Background: Osteosarcoma, a primary bone malignancy in children and adolescents, frequently metastasizes to the lungs, contributing significantly to morbidity and mortality. Lung Metastases: At diagnosis, 15–20% of patients present with detectable lung metastases. Chest computed tomography (CT) is vital for the early detection and monitoring of these metastases. Lung involvement typically presents as multiple nodules of varying sizes and can include atypical features such as cavitation, cystic lesions, ground-glass halos, intravascular tumor thrombi, and endobronchial disease. Additional Findings: Pleural metastasis often occurs alongside pulmonary disease, and complications like spontaneous pneumothorax may arise. Additional findings may include thoracic lymphadenopathy, cardiac tumor thrombus, and chest wall deposits. Conclusion: Familiarity with these imaging patterns is essential for radiologists to ensure timely diagnosis and effective management. This review highlights the critical role of chest CT in detecting and characterizing osteosarcoma metastasis.

Objectives: Accurate differentiation between usual interstitial pneumonia (UIP) and nonspecific interstitial pneumonia (NSIP) is crucial for guiding treatment in interstitial lung diseases (ILDs). This study evaluates the efficacy of clinical, radiomic, and combined models in classifying UIP and NSIP using high-resolution computed tomography (HRCT) scans. Materials and Methods: A retrospective analysis was performed on 105 HRCT scans (UIP = 60, NSIP = 45) from Faisal Hospital and Research Center. Demographic and pulmonary function data formed the clinical model. Radiomic features, extracted using the pyRadiomics package, were refined using recursive feature elimination. A combined model was developed by integrating clinical and radiomic features to assess their complementary diagnostic value. Model performance was assessed via the area under the receiver operating characteristic curve (AUC). SHapley Additive exPlanations (SHAP) analysis, including both global feature importance and individual-level explanations, was used to interpret the model predictions. Results: The clinical model achieved an AUC of 0.62 with a sensitivity of 54% and a specificity of 78%. The radiomic model outperformed it with an AUC of 0.90 with a sensitivity and specificity above 85%. The combined model showed an AUC of 0.86 with a sensitivity of 88% and a specificity of 78%. SHAP analysis identified texture-based features, such as GLCM_Idmn and NGTDM_Contrast, as influential for classification. Conclusions: Radiomic features enhance classification accuracy for UIP and NSIP compared to clinical models. Integrating HCR into clinical workflows may reduce variability and improve diagnostic accuracy in ILD. Future studies should validate findings using larger, multicenter datasets.