Long-term reproducibility of opportunistically assessed vertebral bone mineral density and texture features in routine clinical multi-detector computed tomography using an automated segmentation framework

BACKGROUND: To investigate reproducibility of texture features and volumetric bone mineral density (vBMD) extracted from trabecular bone in the thoracolumbar spine in routine clinical multi-detector computed tomography (MDCT) data in a single scanner environment. METHODS: Patients who underwent two routine clinical thoraco-abdominal MDCT exams at a single scanner with a time interval of 6 to 26 months (n=203, 131 males; time interval mean, 13 months; median, 12 months) were included in this observational study. Exclusion criteria were metabolic and hematological disorders, bone metastases, use of bone-active medications, and history of osteoporotic vertebral fractures (VFs) or prior diagnosis of osteoporosis. A convolutional neural network (CNN)-based framework was used for automated spine labeling and segmentation (T5–L5), asynchronous Hounsfield unit (HU)-to-BMD calibration, and correction for the intravenous contrast medium phase. Vertebral vBMD and six texture features [variance(global), entropy, short-run emphasis (SRE), long-run emphasis (LRE), run-length non-uniformity (RLN), and run percentage (RP)] were extracted for mid- (T5–T8) and lower thoracic (T9–T12), and lumbar vertebrae (L1–L5), respectively. Relative annual changes were calculated in texture features and vBMD for each vertebral level and sorted by sex, and changes were checked for statistical significance (P<0.05) using paired t-tests. Root mean square coefficient of variation (RMSCV) and root mean square error (RMSE) were calculated as measures of variability. RESULTS: SRE, LRE, RLN, and RP exhibited substantial reproducibility with RMSCV-values below 2%, for both sexes and at all spine levels, while vBMD was less reproducible (RMSCV =11.9–16.2%). Entropy showed highest variability (RMSCV =4.34–7.69%) due to statistically significant increases [range, mean ± standard deviation: (4.40±5.78)% to (8.36±8.66)%, P<0.001]. RMSCV of variance(global) ranged from 1.60% to 3.03%. CONCLUSIONS: Opportunistic assessment of texture features in a single scanner environment using the presented CNN-based framework yields substantial reproducibility, outperforming vBMD reproducibility. Lowest scan-rescan variability was found for higher-order texture features. Further studies are warranted to determine, whether microarchitectural changes to the trabecular bone may be assessed through texture features.