Radiomic texture analysis for the assessment of osteoporosis on low-dose thoracic CT scans

Abstract

In recent years, the assessment of non-cancerous diseases on low-dose CT scans for lung cancer screening has gained significant attention. Osteoporosis shares many risk factors with lung cancer and the thoracic and upper lumbar vertebrae can be visualized within the screening scan range, making diagnosis of osteoporosis viable. However, manual assessments can be time-consuming and inconsistent. This study investigates the application of radiomic texture analysis (RTA) for the automatic detection of osteoporosis. In this retrospective analysis of 613 CT screening scans acquired from the I-ELCAP database, quantitative features, including those based on intensity, texture, and frequency, were extracted from ROIs manually placed within the central body of the T6 and L1 vertebrae on axial images. The top 4 individually performing features were selected to train an SVM classifier for the classification between osteoporotic, abnormal, and normal vertebrae. Performance was evaluated through ROC analysis, with areas under the ROC curve of 0.925 +/- 0.054 for the T6 vertebra and 0.847 +/- 0.092 for L1. Further, RTA was compared to a radiologist’s visual diagnosis and a previously published automatic bone mineral density calculation approach. The RTA technique correlated well with the automatic BMD calculation, with Pearson linear correlation coefficients of - 0.752 and -0.653 for the T6 and L1 vertebrae, respectively, and qualitative comparison to the visual assessment was favorable. Based on the ROC results and the correlation with previously established methods, RTA demonstrated significant potential in quantifying vertebral bodies in axial CT screening scans and characterizing the vertebral disease state.