Dual-energy X-ray absorptiometry (DXA)-based bone mineral density testing is standard to diagnose osteoporosis to detect individuals at high risk of fracture. A radiomics approach to extract quantifiable texture features from DXA hip images may improve hip fracture prediction without additional costs. Here, we investigated whether bone radiomics scores from DXA hip images could improve hip fracture prediction in a community-based cohort of older women. The derivation set (143 women who sustained hip fracture [mean age 73 years, time to fracture median 2.1 years] versus 290 age-matched women [mean age 73 years] who did not sustain hip fracture during follow-up [median 5.5 years]) were split into the train set (75%) and the test set (25% hold-out set). Among various models using 14 selected features out of 300 texture features mined from DXA hip images in the train set, random forest model was selected as the best model to build a bone radiomics score (range 0 to 100) based on the performance in the test set. In a community-based cohort (2029 women, mean age 71 years) as the clinical validation set, the bone radiomics score was calculated using a model fitted in the train set. A total of 34 participants (1.7%) sustained hip fracture during median follow-up of 5.4 years (mean bone radiomics score 40 ± 16 versus 28 ± 12 in non-fractured, p < 0.001). A one-point bone radiomics score increment was associated with a 4% elevated risk of incident hip fracture (adjusted hazard ratio [aHR] = 1.04, p = 0.001) after adjustment for age, body mass index (BMI), previous history of fracture, and femoral neck T-score, with improved model fit when added to covariates (likelihood ratio chi-square 10.74, p = 0.001). The association between bone radiomics score with incident hip fracture remained robust (aHR = 1.06, p < 0.001) after adjustment for FRAX hip fracture probability. Bone radiomics scores estimated from texture features of DXA hip images have the potential to improve hip fracture prediction.
Bibliographical noteFunding Information:
This work was supported by the Research of Korea Centers for Disease Control and Prevention (2013‐E63007‐01, 2013‐E63007‐02, 2019‐ER6302‐01). We thank all participants, team members of the KURE cohort for conducting studies, and Sukyoung Han and Dawon Song for assisting in collection of DXA scan images for radiomics analysis. We thank the SENTINEL (Severance Endocrinology Data Science Platform) team for the assistance in data management and statistical analysis, which was funded by the 2020 Research Fund of the Department of Internal Medicine, Severance Hospital, Seoul, South Korea (4‐2018‐1215; DUCD000002).
© 2021 American Society for Bone and Mineral Research (ASBMR).
All Science Journal Classification (ASJC) codes
- Endocrinology, Diabetes and Metabolism
- Orthopedics and Sports Medicine