Automated opportunistic osteoporotic fracture risk assessment using computed tomography scans to aid in FRAX underutilization.
Noa DaganEldad ElnekaveNoam BardaOrna Bregman-AmitaiAmir BarMila OrlovskyEitan BachmatRan D BalicerPublished in: Nature medicine (2020)
Methods for identifying patients at high risk for osteoporotic fractures, including dual-energy X-ray absorptiometry (DXA)1,2 and risk predictors like the Fracture Risk Assessment Tool (FRAX)3-6, are underutilized. We assessed the feasibility of automatic, opportunistic fracture risk evaluation based on routine abdomen or chest computed tomography (CT) scans. A CT-based predictor was created using three automatically generated bone imaging biomarkers (vertebral compression fractures (VCFs), simulated DXA T-scores and lumbar trabecular density) and CT metadata of age and sex. A cohort of 48,227 individuals (51.8% women) aged 50-90 with available CTs before 2012 (index date) were assessed for 5-year fracture risk using FRAX with no bone mineral density (BMD) input (FRAXnb) and the CT-based predictor. Predictions were compared to outcomes of major osteoporotic fractures and hip fractures during 2012-2017 (follow-up period). Compared with FRAXnb, the major osteoporotic fracture CT-based predictor presented better receiver operating characteristic area under curve (AUC), sensitivity and positive predictive value (PPV) (+1.9%, +2.4% and +0.7%, respectively). The AUC, sensitivity and PPV measures of the hip fracture CT-based predictor were noninferior to FRAXnb at a noninferiority margin of 1%. When FRAXnb inputs are not available, the initial evaluation of fracture risk can be done completely automatically based on a single abdomen or chest CT, which is often available for screening candidates7,8.
Keyphrases
- dual energy
- computed tomography
- bone mineral density
- image quality
- hip fracture
- postmenopausal women
- positron emission tomography
- body composition
- contrast enhanced
- risk assessment
- magnetic resonance imaging
- chronic kidney disease
- end stage renal disease
- patient reported outcomes
- metabolic syndrome
- pregnant women
- polycystic ovary syndrome
- machine learning
- type diabetes
- ejection fraction
- high throughput
- deep learning
- soft tissue
- heavy metals
- peritoneal dialysis
- human health