在使用人工智能工具进行对比后计算机断层扫描时,对肝硬化症的量化
Brian A Derstine1, Sven A Holcombe2, Vincent L Chen2
1Michigan Medicine, Ann Arbor, USA. bderstin@med.umich.edu.
Abdominal radiology (New York)
|July 27, 2025
概括
这项研究表明,自动化CT分析可以准确地测量肝脂肪,使用后对比扫描能够早期检测脂肪性肝病 (SLD). 这种方法可以从临床CTs更好地量化肝脏肥胖症.
科学领域:
- 放射学 放射学是一门学科.
- 医疗成像医学成像
- 肝病学 肝病学是一种肝病学.
背景情况:
- 早期发现脂肪性肝病 (SLD) 对患者的治疗结果至关重要.
- 肝硬化症通常在计算机断层扫描 (CT) 扫描中偶然发现.
- 使用CT估计肝脂肪分数 (FF) 的现有方法在对比后扫描方面存在局限性.
研究的目的:
- 为了确定自动化工作流是否可以在CT上准确测量肝脏衰减.
- 在对比后CT研究中验证肝脏或肝衰减的值.
- 开发一种用于估计磁共振质子密度脂肪分数 (MR-PDFF) 的方法,用于对比后CT.
主要方法:
- 利用TotalSegmentator机器学习模型在CT扫描上实现肝脏和脏的自动细分.
- 从细分体积和手动感兴趣区域 (ROI) 提取的平均衰减值.
- 开发并验证了特定阶段 (动脉,静脉,延迟) 的回归方程,以估计来自对比后肝衰减的MR-PDFF.
主要成果:
- 自动细分显示了与手动测量肝脏和脏衰减的高相关性.
- 仅仅肝脏衰减就有效地在对比后的CT上分类了中度至重度的肥胖症.
- 使用正确的对比后CT衰减估计的脂肪分数与非对比的MR-PDFF密切一致,达到高的AUROC值.
结论:
- 自动化CT分析为肝脂肪评估提供了手动ROI的可靠替代方案.
- 对比后的CT肝衰减可以识别中度至重度的肝肥胖症.
- 开发的校正方程式可以从对比后CT估计MR-PDFF,从而促进大规模的SLD查和研究.
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