动态对比增强MRI用于评估乳腺癌化疗反应使用条件生成对抗网络
Chad A Arledge1, Alan H Zhao2, Umit Topaloglu3,4
1Department of Biomedical Engineering, Wake Forest School of Medicine, 525 Vine St, Ste 150, Winston-Salem, NC 27101.
Radiology. Imaging cancer
|December 5, 2025
概括
一个新的条件生成对抗网络 (cGAN) 将动态对比增强的MRI数据转换为血管透性图,显著减少计算时间. 这种方法对预测乳腺癌对新辅助化疗的反应有希望.
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:
- 动态对比增强磁共振成像 (DCE-MRI) 对于评估血管透性至关重要.
- 药物动力学建模,就像扩展的托夫特模型 (ETM) 一样,提供了定量透性图 (Ktrans),但在计算上是密集的.
- 准确高效的基因转移映射对于癌症诊断和治疗反应评估至关重要.
研究的目的:
- 开发和评估一个图像对图像条件生成对抗网络 (cGAN),用于将DCE-MRI数据转化为血管药物动力学透性图.
- 将cGAN衍生Ktrans图的计算效率和准确性与已建立的ETM进行比较.
- 评估cGAN方法在接受新辅助化疗的乳腺癌患者预测治疗反应方面的潜力.
主要方法:
- 乳腺癌DCE-MRI扫描的回顾性队列被用于训练和验证cGAN.
- 扩展的Tofts模型 (ETM) 用于生成参考标准Ktrans地图.
- 使用线性回归,逻辑回归和对 t 测试来评估一致性,空间相似性和预测能力.
主要成果:
- 与ETM相比,cGAN实现了超过1000倍的计算时间缩短.
- cGAN Ktrans地图显示出优异的空间一致性和与ETM地图的高度结构相似性 (R2 ≥ 0.98).
- 在新辅助化疗后,cGAN Ktrans的百分比变化有效地区分了病理完整反应 (60%的减少) 和没有 (17%的减少) 的患者.
结论:
- 开发的DCE-to-pharmacokinetic cGAN为DCE-MRI中药物动力学分析提供了一种标准化和计算效率高的方法.
- 这种人工智能驱动的方法显示了早期预测乳腺癌对新辅助化疗反应的巨大潜力.
- 在临床瘤学中,cGAN促进了更快,更容易获得的定量成像生物标记分析.
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