Modality-level attribution and redundancy-aware radiomics for MRI-based differentiation of melanoma and NSCLC brain...

Differentiating the primary source of brain metastases using imaging alone is difficult, especially when intracranial disease is the initial sign of cancer. This study aimed to develop and validate a redundancy-aware, explainable radiomics framework for distinguishing melanoma from non-small cell lung cancer (NSCLC) brain metastases through multiparametric MRI.

Lesion-level radiomic features were extracted from T1, contrast-enhanced T1 (T1CE), T2, and FLAIR sequences. Correlation-based redundancy filtering and L1-regularized feature ranking were applied to minimize collinearity among features. A structured parametric analysis across progressively larger top-K feature subsets was performed to evaluate performance stability and modality-specific contribution patterns. Multiple machine learning classifiers were tested using cross-validation and an independent test set, with model interpretability assessed via TreeSHAP to quantify feature- and modality-level contributions.

The best-performing configuration achieved a test AUC of 0.75, while RF-200 was selected for the primary tree-based explainability analysis due to its stable AUC-based discrimination. SHAP analysis showed that classification depended on a small set of predictors combining texture and intensity distribution features. FLAIR and T2 features contributed most to model attribution, suggesting that fluid-sensitive sequences capture discrimination-relevant heterogeneity and microenvironmental signals beyond contrast enhancement.

A redundancy-aware, interpretable radiomics approach shows promising preliminary discrimination between melanoma and NSCLC brain metastases and provides structured insight into modality-specific sequence contributions. External validation and prospective reader studies are required before clinical translation.