cell densities beyond clinical parameters in non-small cell lung cancer.

[BACKGROUND] Multiplex immunofluorescence imaging enables detailed characterization of the tumor immune microenvironment, but whether immune cell densities add prognostic value beyond established clinical factors in non-small cell lung cancer (NSCLC) remains unclear.

[METHODS] Tissue samples from an NSCLC cohort (n = 298) were stained with a multiplex immunofluorescence panel targeting immune cell markers (CD4, CD8, FoxP3, CD20), cancer cells (pan-cytokeratin), and cell nuclei (DAPI). We quantified immune cell densities, nuclear pleomorphism features, and clinical variables, and trained four machine learning models (logistic regression, random forest, support vector machine, and k-nearest neighbors) to predict overall survival.

[RESULTS] Clinical parameters consistently demonstrated the strongest performance in predicting long and short-term survival (logistic regression mean accuracy 0.60 ± 0.01, AUC 0.66 ± 0.01). The addition of immune cell densities revealed a small, statistically significant improvement in survival prediction (accuracy 0.62 ± 0.01, p < 0.01, AUC 0.67 ± 0.01, p = 0.04), while nuclear pleomorphism features did not improve prediction. When combined with clinical parameters, immune cell densities also improved survival stratification in Cox regression analyses numerically (HR = 0.51 vs. 0.55 for clinical parameters alone). Model interpretation analyses showed that stage and performance status have the largest effect on model performance. Selected immune cell densities (tumor CD4-helper and stroma B-cells) have a limited but consistent effect.

[CONCLUSION] Clinical parameters remain the dominant predictors of outcome in NSCLC, with immune cell densities providing only limited prognostic value for clinical stratification. The openly available code and datasets present a unique resource for method development or focused analysis.