Department of Radiology, University of Mississippi Medical Center, Jackson, MS, USA.
Department of Radiology, University of Alabama at Birmingham, JTN 405, 619 19th Street South, Birmingham, AL, 35249-6830, USA.
Abdom Radiol (NY). 2021 Apr;46(4):1752-1760. doi: 10.1007/s00261-020-02791-1. Epub 2020 Oct 12.
To prospectively validate a method to accurately and rapidly differentiate normal from abnormal spinal bone mineral density (BMD) using colored abdominal CT images.
For this prospective observational study, 196 asymptomatic women ≥ 50 years of age presenting for screening mammograms underwent routine nonenhanced CT imaging of the abdomen. The CT images were processed with software designed to generate sagittal colored images with green vertebral trabecular bone indicating normal BMD and red indicating abnormal BMD (low BMD or osteoporosis). Four radiologists evaluated L1/L2 BMD on sagittal images using visual assessment of grayscale images, quantitative measurements of mean vertebral attenuation, and visual assessment of colored images. Mean BMD values at L1/L2 using quantitative CT with a phantom served as the reference standard. The average accuracy and time of interpretation were calculated. Inter-observer agreement was assessed using intraclass correlation coefficient (ICC).
Mean attenuation at L1/L2 was highly correlated with mean BMD (r = 0.96/0.91, p < 0.001 for both). The average accuracy and mean time to assess BMD among four readers for differentiating normal from abnormal BMD was 66% and 6.0 s using visual assessment of grayscale images, 88% and 15.2 s using quantitative measurements of mean vertebral attenuation, and 92% and 2.1 s using visual assessment of colored images (p < 0.001 and p < 0.001, respectively). Inter-observer agreement was poor using visual assessment of grayscale images (ICC:0.31), good using quantitative measurements of mean vertebral attenuation (ICC:0.73), and excellent using visual assessment of colored images (ICC:0.90).
Detection of abnormal BMD using colored abdominal CT images was highly accurate, rapid, and had excellent inter-observer agreement.
前瞻性验证一种使用彩色腹部 CT 图像准确快速区分正常和异常脊柱骨密度（BMD）的方法。
这项前瞻性观察性研究纳入了 196 名无症状、年龄≥50 岁、因筛查性乳房 X 光检查而就诊的女性，对其行腹部常规非增强 CT 扫描。使用设计用于生成矢状彩色图像的软件对 CT 图像进行处理，绿色的椎体小梁骨表示正常 BMD，红色表示异常 BMD（低 BMD 或骨质疏松症）。4 位放射科医生使用灰度图像的视觉评估、平均椎体衰减的定量测量和彩色图像的视觉评估，对矢状图像上的 L1/L2 BMD 进行评估。使用带有体模的定量 CT 测量 L1/L2 的平均 BMD 值作为参考标准。计算平均解释准确性和时间。使用组内相关系数（ICC）评估观察者间的一致性。
L1/L2 的平均衰减与平均 BMD 高度相关（r 值分别为 0.96/0.91，均<0.001）。4 位读者使用灰度图像的视觉评估、平均椎体衰减的定量测量和彩色图像的视觉评估来区分正常和异常 BMD 的平均准确性和平均评估时间分别为 66%和 6.0s、88%和 15.2s、92%和 2.1s（均<0.001，p 值分别为<0.001 和<0.001）。使用灰度图像的视觉评估时，观察者间的一致性较差（ICC：0.31），使用平均椎体衰减的定量测量时一致性较好（ICC：0.73），使用彩色图像的视觉评估时一致性极好（ICC：0.90）。
使用彩色腹部 CT 图像检测异常 BMD 具有高度准确性、快速性和极好的观察者间一致性。
