首个乳腺断层成像基础模型，提升密度与风险预测能力
DBT-DINO: Towards Foundation model based analysis of Digital Breast Tomosynthesis
- 基于2500万切片自监督预训练，构建首个DBT基础模型
- 密度分类准确率79%，风险预测AUROC达78%，优于通用模型
- 适合乳腺癌筛查研究者，尤其关注影像分析与预训练方法
基础模型在医学影像中展现潜力，但三维成像模态仍缺乏深入探索。尽管数字乳腺断层成像（DBT）广泛用于乳腺癌筛查，目前尚无针对该模态的基础模型。本文提出并评估了首个面向DBT的基础模型DBT-DINO，采用DINOv2方法对来自27,990名患者的487,975例DBT体积数据中的超过2500万张2D切片进行自监督预训练。评估了三个下游任务：（1）使用5,000例筛查影像进行乳腺密度分类；（2）使用106,417例筛查影像进行5年乳腺癌风险预测；（3）使用393例标注体积进行病灶检测。在密度分类任务中，DBT-DINO准确率达0.79（95% CI: 0.76–0.81），显著优于MetaAI DINOv2基线（0.73，p<.001）和DenseNet-121（0.74，p<.001）。在5年风险预测中，DBT-DINO AUROC为0.78（95% CI: 0.76–0.80），略高于DINOv2的0.76（p=.57）。在病灶检测中，DINOv2平均敏感度更高（0.67，p=.60），但针对癌变病灶，DBT-DINO检测率为78.8%，优于DINOv2的77.3%。结果表明，基于大规模数据构建的DBT-DINO在密度分类和风险预测上表现优异，但特定检测任务仍需改进。
原文摘要 · Abstract (English)
Foundation models have shown promise in medical imaging but remain underexplored for three-dimensional imaging modalities. No foundation model currently exists for Digital Breast Tomosynthesis (DBT), despite its use for breast cancer screening. To develop and evaluate a foundation model for DBT (DBT-DINO) across multiple clinical tasks and assess the impact of domain-specific pre-training. Self-supervised pre-training was performed using the DINOv2 methodology on over 25 million 2D slices from 487,975 DBT volumes from 27,990 patients. Three downstream tasks were evaluated: (1) breast density classification using 5,000 screening exams; (2) 5-year risk of developing breast cancer using 106,417 screening exams; and (3) lesion detection using 393 annotated volumes. For breast density classification, DBT-DINO achieved an accuracy of 0.79 (95\% CI: 0.76--0.81), outperforming both the MetaAI DINOv2 baseline (0.73, 95\% CI: 0.70--0.76, p<.001) and DenseNet-121 (0.74, 95\% CI: 0.71--0.76, p<.001). For 5-year breast cancer risk prediction, DBT-DINO achieved an AUROC of 0.78 (95\% CI: 0.76--0.80) compared to DINOv2's 0.76 (95\% CI: 0.74--0.78, p=.57). For lesion detection, DINOv2 achieved a higher average sensitivity of 0.67 (95\% CI: 0.60--0.74) compared to DBT-DINO with 0.62 (95\% CI: 0.53--0.71, p=.60). DBT-DINO demonstrated better performance on cancerous lesions specifically with a detection rate of 78.8\% compared to Dinov2's 77.3\%. Using a dataset of unprecedented size, we developed DBT-DINO, the first foundation model for DBT. DBT-DINO demonstrated strong performance on breast density classification and cancer risk prediction. However, domain-specific pre-training showed variable benefits on the detection task, with ImageNet baseline outperforming DBT-DINO on general lesion detection, indicating that localized detection tasks require further methodological development.
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