“刚刚接受”的论文经过了全面的同行评审,并已被接受发表在放射学:人工智能。本文将进行文案编辑,布局,并在最终版本发布之前进行验证审查。请注意,在制作最终的文案文章期间,可能会发现可能影响内容的错误。目的评估基于深度学习的胸部影像学年龄(CXR-Age)模型在亚洲个体大型外部测试队列中的预后价值。材料和方法这个单中心,回顾性研究包括连续的胸部X光片,在2004年1月至2018年6月期间接受健康检查的年龄为50~80岁的无症状亚裔个体.这项研究对以前开发的CXR-Age模型进行了专门的外部测试,它根据全因死亡率的风险预测年龄。CXR-全因年龄的调整后危险比(HR),心血管,肺癌,使用多变量Cox或Fine-Gray模型评估呼吸系统疾病死亡率,通过似然比检验评估它们的附加值。结果共36,924例(平均实际年龄±SD,58±7岁;CXR年龄,60±5岁;22,352名男性)包括在内。平均随访11.0年,1250人(3.4%)死亡,包括153例心血管疾病(0.4%),166例肺癌(0.4%),和98例呼吸道死亡(0.3%)。CXR-年龄是全因的重要危险因素(50岁时的调整后HR:1.03;60岁时:1.05;70岁时:1.07),心血管(调整后的HR:1.11),肺癌(以前吸烟者的调整后HR:1.12;目前吸烟者:1.05),和呼吸系统疾病死亡率(校正HR:1.12)(所有P值<0.05)。似然比测试表明,CXR-Age对临床因素(包括所有结局的实际年龄)具有额外的预后价值(所有P值<0.001)。结论基于深度学习的胸部X线年龄与各种生存结果相关,并且对无症状亚洲个体的临床因素具有附加价值。表明了它的普遍性。©RSNA,2024.
Purpose To assess the prognostic value of a deep learning-based chest radiographic age (hereafter, CXR-Age) model in a large external test cohort of Asian individuals. Materials and Methods This single-center, retrospective study included chest radiographs from consecutive, asymptomatic Asian individuals aged 50-80 years who underwent health checkups between January 2004 and June 2018. This study performed a dedicated external test of a previously developed CXR-Age model, which predicts an age adjusted based on the risk of all-cause mortality. Adjusted hazard ratios (HRs) of CXR-Age for all-cause, cardiovascular, lung cancer, and respiratory disease mortality were assessed using multivariable Cox or Fine-Gray models, and their added values were evaluated by likelihood ratio tests. Results A total of 36 924 individuals (mean chronological age, 58 years ± 7 [SD]; CXR-Age, 60 years ± 5; 22 352 male) were included. During a median follow-up of 11.0 years, 1250 individuals (3.4%) died, including 153 cardiovascular (0.4%), 166 lung cancer (0.4%), and 98 respiratory (0.3%) deaths. CXR-Age was a significant risk factor for all-cause (adjusted HR at chronological age of 50 years, 1.03; at 60 years, 1.05; at 70 years, 1.07), cardiovascular (adjusted HR, 1.11), lung cancer (adjusted HR for individuals who formerly smoked, 1.12; for those who currently smoke, 1.05), and respiratory disease (adjusted HR, 1.12) mortality (P < .05 for all). The likelihood ratio test demonstrated added prognostic value of CXR-Age to clinical factors, including chronological age for all outcomes (P < .001 for all). Conclusion Deep learning-based chest radiographic age was associated with various survival outcomes and had added value to clinical factors in asymptomatic Asian individuals, suggesting its generalizability. Keywords: Conventional Radiography, Thorax, Heart, Lung, Mediastinum, Outcomes Analysis, Quantification, Prognosis, Convolutional Neural Network (CNN) Supplemental material is available for this article. © RSNA, 2024 See also the commentary by Adams and Bressem in this issue.