人工智能用于多个时间点的动脉相位对比增强的MRI分析,以预测肝细胞癌中跨动脉化学栓塞后的预后
Lanlin Yao1, Hamzah Adwan1, Simon Bernatz1
1Clinic for Radiology and Nuclear Medicine, University Hospital Frankfurt, Goethe University, Theodor-Stern-Kai 7, 60590, Frankfurt Am Main, Germany.
La Radiologia medica
|July 24, 2025
概括
一个人工智能模型,ProgSwin-UNETR,准确地预测了经过跨动脉化学栓塞 (TACE) 的患者的肝细胞癌 (HCC) 风险. 这种人工智能方法提供了个性化的预后分层,优于传统方法,可以更好地规划治疗.
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
- 在瘤学瘤学.
背景情况:
- 肝细胞癌 (HCC) 管理需要在跨动脉化血栓化 (TACE) 期间准确预后.
- 多时间点对比增强磁共振成像 (CE-MRI) 对于监测治疗反应和预测结果至关重要.
研究的目的:
- 开发和验证一个人工智能驱动的模型,用于TACE患者的HCC预后分层.
- 利用多个时间点的动脉相CE-MRI数据来提高预测准确度.
主要方法:
- 从181名接受TACE (单独或与MWA) 的HCC患者的543个CE-MRI扫描的回顾性分析.
- 使用Swin变压器架构开发ProgSwin-UNETR深度学习模型,用于四类预后分层.
- 通过四重交叉验证进行评估,与放射学和mRECIST标准进行基准测试,并使用卡普兰-梅尔曲线进行生存分析.
主要成果:
- 在ProgSwin-UNETR模型中,AUC达到0.92,在HCC预后分层方面表现优异,与放射学和mRECIST相比.
- 基于人工智能的风险分层显示了整个队列和治疗小组的统计学上显著的生存差异 (p < 0.005).
- 多变量考克斯回归证实了人工智能模型作为一个独立的预后因素,GradCAM++提供了区域解释性.
结论:
- 普罗格斯温-UNETR AI模型准确地预测TACE接受的HCC患者的风险组.
- 这种人工智能工具可以实现个性化的预后预测,从而有可能优化HCC的治疗策略.
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