Mammo-LIFE：用于放疗后结局预测的纵向乳腺X线影像与临床特征增强

中文摘要 AI 辅助

近年来，基于人工智能（AI）的计算机辅助诊断（CAD）系统在乳腺癌筛查、诊断和预后方面取得了显著进展。相比之下，利用配对的纵向乳腺X线影像进行放疗后结局预测的研究受到的关注要少得多，这主要是由于高质量标注的纵向数据集有限。纵向乳腺X线影像，结合配对的治疗前后信息，为表征放疗后治疗引起的乳腺组织变化提供了独特的机会。由此学习到的表征可作为推进个性化放疗计划制定和术后管理的宝贵资产。在此背景下，我们提出了Mammo-LIFE，一个用于放疗后结局预测的患者级多模态框架，该框架将纵向乳腺X线影像特征与患者级临床变量相结合。影像分支使用通过低秩适应（LoRA）适配的乳腺X线影像专用编码器，处理从四个标准视图采集的配对治疗前后乳腺X线影像。在每个视图内，通过纵向比较模块显式比较治疗前后的表征，以捕获治疗相关的变化。随后，使用学习的视图注意力池化聚合得到的视图级嵌入，形成统一的患者级乳腺X线影像表征。选定的临床变量随后通过晚期融合策略与基于影像的预测概率相结合。为了评估配对纵向乳腺X线影像与临床信息相结合的有效性，我们在一个内部临床队列上使用患者级分层五折交叉验证进行了实验。

英文摘要

Recent advances in Artificial Intelligence (AI)-powered Computer-Aided Diagnosis (CAD) systems have substantially improved breast cancer screening, diagnosis, and prognosis. Comparatively, postradiotherapy outcome prediction using paired longitudinal mammograms has received considerably less attention. This is largely due to the limited availability of well-annotated longitudinal datasets. Longitudinal mammograms, coupled with paired pre- and post-treatment information, provide a unique opportunity to characterize treatment-induced breast tissue changes following radiotherapy. The resulting learned representations can serve as a valuable asset for advancing personalized radiotherapy planning and post-treatment management. In this context, we propose Mammo-LIFE, a patient-level multimodal framework for post-radiotherapy outcome prediction that combines longitudinal mammographic features with patient-level clinical variables. The imaging branch processes paired pre- and post-treatment mammograms acquired from the four standard views using a mammography-specific encoder adapted via Low-Rank Adaptation (LoRA). Within each view, preand post-treatment representations are explicitly compared through a longitudinal comparison module to capture treatment-related changes. The resulting view-level embeddings are then aggregated using learned view-attention pooling to form a unified patient-level mammographic representation. Selected clinical variables are subsequently combined with the image-derived prediction probability through a late-fusion strategy. To evaluate the effectiveness of combining paired longitudinal mammograms with clinical information, experiments were conducted on an in-house clinical cohort using patient-level stratified five-fold cross-validation.

发表机构

• Concordia University（康考迪亚大学）
• Atrium Health Levine Cancer Institute（Atrium Health 莱文癌症研究所）

机构由 AI 辅助整理，请以论文原文为准。