{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T06:05:29Z","timestamp":1784786729007,"version":"3.55.0"},"reference-count":50,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T00:00:00Z","timestamp":1784764800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Oncol."],"abstract":"<jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>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\u20130.9895). The small training-test AUC difference (0.0179) indicated no severe overfitting. SVM ranked second (test AUC\u00a0=\u00a00.9405), while XGBoost and RF exhibited significant overfitting and LightGBM underfitting. The model\u2019s decision relied on key texture features; only GLSZM non-uniformity differed significantly between groups, consistent with their pathophysiological characteristics.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fonc.2026.1908570","type":"journal-article","created":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T05:37:46Z","timestamp":1784785066000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Radiomics-based high-resolution CT analysis for differentiating primary tumor sources of pulmonary metastases"],"prefix":"10.3389","volume":"16","author":[{"given":"Kangning","family":"Liu","sequence":"first","affiliation":[{"name":"Xuzhou Medical University","place":["Xuzhou, Jiangsu, China"]},{"name":"Xuzhou Central Hospital Affiliated to Medical School of Southeast University","place":["Xuzhou, Jiangsu, 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