自动LSN:基于深度学习的CT中完全自动化的肝脏表面度量化,用于评估晚期慢性肝病
Sisi Yang1,2,3, Riccardo Sartoris4,5, Yann Teyssier6,7
1Guerbet Research, Villepinte, France. sisi.yang@guerbet.com.
European radiology
|February 5, 2026
概括
一个新的人工智能 (AI) 算法,自动LSN,准确量化肝脏表面结节性 (LSN) 以进行高级慢性肝病 (ACLD) 和肝硬化症的非侵入性诊断. 这种全自动化工具的性能与半自动化软件相当,可以进行更广泛的选.
科学领域:
- 放射学和医学成像学 医学成像学
- 人工智能在医学中的应用
- 肝病学和胃肠病学 肝病学和胃肠学
背景情况:
- 肝脏表面结节性 (LSN) 是诊断肝硬化的一个关键的非侵入性生物标志物.
- 目前的LSN量化方法,如肝脏边界分析 (LBA),是半自动化和耗时的.
- 在临床实践中需要有效和准确的自动化工具来进行LSN评估.
研究的目的:
- 引入和评估自动LSN,一个基于人工智能 (AI) 的全自动算法用于LSN量化.
- 评估自动LSN与肝纤维化阶段的相关性.
- 为了比较自动LSN与半自动LBA的诊断性能,用于晚期慢性肝病 (ACLD) 和肝硬化.
主要方法:
- 对127名患有慢性肝病风险因素的患者进行了回顾性双中心研究,这些患者接受了CT和肝活检.
- 使用新型自动LSN算法和已建立的半自动化LBA软件量化LSN.
- 纤维化阶段 (ACLD的F3-F4,肝硬化的F4) 与使用曼-惠特尼-威尔科克森测试,斯皮尔曼相关性和ROC曲线分析 (AUC) 的LSN测量进行了比较.
主要成果:
- 自动LSN显示出与纤维化阶段的显著正相关性 (ρ=0.59),与LBA (ρ=0.44) 相比.
- ACLD的诊断准确率 (AUC) 为自动LSN的0.79,而LBA的0.73.
- 肝硬化诊断准确率 (AUC) 为自动LSN的0.84,而LBA的0.74,所有比较均在非劣势率范围内.
结论:
- 完全自动化的自动LSN算法显示出与肝纤维化有很强的相关性.
- 自动LSN实现了诊断性能,与半自动LBA软件用于检测ACLD和肝硬化不逊色.
- 自动LSN的自动化和准确性使其成为机会性查和肝病患者监测的宝贵工具.
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