Automated body-composition analysis from preoperative PET/CT improves disease-free survival risk stratification in HER2-negative breast cancer, according to a study evaluating predictive performance beyond tumor metabolism. The work positions body-level metrics as actionable complements to standard PET signal features for earlier recurrence risk assessment. By extracting quantitative features from routine scans, the approach could streamline risk modeling in settings where clinicians already obtain PET/CT for staging or workup. The report emphasizes improved prognostic accuracy, implying better separation of recurrence risk categories. For oncology teams, the finding supports continued movement toward scan-derived, multiparametric prognostic models rather than single-modality biomarkers.