用深度学习从乳腺影像预测五年内癌变风险，准确率超80%。
Towards Early Detection: AI-Based Five-Year Forecasting of Breast Cancer Risk Using Digital Breast Tomosynthesis Imaging
- 基于DINOv2提取乳腺影像特征，结合累积风险层建模。
- 在16万例数据上测试，五年预测AUROC达0.80。
- 可辅助传统评估，适合临床早期筛查研究者使用。
由于早期发现乳腺癌能显著提升治疗效果，优化筛查流程具有重大商业价值。然而，现有风险预测模型性能有限，且未纳入2011年FDA批准用于筛查的数字乳腺断层成像（DBT）。为解决这一问题，我们提出一种基于深度学习的框架，直接从筛查用的DBT影像预测个体五年内的乳腺癌风险。利用包含161,753例DBT检查、来自50,590名患者的超大规模数据集，我们采用Meta AI的DINOv2图像编码器提取特征，并结合累积风险层，评估患者未来五年的患病概率。在独立测试集上，最优模型的AUROC达到0.80。结果表明，基于DBT的深度学习方法具备显著潜力，可补充传统风险评估工具，为后续验证与优化提供有力基础。
原文摘要 · Abstract (English)
As early detection of breast cancer strongly favors successful therapeutic outcomes, there is major commercial interest in optimizing breast cancer screening. However, current risk prediction models achieve modest performance and do not incorporate digital breast tomosynthesis (DBT) imaging, which was FDA-approved for breast cancer screening in 2011. To address this unmet need, we present a deep learning (DL)-based framework capable of forecasting an individual patient's 5-year breast cancer risk directly from screening DBT. Using an unparalleled dataset of 161,753 DBT examinations from 50,590 patients, we trained a risk predictor based on features extracted using the Meta AI DINOv2 image encoder, combined with a cumulative hazard layer, to assess a patient's likelihood of developing breast cancer over five years. On a held-out test set, our best-performing model achieved an AUROC of 0.80 on predictions within 5 years. These findings reveal the high potential of DBT-based DL approaches to complement traditional risk assessment tools, and serve as a promising basis for additional investigation to validate and enhance our work.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。