A Deep Learning Model for Three-Dimensional Determination of Whole Thoracic Vertebral Bone Mineral Density from Noncontrast Chest CT: The Multi-Ethnic Study of Atherosclerosis
Abstract
Background Recent studies have investigated how deep learning (DL) algorithms applied to CT using two-dimensional (2D) segmentation (sagittal or axial planes) can calculate bone mineral density (BMD) and predict osteoporosis-related outcomes. Purpose To determine whether TotalSegmentator, an nnU-net algorithm, can measure three-dimensional (3D) vertebral body BMD across consistently imaged thoracic levels (T1-T10) at any conventional, noncontrast chest CT examination. Materials and Methods This study is a secondary analysis of a multicenter (n = 6) prospective cohort, the Multi-Ethnic Study of Atherosclerosis (MESA). Participants underwent noncontrast chest CT with (n = 296) and without (n = 2660) a phantom. In 594 participants, manual segmentation for T1-T10 vertebrae was performed on axial and sagittal planes. TotalSegmentator provided 3D vertebral body segmentation of T1-T10 levels with further postprocessing to remove cortical bone. Two-dimensional axial and sagittal DL-derived algorithms were developed and compared with 3D model performance. Dice and intersection-over-union scores were calculated. Vertebral BMD-derived data, integrated with the Fracture Risk Assessment Tool with no BMD (FRAXnb), were used to predict incident vertebral fractures (VFx) in participants from the follow-up MESA Examination 6 (n = 1304). Results This study included 2956 participants (1546 [52%] female; age, 69 years ± 9 [SD]), with longitudinal data obtained approximately 6.2 years later in a…
How strong is the evidence?
Evidence Hub reads this as a cohort study, done in people. Its evidence level is 3 of 5 (Moderate · trial / cohort / secondary analysis). Trials without a large randomized design, cohorts and secondary analyses give moderate evidence. Reported size: 2,956 participants or records.
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