Vertebral trabecular bone texture analysis in opportunistic MRI and CT scan can distinguish patients with and without osteoporotic vertebral fracture: A preliminary study. Issue 158 (January 2023)

• Record Type: Journal Article
• Authors: Poullain, François; Champsaur, Pierre; Pauly, Vanessa; Knoepflin, Paul; Le Corroller, Thomas; Creze, Maud; Pithioux, Martine; Bendahan, David; Guenoun, Daphne
• Is Part Of: European journal of radiology. Issue 158(2023)
• Publication Date: 2023-01
• Subjects: Spine -- Osteoporosis -- Opportunistic -- CT -- MRI -- Texture; Medical radiology -- Periodicals; Radiology -- Periodicals; Radiologie médicale -- Périodiques; Medical radiology; Periodicals; 616.075705
• DOI: 10.1016/j.ejrad.2022.110642
• Languages: English
• ISSNs: 0720-048X

Abstract

Highlights: Texture parameters computed from MRI and CT scans can identify patients with vertebral fracture. Hounsfield Unit Bone Density and age are the best parameters to predict the fracture risk. Machine learning algorithm can detect fracture risk in opportunistic CT and MR. Abstract: Purpose: To investigate the potential of texture parameters from opportunistic MRI and CT for the detection of patients with vertebral fragility fracture, to design a decision tree and to compute a Random Forest analysis for the prediction of fracture risk. Methods: One hundred and eighty vertebrae of sixty patients with at least one (30) or without (30) a fragility fracture were retrospectively assessed. Patients had a DXA, an MRI and a CT scan from the three first lumbar vertebrae. Vertebrae texture analysis was performed in routine abdominal or lumbar CT and lumbar MRI using 1st and 2nd order texture parameters. Hounsfield Unit Bone density (HU BD) was also measured on CT-scan images. Results: Twelve texture parameters, Z-score and HU BD were significantly different between the two groups whereas T score and BMD were not. The inter observer reproducibility was good to excellent. Decision tree showed that age and HU BD were the most relevant factors to predict the fracture risk with a 93 % sensitivity and 56 % specificity. AUC was 0.91 in MRI and 0.92 in CT-scan using the Random Forest analysis. The corresponding sensitivity and specificity were 72 % and 93 % in MRI and 83 and 89 % in CT. Conclusions: This study is the first to compare texture indices computed from opportunistic CT and MR images. Age and HU-BD together with selected texture parameters could be used to assess risk fracture. Machine learning algorithm can detect fracture risk in opportunistic CT and MR imaging and might be of high interest for the diagnosis of osteoporosis.