Bunnell Arianna, Valdez Dustin, Wolfgruber Thomas K, Quon Brandon, Hung Kailee, Hernandez Brenda Y, Seto Todd B, Killeen Jeffrey, Miyoshi Marshall, Sadowski Peter, Shepherd John A
Department of Information and Computer Sciences, University of Hawai'i at Mānoa, POST Bldg, 1860 East-West Rd, Honolulu, HI, 96822, USA.
University of Hawai'i Cancer Center, 701 Ilalo St, Honolulu, HI, 96813, USA.
Lancet Reg Health Am. 2025 Apr 18;46:101096. doi: 10.1016/j.lana.2025.101096. eCollection 2025 Jun.
Breast density, as derived from mammographic images and defined by the Breast Imaging Reporting & Data System (BI-RADS), is one of the strongest risk factors for breast cancer. Breast ultrasound is an alternative breast cancer screening modality, particularly useful in low-resource, rural contexts. To date, breast ultrasound has not been used to inform risk models that need breast density. The purpose of this study is to explore the use of artificial intelligence (AI) to predict BI-RADS breast density category from clinical breast ultrasound imaging.
We compared deep learning methods for predicting breast density directly from breast ultrasound imaging, as well as machine learning models from breast ultrasound image gray-level histograms alone. The use of AI-derived breast ultrasound breast density as a breast cancer risk factor was compared to clinical BI-RADS breast density. Retrospective (2009-2022) breast ultrasound data were split by individual into 70/20/10% groups for training, validation, and held-out testing for reporting results.
405,120 clinical breast ultrasound images from 14,066 women (mean age 53 years, range 18-99 years) with clinical breast ultrasound exams were retrospectively selected for inclusion from three institutions: 10,393 training (302,574 images), 2593 validation (69,842), and 1074 testing (28,616). The AI model achieves AUROC 0.854 in breast density classification and statistically significantly outperforms all image statistic-based methods. In an existing clinical 5-year breast cancer risk model, breast ultrasound AI and clinical breast density predict 5-year breast cancer risk with 0.606 and 0.599 AUROC (DeLong's test p-value: 0.67), respectively.
BI-RADS breast density can be estimated from breast ultrasound imaging with high accuracy. The AI model provided superior estimates to other machine learning approaches. Furthermore, we demonstrate that age-adjusted, AI-derived breast ultrasound breast density provides similar predictive power to mammographic breast density in our population. Estimated breast density from ultrasound may be useful in performing breast cancer risk assessment in areas where mammography may not be available.
National Cancer Institute.
乳腺密度源自乳腺钼靶图像，由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。乳腺超声是另一种乳腺癌筛查方式，在资源匮乏的农村地区尤为有用。迄今为止，乳腺超声尚未用于需要乳腺密度的风险模型。本研究的目的是探索使用人工智能（AI）从临床乳腺超声成像预测BI-RADS乳腺密度类别。
我们比较了直接从乳腺超声成像预测乳腺密度的深度学习方法，以及仅基于乳腺超声图像灰度直方图的机器学习模型。将AI衍生的乳腺超声乳腺密度作为乳腺癌风险因素的用途与临床BI-RADS乳腺密度进行了比较。回顾性（2009 - 2022年）乳腺超声数据按个体分为70/20/10%的组用于训练、验证和留存测试以报告结果。
从三个机构回顾性选取了14066名女性（平均年龄53岁，范围18 - 99岁）进行临床乳腺超声检查的405120张临床乳腺超声图像纳入研究：10393例用于训练（302574张图像），2593例用于验证（69842张），1074例用于测试（28616张）。AI模型在乳腺密度分类中实现了0.854的曲线下面积（AUROC），在统计学上显著优于所有基于图像统计的方法。在现有的临床5年乳腺癌风险模型中，乳腺超声AI和临床乳腺密度预测5年乳腺癌风险的AUROC分别为0.606和0.599（德龙检验p值：0.67）。
可以从乳腺超声成像中高精度估计BI-RADS乳腺密度。AI模型提供了优于其他机器学习方法的估计。此外，我们证明在我们的人群中，年龄调整后的AI衍生乳腺超声乳腺密度与乳腺钼靶乳腺密度具有相似的预测能力。在无法进行乳腺钼靶检查的地区，超声估计的乳腺密度可能有助于进行乳腺癌风险评估。
美国国立癌症研究所
