基于深度学习的胸部CT衍生的呼吸系统参数与肌缩侧面硬化症疾病进展的关联
Seok-Jin Choi1,2, Jong-Su Kim3, Seong Yun Jeong4
1Department of Neurology, Seoul National University Hospital, Seoul, Republic of Korea.
Radiology
|May 13, 2025
概括
胸部CT扫描的深度学习分析提供了新的肺部和呼吸道肌肉指标 (LVI和RMI),可以预测肌缩性侧面硬化症 (ALS) 的进展和存活率,即使在患有腹筋损伤的患者中也是如此.
科学领域:
- 放射学和成像学 放射学和成像学
- 神经学 神经学
- 肺部病理学 肺部病理学
背景情况:
- 强迫生命能力 (FVC) 是一种标准的,但有限的,用于测量肌缩侧面硬化症 (ALS) 的呼吸功能.
- 用传统方法评估呼吸功能时,患有气柱损伤的患者面临着特殊的挑战.
研究的目的:
- 评估深度学习衍生的胸部CT参数在预测ALS进展中的实用性.
- 评估这些新型CT指标在预测ALS患者存活率方面的价值.
- 为了确定这些参数是否可以帮助评估患有凸骨损伤的患者.
主要方法:
- 对261名ALS患者的胸部CT扫描进行了回顾性分析.
- 深度学习软件计算了肺体积指数 (LVI) 和呼吸肌指数 (RMI).
- 统计分析包括顺序逻辑回归,考克斯回归和时间依赖ROC分析.
主要成果:
- 随着ALS临床阶段 (King阶段) 的增加,LVI和RMI的减少.
- 较高的LVI和RMI与更好的无气管切除术生存率相关 (P < .001).
- 乳腺炎和乳腺炎仍然是生存的独立预后因素,包括在乳腺损伤的患者中.
结论:
- 深度学习衍生的CT指标 (LVI和RMI) 有效地反映了ALS疾病阶段.
- 这些新的指标证明了ALS进展和生存的预后价值.
- 在ALS中,LVI和RMI为评估呼吸功能提供了一种有价值的工具,特别是在有气柱损伤的患者中.
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