Division of Endocrinology, Endocrine Research Institute, Department of Internal Medicine, Yonsei University College of Medicine, Seoul, South Korea.
Yonsei University Graduate School of Medicine, Seoul, South Korea.
J Bone Miner Res. 2021 Sep;36(9):1708-1716. doi: 10.1002/jbmr.4342. Epub 2021 Jun 7.
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. © 2021 American Society for Bone and Mineral Research (ASBMR).
双能 X 射线吸收法（DXA）骨密度检测是诊断骨质疏松症的标准方法，用于检测骨折高危个体。从 DXA 髋部图像中提取可量化的纹理特征的放射组学方法可能无需额外成本即可提高髋部骨折预测的准确性。在此，我们研究了 DXA 髋部图像的骨放射组学评分是否可以提高老年女性社区队列中髋部骨折的预测能力。在该推导队列（143 名发生髋部骨折的女性[平均年龄 73 岁，骨折中位时间 2.1 年]与 290 名年龄匹配的未发生髋部骨折的女性[平均年龄 73 岁]，随访期间中位时间为 5.5 年）中，将数据分为训练集（75%）和测试集（25%，预留集）。在使用从训练集中的 300 个纹理特征中挖掘出的 14 个特征的各种模型中，随机森林模型被选为最佳模型，基于测试集中的性能，构建基于 DXA 髋部图像的骨放射组学评分（范围为 0 到 100）。在一个作为临床验证集的社区队列（2029 名女性，平均年龄 71 岁）中，使用在训练集中建立的模型计算骨放射组学评分。在中位随访 5.4 年期间，共有 34 名参与者（1.7%）发生髋部骨折（平均骨放射组学评分 40±16 与未骨折者的 28±12，p<0.001）。骨放射组学评分增加 1 分，与髋部骨折的风险增加 4%相关（校正后的危险比[aHR] = 1.04，p = 0.001），在调整年龄、体重指数（BMI）、既往骨折史和股骨颈 T 评分后，当加入协变量时，模型拟合得到改善（似然比卡方 10.74，p = 0.001）。在调整 FRAX 髋部骨折概率后，骨放射组学评分与髋部骨折的相关性仍然稳健（aHR = 1.06，p<0.001）。从 DXA 髋部图像的纹理特征中估计的骨放射组学评分有可能提高髋部骨折的预测能力。
