Comparison of diagnostic accuracy of 2D and 3D measurements to determine opportunistic screening of osteoporosis using the proximal femur on abdomen-pelvic CT

PLOS ONE(2022)

引用 1|浏览4
暂无评分
摘要
Objectives To compare the osteoporosis-predicting ability of computed tomography (CT) indexes in abdomen-pelvic CT using the proximal femur and the reliability of measurements in two- and three-dimensional analyses. Methods Four hundred thirty female patients (age range, 50-96 years) who underwent dual-energy X-ray absorptiometry and abdominal-pelvic CT within 1 month were retrospectively selected. The volumes of interest (VOIs) from the femoral head to the lesser trochanter and the femoral neck were expressed as 3D(Femur). Round regions of interest (ROIs) of image plane drawn over the femoral neck touching the outer cortex were determined as 2D(coronal). In HU histogram analysis (HUHA), the percentages of HU histogram ranges related to the ROI or VOI were classified as HUHA(Fat) (<0 HU) and HUHA(Bone) (126 HU <=). Diagnostic performance, correlation analysis and measurement reliability were analyzed by receiver operating characteristic curves, correlation coefficient and interobserver correlation coefficient (ICC), respectively. Results AUCs of each HUHA and mean-HU measurement on 2D-ROI and 3D-VOI were 0.94 or higher (P < 0.001). Both 3D(Femur)-Mean-HU and 3D(Femur)-HUHA(Bone) showed the highest AUC (0.96). The cut-off value of 3D(Femur)-Mean-HU was 231HU or less, (sensitivity: 94.8%; specificity: 85.0%; correlation coefficient: -0.65; P<0.001) for diagnosis of osteoporosis. There was no superiority between AUCs in 2D-ROI and 3D-VOI measurements (P>0.05). Reliability of the 3D-VOI measurement showed perfect agreement (ICC >= 0.94), and 2D-ROI showed moderate to good agreement (ICC range: 0.63 similar to 0.84). Conclusions CT indexes on 3D-VOI for predicting femoral osteoporosis showed similar diagnostic accuracy with better reproducibility of measurement, compared with 2D-ROI.
更多
查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要