{"title":"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。","authors":"Keisuke Uemura, Yoshito Otake, Kazuma Takashima, Hidetoshi Hamada, Takashi Imagama, Masaki Takao, Takashi Sakai, Yoshinobu Sato, Seiji Okada, Nobuhiko Sugano","doi":"10.1302/2046-3758.129.BJR-2023-0115.R1","DOIUrl":null,"url":null,"abstract":"<p><strong>Aims: </strong>This study aimed to develop and validate a fully automated system that quantifies proximal femoral bone mineral density (BMD) from CT images.</p><p><strong>Methods: </strong>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/cm<sup>3</sup>). 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.</p><p><strong>Results: </strong>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/cm<sup>2</sup>.</p><p><strong>Conclusion: </strong>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.</p>","PeriodicalId":9074,"journal":{"name":"Bone & Joint Research","volume":"12 9","pages":"590-597"},"PeriodicalIF":4.7000,"publicationDate":"2023-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10509772/pdf/","citationCount":"0","resultStr":"{\"title\":\"Development and validation of an open-source tool for opportunistic screening of osteoporosis from hip CT images.\",\"authors\":\"Keisuke Uemura, Yoshito Otake, Kazuma Takashima, Hidetoshi Hamada, Takashi Imagama, Masaki Takao, Takashi Sakai, Yoshinobu Sato, Seiji Okada, Nobuhiko Sugano\",\"doi\":\"10.1302/2046-3758.129.BJR-2023-0115.R1\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Aims: </strong>This study aimed to develop and validate a fully automated system that quantifies proximal femoral bone mineral density (BMD) from CT images.</p><p><strong>Methods: </strong>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/cm<sup>3</sup>). 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.</p><p><strong>Results: </strong>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/cm<sup>2</sup>.</p><p><strong>Conclusion: </strong>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.</p>\",\"PeriodicalId\":9074,\"journal\":{\"name\":\"Bone & Joint Research\",\"volume\":\"12 9\",\"pages\":\"590-597\"},\"PeriodicalIF\":4.7000,\"publicationDate\":\"2023-09-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10509772/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Bone & Joint Research\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1302/2046-3758.129.BJR-2023-0115.R1\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"CELL & TISSUE ENGINEERING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Bone & Joint Research","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1302/2046-3758.129.BJR-2023-0115.R1","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"CELL & TISSUE ENGINEERING","Score":null,"Total":0}
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.