开发和验证一种开源工具,用于髋关节CT图像中骨质疏松症的机会筛查。

IF 4.7 2区 医学 Q2 CELL & TISSUE ENGINEERING Bone & Joint Research Pub Date : 2023-09-20 DOI:10.1302/2046-3758.129.BJR-2023-0115.R1
Keisuke Uemura, Yoshito Otake, Kazuma Takashima, Hidetoshi Hamada, Takashi Imagama, Masaki Takao, Takashi Sakai, Yoshinobu Sato, Seiji Okada, Nobuhiko Sugano
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引用次数: 0

摘要

目的:本研究旨在开发和验证一种从CT图像中量化股骨近端骨密度(BMD)的全自动系统。方法:本研究分析了从三个机构收集的978对髋关节CT和双能X线骨密度仪(DXA)测量的股骨近端(DXA-BMD)。根据CT图像,使用先前训练的深度学习模型自动分割股骨和校准体模。将每个体素的Hounsfield单位转换为密度(mg/cm3)。然后,开发了一个通过315例手动地标选择训练的深度学习模型,以选择股骨近端的地标,将CT体积旋转到中性位置。最后,将股骨的CT体积投影到冠状面上,并量化股骨近端的面积BMD(CT aBMD)。CT aBMD与DXA-BMD相关,受试者操作特征(ROC)分析量化了诊断骨质疏松症的准确性。结果:976/978髋(99.8%)成功测量了CT aBMD,CT aBMD与DXA-BMD之间存在显著相关性(r=0.941;p<0.001),ROC分析中诊断骨质疏松的曲线下面积为0.976。诊断的敏感性和特异性分别为88.9%和96%,临界值为0.625g/cm2。结论:使用本文开发的系统,可以从CT图像中准确地测量和诊断骨质疏松症。由于这些模型是开源的,临床医生可以使用所提出的系统来筛查骨质疏松症,并确定髋关节手术的手术策略。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Development and validation of an open-source tool for opportunistic screening of osteoporosis from hip CT images.

Aims: This study aimed to develop and validate a fully automated system that quantifies proximal femoral bone mineral density (BMD) from CT images.

Methods: The study analyzed 978 pairs of hip CT and dual-energy X-ray absorptiometry (DXA) measurements of the proximal femur (DXA-BMD) collected from three institutions. From the CT images, the femur and a calibration phantom were automatically segmented using previously trained deep-learning models. The Hounsfield units of each voxel were converted into density (mg/cm3). Then, a deep-learning model trained by manual landmark selection of 315 cases was developed to select the landmarks at the proximal femur to rotate the CT volume to the neutral position. Finally, the CT volume of the femur was projected onto the coronal plane, and the areal BMD of the proximal femur (CT-aBMD) was quantified. CT-aBMD correlated to DXA-BMD, and a receiver operating characteristic (ROC) analysis quantified the accuracy in diagnosing osteoporosis.

Results: CT-aBMD was successfully measured in 976/978 hips (99.8%). A significant correlation was found between CT-aBMD and DXA-BMD (r = 0.941; p < 0.001). In the ROC analysis, the area under the curve to diagnose osteoporosis was 0.976. The diagnostic sensitivity and specificity were 88.9% and 96%, respectively, with the cutoff set at 0.625 g/cm2.

Conclusion: Accurate DXA-BMD measurements and diagnosis of osteoporosis were performed from CT images using the system developed herein. As the models are open-source, clinicians can use the proposed system to screen osteoporosis and determine the surgical strategy for hip surgery.

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来源期刊
Bone & Joint Research
Bone & Joint Research CELL & TISSUE ENGINEERING-ORTHOPEDICS
CiteScore
7.40
自引率
23.90%
发文量
156
审稿时长
12 weeks
期刊介绍: The gold open access journal for the musculoskeletal sciences. Included in PubMed and available in PubMed Central.
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