AI 中文总结
本研究验证基础模型嵌入可无任务适应地编码筛查乳腺X线摄影中的诊断前变化，且该能力源于临床相关预训练，而非通用生物医学预训练。
AI 中文摘要
筛查性乳腺X线摄影的基础模型嵌入可能编码诊断前的组织变化，而无需任务特定的适应。我们测试了在后来因癌症接受活检的女性中，嵌入是否沿着数据派生的“癌症方向”比匹配的筛查阴性对照组移动得更快，以及这是否依赖于预训练领域。我们研究了1,773名接受活检的女性（785例恶性，988例活检阴性）和1,773名匹配的对照组，每位受试者在索引检查前至少进行过两次年度筛查检查。对四个2D模型应用了相同的流程：Mammo-CLIP（MC，分布外乳腺X线摄影）、HOPPR（分布内乳腺X线摄影）、MedImageInsight（MII，通用医学影像）和BiomedCLIP（基于文献图表的生物医学视觉-语言预训练）。乳腺水平的嵌入量化了沿癌症方向的纵向运动。我们使用患者间设计并辅以混合效应分析比较病例和对照组，并在患者内部比较活检侧与健康对侧乳腺。在MII嵌入空间的匹配模态下，恶性病例在索引检查前的两个筛查间隔内比对照组漂移显著更快；活检阴性病例仅在第一个间隔内表现出显著性。MC差异在两种活检组的第一个间隔内均显著。患者内比较显示大致相似的模式，MC的显著性在两组中扩展到第二个间隔，而HOPPR在间隔1处显示显著性。BiomedCLIP在任一设计或活检组中均未显示显著差异。总体而言，方向性嵌入速度作为临床基础而非一般生物医学预训练的一个属性出现，表明基础模型嵌入可以在没有任务特定适应的情况下编码诊断前的乳腺X线摄影变化。
英文摘要
Foundation model embeddings of screening mammograms may encode pre-diagnostic tissue change without task-specific adaptation. We tested whether embeddings move faster along a data-derived "cancer direction" in women later biopsied for cancer than in matched screen-negative controls, and whether this depends on pretraining domain. We studied 1,773 biopsied women (785 malignant, 988 biopsy-negative) and 1,773 matched controls, each with at least two annual screening exams before their index exam. An identical pipeline was applied to four 2D models: Mammo-CLIP (MC, out-of-distribution mammography), HOPPR (in-distribution mammography), MedImageInsight (MII, general medical imaging), and BiomedCLIP (biomedical vision-language pretraining on literature figures). Breast-level embeddings quantified longitudinal movement along the cancer direction. We compared cases and controls using a between-patient design with complementary mixed-effects analysis, and biopsied versus healthy contralateral breasts within patients. Under matched modality in MII embedding space, malignant cases drifted significantly faster than controls in the first two screening intervals preceding the index exam; biopsy-negative cases showed significance only in the first. MC differences were significant in the first interval for both biopsy groups. Within-patient comparisons showed a broadly similar pattern, with MC significance extending to the second interval in both groups and HOPPR showing significance at interval 1. BiomedCLIP showed no significant differences in either design or biopsy group. Overall, directional embedding velocity emerges as a property of clinically grounded rather than general biomedical pretraining, showing that foundation model embeddings can encode pre-diagnostic mammographic change without task-specific adaptation.
Comments13 pages, 5 figures, supplementary info attached
