Incidental vertebral fracture prediction using neuronal network-based automatic spine segmentation and volumetric bone mineral density extraction from routine clinical CT scans - Bodden, Jannis; Dieckmeyer, Michael; Sollmann, Nico; Burian, Egon; Rühling, Sebastian; Löffler, Maximilian T.; Sekuboyina, Anjany; El Husseini, Malek; Zimmer, Claus; Kirschke, Jan S.; Baum, Thomas (Frontiers in Endocrinology, 2023)
Objectives: To investigate vertebral osteoporotic fracture (VF) prediction by automatically extracted trabecular volumetric bone mineral density (vBMD) from routine CT, and to compare the model with fracture prevalence-based prediction models.Methods: This single-center retrospective study included patients who underwent two thoracoabdominal CT scans during clinical routine with an average inter-scan interval of 21.7±13.1 months (range 5-52 months). Automatic spine segmentation and vBMD extraction was performed by a convolutional neural network framework (anduin.bonescreen.de). Mean vBMD was calculated for levels T5-8, T9-12, and L1-5. VFs were identified by an expert in spine imaging. Odds ratios (ORs) for prevalent and incident VFs were calculated for vBMD (per standard deviation decrease) at each level, for baseline VF prevalence (yes/no), and for baseline VF count (n) using logistic regression models, adjusted for age and sex. Models were compared using Akaike's and Bayesian information criteria (AIC & BIC).Results: 420 patients (mean age, 63 years ± 9, 276 males) were included in this study. 40 (25 female) had prevalent and 24 (13 female) had incident VFs.