CT-Derived Radiomic Features for the Non-Invasive Differentiation of Mediastinal Lymphadenopathy in Lung Cancer and Sarcoidosis.
Authors
Affiliations (5)
Affiliations (5)
- Department of Radiology, Faculty of Medicine, İstanbul Okan University, İstanbul 34947, Türkiye.
- Department of Electrical and Electronics Engineering, Izmir Bakircay University, Izmir 35665, Türkiye.
- Department of Radiology, Faculty of Medicine, Kocaeli University, Kocaeli 41001, Türkiye.
- Department of Pulmonology, Faculty of Medicine, İstanbul Okan University, İstanbul 34947, Türkiye.
- Department of Electronics & Telecommunication Engineering, Kocaeli University, Kocaeli 41001, Türkiye.
Abstract
<b>Background/Objectives:</b> Differentiating mediastinal lymphadenopathy associated with lung cancer from sarcoidosis remains a clinical challenge because of overlapping imaging findings. This study evaluated whether CT-derived radiomic features, alone and in combination with clinical variables, could support the non-invasive differentiation of these two entities. <b>Methods:</b> In this retrospective single-center study, 204 histopathologically confirmed mediastinal lymph nodes were analyzed. A total of 107 radiomic features were extracted from manually segmented contrast-enhanced thoracic CT images. Multiple feature selection methods, dimensionality reduction techniques, and machine learning classifiers were evaluated using patient-level five-fold cross-validation. Additional clinical-only, combined clinical-radiomic, one-node-per-patient sensitivity, and exploratory interobserver feature stability analyses were performed. <b>Results:</b> Among radiomics-only models, LASSO achieved the highest ROC-AUC of 0.9108, whereas ElasticNet achieved the highest accuracy of 81.20%. The clinical-only ensemble model using age, sex, and smoking status showed strong performance, with an accuracy of 94.92% and an ROC-AUC of 0.9733. Selected combined clinical-radiomic models showed numerically higher performance; the combined correlation-filtered ensemble model achieved the highest accuracy of 97.78% and an ROC-AUC of 1.0000. Clinical integration also yielded more compact feature subsets in some methods, as combined LASSO selected 9.6 variables while improving ROC-AUC from 0.9108 to 0.9667 compared with radiomics-only LASSO. In the one-node-per-patient sensitivity analysis, clinical-only and combined models retained high performance, whereas radiomics-only LASSO showed reduced performance. Exploratory interobserver analysis showed moderate reproducibility for only a subset of radiomic features. <b>Conclusions:</b> CT-derived radiomic features may provide complementary information for differentiating mediastinal lymphadenopathy associated with lung cancer from that associated with sarcoidosis. However, clinical variables were highly informative, and the incremental value of radiomics should be interpreted cautiously. Further multicenter studies with external validation, standardized segmentation protocols, and clinically balanced cohorts are required before routine clinical implementation can be recommended.