Evaluation of a radiomics workflow in chest CT: a pilot study of lung lesion segmentation, feature extraction, and clustering.

Objective: To evaluate a radiomics workflow applied to chest computed tomography (CT), examining its feasibility and performance across the steps of lung lesion segmentation, feature extraction, and clustering, as well as exploring its potential to distinguish between benign and malignant lung lesions. Materials and Methods: This was a retrospective study including CT images of 32 histopathology-confirmed lesions (11 benign; 21 malignant). Lesions were segmented by using syngo.via Frontier Radiomics software, af-ter which > 900 radiomic features were extracted with the same software. Data processing-including normal-ization, dimensionality reduction (principal component analysis), and unsupervised (k-means) clustering-was conducted in Python. Results: The best clustering solution was obtained with a k = 2 (silhouette score = 0.4668), effectively separating benign lesions, which exhibited low heterogeneity, from malignant lesions, which exhibited complex patterns of texture and shape. Conclusion: The application of a radiomics workflow proved feasible and potentially useful for differentiating between benign and malignant lung lesions on CT, underscoring its value as a complementary tool in thoracic oncology. How-ever, future studies using multicenter datasets, blinded evaluations, and more structured integration environments are warranted in order to support clinical validation and prototyping.