标题： 深度学习在临床乳腺超声图像中预测乳腺密度 显示英文标题

标题： Deep Learning Predicts Mammographic Breast Density in Clinical Breast Ultrasound Images

摘要： 背景：乳腺密度来源于乳腺X线影像，并由美国放射学院的乳腺影像报告和数据系统（BI-RADS）定义，是乳腺癌最强的风险因素之一。乳腺超声（BUS）是一种替代的乳腺癌筛查方式，在低资源、农村背景下尤其适用于早期检测。 本研究的目的在于探索一种人工智能（AI）模型，以从临床和手持式BUS成像中预测BI-RADS乳腺X线乳腺密度类别。 方法：所有数据均来自夏威夷和太平洋岛屿乳腺摄影登记处。 我们比较了来自BUS成像的深度学习方法以及仅基于图像统计的机器学习模型。 然后，在调整年龄的情况下，将AI推导出的BUS密度作为乳腺癌风险因素与临床BI-RADS乳腺密度进行了比较。 BUS数据按个体分为70%/20%/10%的训练、验证和测试组。 结果：从14,066名女性中选择了405,120张临床BUS图像用于本研究，其中9,846名女性用于训练（302,574张图像），2,813名用于验证（11,223张图像），1,406名用于测试（4,042张图像）。 在保留的测试集上，最强的AI模型通过BUS成像预测BI-RADS乳腺X线乳腺密度的AUROC达到0.854，优于基于图像统计的所有浅层机器学习方法。 在癌症风险预测方面，调整年龄后的AI BUS乳腺密度预测5年乳腺癌风险的AUROC为0.633，而调整年龄后的临床乳腺密度的AUROC为0.637。 结论：使用深度学习模型可以从BUS成像中高度准确地估计BI-RADS乳腺X线乳腺密度。 此外，我们证明了AI推导的BUS乳腺密度可以预测我们人群中的5年乳腺癌风险。 显示英文摘要

摘要： Background: Breast density, as derived from mammographic images and defined by the American College of Radiology's Breast Imaging Reporting and Data System (BI-RADS), is one of the strongest risk factors for breast cancer. Breast ultrasound (BUS) is an alternative breast cancer screening modality, particularly useful for early detection in low-resource, rural contexts. The purpose of this study was to explore an artificial intelligence (AI) model to predict BI-RADS mammographic breast density category from clinical, handheld BUS imaging. Methods: All data are sourced from the Hawaii and Pacific Islands Mammography Registry. We compared deep learning methods from BUS imaging, as well as machine learning models from image statistics alone. The use of AI-derived BUS density as a risk factor for breast cancer was then compared to clinical BI-RADS breast density while adjusting for age. The BUS data were split by individual into 70/20/10% groups for training, validation, and testing. Results: 405,120 clinical BUS images from 14.066 women were selected for inclusion in this study, resulting in 9.846 women for training (302,574 images), 2,813 for validation (11,223 images), and 1,406 for testing (4,042 images). On the held-out testing set, the strongest AI model achieves AUROC 0.854 predicting BI-RADS mammographic breast density from BUS imaging and outperforms all shallow machine learning methods based on image statistics. In cancer risk prediction, age-adjusted AI BUS breast density predicted 5-year breast cancer risk with 0.633 AUROC, as compared to 0.637 AUROC from age-adjusted clinical breast density. Conclusions: BI-RADS mammographic breast density can be estimated from BUS imaging with high accuracy using a deep learning model. Furthermore, we demonstrate that AI-derived BUS breast density is predictive of 5-year breast cancer risk in our population.