一个可解释的纵向多模式融合模型,用于预测乳腺癌妇女的新辅助疗法反应
Yuan Gao1,2,3, Sofia Ventura-Diaz4, Xin Wang1,2,3
1GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre, P. Debyelaan 25, 6202 AZ, Maastricht, The Netherlands.
Nature communications
|November 7, 2024
概括
这项研究引入了一个深度学习系统 (MRP) 用于监测乳腺癌中使用多模式成像的新辅助治疗反应. MRP模仿医生的评估,在预测治疗结果方面表现优于人类,提高了临床适用性.
科学领域:
- 人工智能的人工智能
- 医疗成像医学成像
- 在瘤学瘤学.
背景情况:
- 深度学习 (DL) 对于使用多模式成像监测新辅助治疗 (NAT) 反应至关重要.
- 目前的方法往往缺乏现实世界的临床适用性,特别是在长度NAT研究中缺少数据.
研究的目的:
- 开发一种多模态响应预测 (MRP) 系统,模仿医生对乳腺癌中NAT响应的评估.
- 通过整合跨模式知识挖掘和时间信息嵌入来处理缺失的数据和不同的NAT设置来提高临床可行性.
主要方法:
- 提出了MRP系统,包括跨模式知识挖掘和时间信息嵌入.
- 通过多中心和多国读者研究验证了MRP.
- 评估了MRP对治疗和手术临床决策的影响.
主要成果:
- 对于人类放射科医生来说,MRP的强度与人类放射科医生的强度相当.
- 在NAT之前,MRP在预测病理完整反应方面表现优于人类 (ΔAUROC在内部和外部数据集上分别为14%和10%).
- 在指导治疗决策和手术规划方面,MRP显示出潜在的临床实用性.
结论:
- 在乳腺癌中,MRP系统提供了一种可行的和强大的方法来监测多模式NAT反应.
- MRP有可能对NAT试验和手术干预决策的患者分层产生重大影响.
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