早期类风湿性关节炎和健康对照中的深度学习衍生的定量间歇性异常:一个多中心,前性横截面研究
medRxiv : the preprint server for health sciences
|February 27, 2026
概括
早期类风湿性关节炎 (RA) 患者与健康个体相比,肺部异常明显更多,肺部组织不那么正常,由深度学习定量计算断层扫描 (QCT) 检测到. 这凸显了早期RA肺部健康保护策略的必要性.
科学领域:
- 放射学和成像学 放射学和成像学
- 类风湿病学 类风湿病学
- 肺部医学 肺部医学
背景情况:
- 类风湿性关节炎 (RA) 是一种全身性自身免疫性疾病,可以影响多个器官,包括肺部.
- 肺部并发症在RA患者中很常见,但早期检测和量化肺部异常仍然具有挑战性.
- 使用深度学习的定量计算机断层扫描 (QCT) 为自动分析胸部CT成像提供了一个有前途的工具.
研究的目的:
- 利用基于深度学习的QCT来检测和量化早期类风湿性关节炎 (RA) 患者的肺部异常,与健康对照人群相比.
- 评估QCT衍生肺部测量与早期RA患者的临床因素之间的关联.
主要方法:
- 分析了200名早期RA患者的高分辨率CT胸部成像 (SAIL-RA研究) 和104名健康对照 (COPDGene研究).
- 深度学习分类器量化了每个参与者的正常肺部,间歇性异常和肺气的百分比.
- 使用多变量线性回归来比较群体之间的QCT特征,并检查RA队列内的关联.
主要成果:
- 早期的RA患者与健康对照人群相比,表现出更高的间歇性异常的中位数比例 (3.7%对1.6%,p<0.0001) 和不太正常的肺部 (9.3%少,p<0.0001).
- 在RA参与者中,QCT检测到的间歇性异常与年龄较大和DAS28-ESR得分较高有关.
- 深度学习QCT方法有效地区分了早期RA中的肺外细胞变化.
结论:
- 基于深度学习的QCT揭示了早期RA患者显著的肺异常,包括减少正常肺体积和增加间歇性变化.
- 这些发现表明,在RA的早期可能存在肺损伤,这强调了主动肺健康干预的重要性.
- QCT是量化RA肺部参与的一个有价值的工具,可能有助于监测疾病进展和治疗反应.
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