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This study presents a novel compressed sensing (CS)\u2010based feature selection method, offering distinct advantages over conventional approaches such as the Least Absolute Shrinkage and Selection Operator (Lasso). Two feature selection models are developed\u2014binary and Gaussian random projections\u2014and applied them to a brain metastasis dataset. Using the top five features identified, support vector machine models are trained to classify whether lesions achieved \u226520% volume reduction at a 3\u2010month follow\u2010up. Integrating residual error with frequency\u2010based selection further enhances performance over weight coefficient\u2010based criteria. For 5\u2010feature sets, the CS\u2010Binary model outperforms Lasso across multiple metrics: AUC (0.937 vs 0.890), balanced accuracy (87.3% vs 83.0%), F1 score (79.4% vs 75.9%), Kappa coefficient (75.8% vs 69.0%), and Matthews correlation coefficient (78.0% vs 72.1%). The CS\u2010based framework shows great potential in streamlining feature selection and improving predictive accuracy, particularly beneficial in two scenarios: 1) early phase clinical trials with small datasets where traditional radiomics methods are prone to overfitting; and 2) emphasizing the selection of the most prognostically relevant features to help improve interpretability.<\/jats:p>","DOI":"10.1002\/aisy.202500116","type":"journal-article","created":{"date-parts":[[2025,6,22]],"date-time":"2025-06-22T22:50:26Z","timestamp":1750632626000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Leveraging Compressed Sensing and Radiomics for Robust Feature Selection for Outcome Prediction in Personalized Ultra\u2010Fractionated Stereotactic Adaptive 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