在非大规模乳腺癌中,通过放射学驱动的病理完整反应预测,使用后新辅助化疗术术前动态对比增强磁共振成像
Oleksandr Moroz1, Zhiqiang Liu2, Cheng Liu3
1Department of Thyroid and Breast Surgery, Tongji Hospital, Tongji Medical College of Huazhong University of Science and Technology, Wuhan, China.
Diagnostic and interventional radiology (Ankara, Turkey)
|March 9, 2026
概括
使用化疗后MRI的放射学模型有效预测非质量损伤乳腺癌的病理完整反应 (pCR). 这种非侵入性方法通过评估化疗反应,有助于个性化治疗规划.
科学领域:
- 在瘤学瘤学.
- 放射学 放射学是一门学科.
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 准确预测新辅助化疗 (NAC) 反应对于优化乳腺癌治疗至关重要.
- 与不同质量相比,非质量病变 (NMLs) 在评估治疗反应方面存在独特的挑战.
- 动态对比增强磁共振成像 (DCE-MRI) 是评估乳腺癌的一个关键模式.
研究的目的:
- 评估从NAC术前DCE-MRI得出的放射学模型的临床实用性,用于预测NML乳腺癌中的pCR.
- 为了比较仅放射学,放射学-临床和三重整合模型与放射学家评估的性能.
- 确定影响NAC响应预测的关键放射性特征.
主要方法:
- 对121名患有NML的乳腺癌患者进行了回顾性分析,这些患者在NAC之前和之后接受了DCE-MRI.
- 从NAC后的DCE-MRI中提取的放射性特征,在空间注册后使用深度多式调节研究平台.
- 使用后勤回归并通过AUC,校准曲线和决策曲线分析评估的预测模型 (仅放射学,放射学-临床,三重集成) 的开发.
主要成果:
- 仅放射学模型显示出强大的区分性能 (验证AUC:0.867),表现优于临床模型和放射学家评估.
- 结合临床变量提高了准确性 (验证AUC:0.870),而三重整合模型没有显著差异 (验证AUC:0.810).
- 莎普利添加剂扩展 (SHAP) 确定放射性特征是最重要的预测因素,其次是HER2和激素受体状态.
结论:
- 在NAC手术前后,基于DCE-MRI的放射学提供了一种非侵入性方法,用于预测NML乳腺癌中的pCR.
- 结合放射学和临床模型显示,对于个性化NAC反应评估,其性能优越.
- 放射性特征提供了一个客观的,定量方法来表征化疗改变的组织,支持个性化治疗规划.
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