Deep Learning-Enhanced Opportunistic Osteoporosis Screening in 100 kV Low-Voltage Chest CT: A Novel Way Toward Bone...

To explore the feasibility and accuracy of a deep learning (DL) method for fully automated vertebral body (VB) segmentation, region of interest (ROI) extraction, and bone mineral density (BMD) calculation using 100kV low-voltage chest CT performed for lung cancer screening across various scanners from different manufacturers and hospitals.

This study included 1167 patients who underwent 100 kV low-voltage chest and 120 kV lumbar CT from October 2022 to August 2024. Patients were divided into a training set (495 patients), a validation set (169 patients), and three test sets (245, 128, and 130 patients). The DL framework comprised four convolutional neural networks (CNNs): 3D VB-Net and SCN for automated VB segmentation and ROI extraction, and DenseNet and ResNet for BMD calculation of target VBs (T12-L2). The BMD values of 120 kV QCT were identified as reference data. Linear regression and BlandAltman analyses were used to compare the BMD values between 120 kV QCT and 100 kV CNNs and 100 kV QCT. Receiver operating characteristic curve analysis was used to evaluate the diagnostic performance of 100 kV CNNs and 100 kV QCT for osteoporosis and low BMD from normal BMD.

For three test sets, linear regression and BlandAltman analyses revealed a stronger correlation (R² = 0.970-0.994 and 0.968-0.986, P < .001) and better agreement (mean error, -2.24 to 1.52 and 2.72 to 3.06 mg/cm³) for the BMD between the 120 kV QCT and 100 kV CNNs than between the 120 kV and 100 kV QCT. The areas under the curve of the 100 kV CNNs and 100 kV QCT were 1.000 and 0.999-1.000, and 1.000 and 1.000 for detecting osteoporosis and low BMD from normal BMD, respectively.

The DL method achieved high accuracy for fully automated osteoporosis screening in 100 kV low-voltage chest CT scans obtained for lung cancer screening and performed well on various scanners from different manufacturers and hospitals.