Sang Wouk Cho, Namki Hong, Kyoung Min Kim, Young Han Lee, Chang Oh Kim, Hyeon Chang Kim, Yumie Rhee, Brian H Chen, William D Leslie, Steven R Cummings
{"title":"使用深度学习侧位脊柱x线片和DXA VFA来估计脊柱年龄,以预测意外骨折和死亡率。","authors":"Sang Wouk Cho, Namki Hong, Kyoung Min Kim, Young Han Lee, Chang Oh Kim, Hyeon Chang Kim, Yumie Rhee, Brian H Chen, William D Leslie, Steven R Cummings","doi":"10.1038/s41514-025-00271-8","DOIUrl":null,"url":null,"abstract":"<p><p>Spine age estimated from lateral spine radiographs and DXA VFAs could be associated with fracture and mortality risk. In the VERTE-X cohort (n = 10,341, derivation set) and KURE cohort (n = 3517; external test set), spine age discriminated prevalent vertebral fractures and osteoporosis better than chronological age. Predicted age difference was associated with overall (adjusted HR [aHR] 1.22 per 1 SD increment, p < 0.001), vertebral, non-vertebral incident fractures, and mortality (aHR 1.31, p = 0.001) during a median 6.6 years follow-up in KURE, independent of chronological age and covariates. Spine age to estimate FRAX hip fracture probabilities, instead of chronological age, improved the discriminatory performance for incident hip fracture (AUROC 0.83 vs. 0.78, p = 0.027). Shorter height, lower femoral neck BMD, diabetes, vertebral fractures, and surgical prosthesis were associated with higher predicted age difference, explaining 40% of variance. Spine age estimated from lateral spine radiographs and DXA VFA enhanced fracture risk assessment and mortality prediction over chronological age.</p>","PeriodicalId":94160,"journal":{"name":"npj aging","volume":"11 1","pages":"83"},"PeriodicalIF":6.0000,"publicationDate":"2025-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Spine age estimation using deep learning in lateral spine radiographs and DXA VFA to predict incident fracture and mortality.\",\"authors\":\"Sang Wouk Cho, Namki Hong, Kyoung Min Kim, Young Han Lee, Chang Oh Kim, Hyeon Chang Kim, Yumie Rhee, Brian H Chen, William D Leslie, Steven R Cummings\",\"doi\":\"10.1038/s41514-025-00271-8\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Spine age estimated from lateral spine radiographs and DXA VFAs could be associated with fracture and mortality risk. In the VERTE-X cohort (n = 10,341, derivation set) and KURE cohort (n = 3517; external test set), spine age discriminated prevalent vertebral fractures and osteoporosis better than chronological age. Predicted age difference was associated with overall (adjusted HR [aHR] 1.22 per 1 SD increment, p < 0.001), vertebral, non-vertebral incident fractures, and mortality (aHR 1.31, p = 0.001) during a median 6.6 years follow-up in KURE, independent of chronological age and covariates. Spine age to estimate FRAX hip fracture probabilities, instead of chronological age, improved the discriminatory performance for incident hip fracture (AUROC 0.83 vs. 0.78, p = 0.027). Shorter height, lower femoral neck BMD, diabetes, vertebral fractures, and surgical prosthesis were associated with higher predicted age difference, explaining 40% of variance. Spine age estimated from lateral spine radiographs and DXA VFA enhanced fracture risk assessment and mortality prediction over chronological age.</p>\",\"PeriodicalId\":94160,\"journal\":{\"name\":\"npj aging\",\"volume\":\"11 1\",\"pages\":\"83\"},\"PeriodicalIF\":6.0000,\"publicationDate\":\"2025-10-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"npj aging\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1038/s41514-025-00271-8\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"GERIATRICS & GERONTOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"npj aging","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1038/s41514-025-00271-8","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"GERIATRICS & GERONTOLOGY","Score":null,"Total":0}
Spine age estimation using deep learning in lateral spine radiographs and DXA VFA to predict incident fracture and mortality.
Spine age estimated from lateral spine radiographs and DXA VFAs could be associated with fracture and mortality risk. In the VERTE-X cohort (n = 10,341, derivation set) and KURE cohort (n = 3517; external test set), spine age discriminated prevalent vertebral fractures and osteoporosis better than chronological age. Predicted age difference was associated with overall (adjusted HR [aHR] 1.22 per 1 SD increment, p < 0.001), vertebral, non-vertebral incident fractures, and mortality (aHR 1.31, p = 0.001) during a median 6.6 years follow-up in KURE, independent of chronological age and covariates. Spine age to estimate FRAX hip fracture probabilities, instead of chronological age, improved the discriminatory performance for incident hip fracture (AUROC 0.83 vs. 0.78, p = 0.027). Shorter height, lower femoral neck BMD, diabetes, vertebral fractures, and surgical prosthesis were associated with higher predicted age difference, explaining 40% of variance. Spine age estimated from lateral spine radiographs and DXA VFA enhanced fracture risk assessment and mortality prediction over chronological age.