Radiomics-based high-resolution CT analysis for differentiating primary tumor sources of pulmonary metastases.

Objective: To evaluate machine learning models based on HRCT radiomic features for distinguishing breast and colorectal cancer pulmonary metastases, and interpret the optimal model to aid clinical decision-making. Methods: This retrospective study enrolled 85 patients with pathologically confirmed pulmonary metastases. After radiomic feature extraction, the cohort was divided into a training set (n=59) and an independent test set (n=26) at a 7:3 ratio via stratified sampling. Data were processed with Z-score normalization, variance thresholding and PCA (45 principal components). Five classifiers were constructed: LR, linear SVM, RF, XGBoost and LightGBM. Model stability and performance were assessed by 5-fold stratified cross-validation and independent test validation. Results: The 45 principal components accounted for 99.92% of cumulative variance. LR showed optimal performance, with a test AUC of 0.9821, classification accuracy of 84.62%, and a mean cross-validation AUC of 0.9606 (95% CI: 0.8887-0.9895). The small training-test AUC difference (0.0179) indicated no severe overfitting. SVM ranked second (test AUC = 0.9405), while XGBoost and RF exhibited significant overfitting and LightGBM underfitting. The model's decision relied on key texture features; only GLSZM non-uniformity differed significantly between groups, consistent with their pathophysiological characteristics. Conclusion: The PCA-reduced and regularization-optimized LR model has excellent generalization, stability and clinical interpretability in differentiating pulmonary metastases from breast and colorectal cancers. This study preliminarily highlights the potential of LR in high-dimensional small-sample scenarios, and provides a foundational methodological reference for future large-scale multicenter diagnostic research.